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PMC

ACS Omega

PMC10720292

11-29-2023

10.1021/acsomega.3c07461

Study on Reaction Mechanism and Process Safety for Epoxidation

Cheng Chunsheng, Wei Zhenyun, Ming Xu, Hu Jie, Kong Rong

The reaction mechanism and process safety for epoxidation were investigated in this study. 1-(2-Chlorophenyl)-2-(4-fluorophenyl)-3-(1,2,4-triazole) propene (triazolene), a typical representative of high steric olefinic compounds, was chosen as the raw material. In addition, hydrogen peroxide was chosen as the oxygen source in the reaction. Online Raman spectroscopy combined with high-performance liquid chromatography (HPLC) was used for the process monitoring analysis. The results of this study indicated that the epoxidation process is exothermic, and the apparent reaction heat was 1340.0 kJ·kg–1 (measured by the mass of triazolene). The heat conversion rate was 39.7% immediately after hydrogen peroxide dosing to a triazolene and maleic anhydride mixture solution in chloroform. This result indicated that a considerable amount of heat is accumulated during the epoxidation reaction, which leads to a potential high safety concern. The study of the reaction mechanism showed that maleic anhydride reacts with hydrogen peroxide quickly to form maleic acid peroxide, which is controlled by hydrogen peroxide feeding, and the formed maleic acid peroxide further reacts with triazolenes slowly, which is a kinetically controlled reaction. Decomposition kinetics studies revealed that the temperatures corresponding to the time of maximum reaction rate for 8 and 24 h are TD24 = 89.9 °C and TD8 = 104.1 °C, respectively.

1IntroductionEpoxidation of alkenes is a very important oxidation reaction,1 which aims to form an epoxide compound by adding one atom of oxygen between the carbon atoms at both ends of the double bond of alkenes.2 The Epoxide compound is one of the most valuable compounds in the pharmaceutical and spice industries.3−5 In addition, it has an active ternary epoxy structure, which is prone to ring-opening reactions under various conditions, resulting in high-value-added chemical products and intermediates.6,7 At present, the traditional epoxidation methods are mostly used in industry, the halogen alcohol method and peroxide acid method are mainly used to prepare epoxide compounds.8 The halogen alcohol method9 was widely used in the early industrial preparation of Epoxide, but its synthesis process was complicated, and the separation and treatment of byproducts were difficult and then caused serious environmental pollution. Now, this method has been phased out in industrial production. The peroxide acid method10 is a relatively simple epoxidation method of olefins. Based on the different epoxidation methods, it can be classified into direct oxidation and indirect oxidation.11 In direct oxidation, the olefins react directly with organic peroxy acids, such as performic acid, peracetic acid, and peroxy benzoic acid. In indirect oxidation, organic peroxy acid is generated during the process and immediately participates in epoxidation with olefins. Because of the high price of organic peroxy acid and the existence of instability, it is easy to decompose, inconvenient to store, and involves other security risks. The indirect oxidation method is thus typically used for epoxidation in industry.A high concentration of hydrogen peroxide was commonly used as the oxidant in indirect epoxidation, and hydrogen peroxide is also unstable, heat, light, heavy metals, and other impurities will result in its decomposition, oxygen gas, and heat released.12−14 Under the influence of reactant activity, hydrogen peroxide and peroxy acid accumulate at different degrees during the reaction and decompose rapidly when heated.15 In addition, the epoxidation reaction itself is highly exothermic,16,17 once the temperature is out of control, extremely easy to cause reaction accidents.18−21 Therefore, the study on the safety of the indirect epoxidation process is helpful to avoid serious safety accidents in industrial processes and has important practical significance.In this paper, the synthesis of epoxiconazole was chosen as a typical indirect epoxidation of highly hindered substituted olefins. Epoxiconazole is a famous broad-spectrum fungicide commonly used in agricultural production22 and its effect inhibits synthesizing pathogenic ergosterol and hinders the pathogenic cell wall from forming.23 It is an effective preventative measure against leaf spots, mildew, and spotting for bananas, green onions, garlic, celery, kidney beans, melons, asparagus, peanuts, sugar beets, and other crops.24,25 The indirect epoxidation synthesis of fluconazole is carried out using triazolene as the raw material, using hydrogen peroxide and maleic anhydride to react to generate peroxy acid for epoxidation,26 as shown in Scheme 1.Scheme 1Chemical Reaction Showing the Formation of EpoxiconazoleThe safety of the indirect epoxidation process has not been reported. In view of the hazard of epoxidation, the process of synthesizing epoxiconazole was studied by online Raman combined with offline HPLC for the first time, clarifying the space-time rule and potential risk distribution of peroxide formation. Then the exothermic characteristics of the epoxidation process were studied by reaction calorimeter RC1, and the safety of the mixed system in different reaction stages was tested by TSU. Finally, the decomposition kinetics of the mixed system after the reaction was studied, and the relevant kinetic parameters were obtained. The change of reaction rate and conversion with time was simulated under adiabatic conditions. It provides the technical basis for final industrialization safety control.2Experiment2.1ReagentsHydrogen peroxide (50%), maleic anhydride (≥99%), sodium bisulfate (≥99%), and chloroform (≥99%) were all purchased from China Pharmaceutical Group Co., Ltd. 1-(2-Chlorophenyl)-2-(4-fluorophenyl)-3-(1,2,4-triazole) propene (95%) was purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. All reagents were used without further purification.2.2High-Performance Liquid ChromatographyHigh-performance liquid chromatography (Waters1525); Agilent SB-C18 (150 mm × 4.6 mm (i.d),5 μm). Mobile phase: acetonitrile/methanol/water (volume ratio: 40:20:40); column temperature: 30 °C; flow rate: 1.0 mL/min; detection wavelength: 230 nm.2.3Automatic Reaction CalorimeterA reaction calorimeter (RC1e; Mettler Toledo, Zurich, Switzerland) was used to assess the exothermic conditions during the process. IControl software is used to analyze the test data processing, and can get the heat flow (Q), thermal conversion rate (X), heat transfer coefficient (U), adiabatic temperature rise (ΔTad), specific heat (Cp), and other thermodynamic information.2.4Thermal Screening UnitA thermal Screening Unit (TSU; HEL, United Kingdom) was used to study the thermal decomposition characteristics of the reactants. The test ball is constructed using Hastelloy with a volume of 10 mL. The temperature range was from 30 to 300 °C. The operating pressure range is 0–100 bar, the heating rate is 5 K/min, and the loading volume is 1–3 g.2.5Online Raman SpectroscopyAn Online Raman spectrometer (ReactRaman 785; Mettler Toledo) was used to monitor the reaction process. By comparing the disappearance rate of the significant absorption peak of the reactant and the formation rate of the significant absorption peak of the product, the existence of active intermediates in the reaction process is judged.27,28 The collection time of a single spectrogram is set as 15 s and the Raman exposure time as 1 s.2.6Differential Scanning CalorimetryA differential scanning calorimetry (HP DSC1, Mettler Toledo) was used for thermal safety study of a milligram sample to obtain the thermal decomposition status of the sample to be tested. The samples were contained in a high-pressure gold-plated crucible with a volume of 30 μL. The sample mass was 3.0 ± 0.2 mg and the temperature range was 30–350 °C. The heating rates selected in these experiments were 3, 5, and 8 K/min.2.7Synthesis MethodTriazolene chloroform solution (wt 20%) (134 g, 0.0855 mol, 1.0 equiv) and maleic anhydride (83.8 g, 0.8551 mol, 10.0 equiv) were added to a 500 mL reactor, and the mixture was cooled to 20 ± 1 °C after full dissolution. Sodium bisulfate (0.2 g, 0.0020 mol, 0.02 equiv) was added, and wt 50% hydrogen peroxide (35.0 g, 0.5130 mol, 6.0 equiv) was dropwise added in 2.5 h, and the temperature was kept at 20 ± 1 °C for 16–20 h after dropwise addition finished. Sampling and analysis to triazolene were less than 10% as the reaction end point. The NMR characterization29 of the product is shown in Figures S1 and S2.3Results and Discussion3.1Reaction MechanismThe C=C double bond changes from a one-dimensional linear moiety to a C–O–C two-dimensional plane in epoxidation. First, the acid anhydride reacts with hydrogen peroxide to form peroxy acid, and then, the peroxy acid reacts with alkene.30 The epoxide is obtained through a transition state. The reaction mechanism is shown in Scheme 2, which was further verified by online Raman spectroscopy combined with HPLC.Scheme 2Epoxidation Reaction Mechanism3.1.1Characteristic Peak IdentificationOnline Raman spectroscopy was used for analyzing chloroform, maleic anhydride, and hydrogen peroxide. The results of the Raman spectral analysis of each starting material are shown in Figure 1.Figure 1Raman spectra of the main raw materials.Spectral analysis revealed that the primary characteristic absorption peaks are at 260 cm–1 (Cl–C–Cl degeneracy bend), 366 cm–1 (Cl–C–Cl symmetrical bend), and 667 cm–1 (C–Cl symmetrical stretch) for chloroform;31,32 635 cm–1 (ring deformation), 869 cm–1 (C–C stretch), 1065 cm–1 (C–O stretch), 1592 cm–1 (C=C stretch), and 1849 cm–1 (C=O stretch) for maleic anhydride;33 and 881 cm–1 (O–O stretch) for hydrogen peroxide.343.1.2Process MonitoringCombined online Raman spectroscopy and HPLC analysis were adopted to monitor the reaction process in this study. The peaks at 1849, 881, and 1149 cm–1 were selected as the characteristic absorption peaks of maleic anhydride, hydrogen peroxide, and maleic acid peroxide,35 respectively. The concentration changes of the triazolene starting material and epoxiconazole during the reaction were analyzed using HPLC. The relative concentration changes of each component in the reaction are shown in Figure 2.Figure 2Reaction trend of each component. (Hydrogen peroxide is added in the dashed interval.)The results showed that the characteristic absorption peak of hydrogen peroxide at 881 cm–1 increased rapidly with hydrogen peroxide feeding, which is marked with a blue line (Figure 2). Conversely, the characteristic absorption peak of maleic anhydride at 1849 cm–1 showed a rapid decrease (black line). Furthermore, the characteristic absorption peak of maleic acid peroxide at 1149 cm–1 increased rapidly (red line). In the hydrogen peroxide feeding stage, maleic anhydride promptly reacts with hydrogen peroxide to generate maleic acid peroxide, which is a feeding control reaction. HPLC analysis showed that the conversion rate of triazolene was low, as indicated by the green line, and epoxiconazole was produced less, as indicated by the pink line in the hydrogen peroxide feeding stage. After the completion of hydrogen peroxide feeding, the conversion rate of triazolene and the generation of epoxiconazole gradually increased. Some solid epoxiconazoles were generated in the reaction system. The conversion of triazolene to epoxiconazole was related to the concentration of maleic acid peroxide in the system, and the reaction was kinetically controlled.3.2Exothermic Properties of the Epoxidation ReactionThe exothermic characteristics of the epoxidation process are shown in Figures 3 and 4.Figure 3Reaction heat flow rate curve.Figure 4Reaction heat flow rate curve in the early stage.Triazolene and maleic anhydride were added to chloroform to form a suspension solution, and 50% hydrogen peroxide was added dropwise when the temperature was decreased to 20.0 °C ± 1 °C. When hydrogen peroxide was added, the solid in the suspension solution gradually dissolved, and a chemical reaction occurred. The reaction was initially endothermic, with a maximum endothermic rate of 13.5 W·kg–1. After 30 min of hydrogen peroxide feeding, the apparent reaction became exothermic, gradually reaching the maximum heat release rate of 15.7 W·kg–1. The solid material precipitated out of the reaction medium as the feeding progressed; the heat release rate gradually decreased, and the average heat release rate throughout the dripping of hydrogen peroxide was 6.2 W·kg–1. After hydrogen peroxide feeding, the reaction heat conversion rate was 39.7%; this indicates considerable heat accumulation during the reaction process. After 30 min, the reaction heat release rate suddenly increased with accelerated solid precipitation. After maintaining for 11 h, the system exhibited no notable thermal effect.The results of the current study reveal that the specific heat capacity for the reactant liquid is 3.13 kJ·kg–1·K–1, the epoxidation reaction process was overall exothermic, and the total apparent heat release was 268.0 kJ·kg–1 (based on the mass of the triazolene chloroform solution). The adiabatic temperature rise was 45.6 K, reaction heat accumulation was 60.3%, and the maximum temperature of the synthesis reaction (MTSR) was 47.5 °C when the cooling system failed.3.3Process Safety3.3.1Thermal Stability Study during the ReactionThe stability and safety of the epoxidation reaction system were studied in different stages by a thermal stability test. We collected four samples denoted as a, b, c, and d. Sample a is collected at the end of the hydrogen peroxide feeding; b is the reaction mixture kept warm for 3 h; c is the reaction mixture kept warm for 6 h; and d is the reaction mixture collected at the end of the reaction. The results are shown in Figure 5.Figure 5Time–temperature–pressure curve for the TSU calorimetric test. (a) End of the hydrogen peroxide feeding; (b) reaction mixture kept warm for 3 h; (c) reaction mixture kept warm for 6 h; and (d) reaction mixture collected at the end of the reaction.The results show that the samples at each stage of the reaction are thermally unstable, and the temperature and pressure showed a sharp rise with more heat release at 35–50 °C for each sample. With the extension of the holding time, the maximum temperature and pressure rise rate gradually decreased (Figure 6). At the end of hydrogen peroxide feeding, the maximum temperature rise rate of sample a was 31 times more, and maximum pressure rise rate was 41 times more than that of sample d. A large amount of the maleic acid peroxide intermediate was generated in the reaction system during hydrogen peroxide feeding, resulting in the highest risk of decomposition. Therefore, risk control measures such as emergency quenching and overpressure relief must be established to avoid explosions in upscale tests and industrial applications of the epoxidation reaction.Figure 6Relationship between temperature and pressure rise rate in each stage of epoxidation.3.3.2Kinetics of Thermal DecompositionIn this study, considering the influence of the two important factors, conversion rate α and temperature T,36 and that these two parameters are independent of one another, the reaction rate equation can be expressed aswhere α is the conversion rate, t is the reaction time, and f(α) is the reaction mechanism function.The rate constant k is closely related to temperature T. Applying the Arrhenius equation, the following equation is obtained:where T is the temperature in Kelvin, t is the time in seconds, E is activation energy kJ·mol–1, A(α) is the pre-exponential factor with the unit s–1, and R is the universal gas constant with the unit kJ·mol–1·K–1.According to the reaction rate equation, the activation energy for the decomposition reaction is closely related to the reaction rate, conversion rate, and temperature. A differential scanning calorimeter was used to determine the variation trend of the solid–liquid self-decomposition reaction rate after the epoxidation reaction using different scanning rates, as shown in Figure 7.Figure 7Variation trend of the self-decomposition rate.The results indicate that the increased temperature rate was reduced by 2.7 times, the initial decomposition temperature was decreased by 19.3 °C, and the maximum self-decomposition rate was reduced by 2.5 times. Because of this, a higher rate of temperature increase indicates a higher initial detected decomposition temperature. Using Friedman’s equal conversion rate differential method,37 AKTS (Advanced Kinetics and Technology Solutions) software obtained the activation energy for the decomposition reaction. Figure 8 shows that the activation energy for the self-decomposition reaction from the sample was 44–104 kJ/mol. The fluctuation range was extensive, indicating that the decomposition process of the sample was more complex.38Figure 8Activation energy of the self-decomposition reaction of the feed liquid after epoxidation.3.3.3Decomposition Reaction SafetyUsing the thermokinetic results, the decomposition thermokinetics for the time taken to the maximum reaction rate under adiabatic conditions (TMRad)39 of the material solution after the epoxiconazole synthesis reaction were studied and analyzed. The results of the study are shown in Figure 9. Under adiabatic conditions, TD2 is the temperature at which the time to the maximum reaction rate for thermal decomposition is 2 h. Here, TD2 was 119.4 °C, while that at 4 h (TD4) was 110.2 °C, that at 8 h (TD8) was 100.8 °C, that at 24 h (TD24) was 87.2 °C, and that at 168 h (TD168) was 65.9. (the specific heat capacity for the sample was 3.13 kJ·kg–1·K–1, and the system Phi was 1.05).Figure 9TMRad curve of the self-decomposition reaction of epoxidation. (a) 0–14 h; (b) 0–210 h.TD8 and TD24 are the temperatures at which the time to the maximum reaction rate for material decomposition are 8 and 24 h under adiabatic conditions, which is critical for risk control in emergencies.40 Considering the relationship between time, temperature, and conversion rate for the decomposition reaction, the results for the decomposition mechanics study for TD8 and TD24 are shown in Figures 10 and 11, respectively.Figure 10Trend of self-decomposition reaction at TD8 = 100.8 °C.Figure 11Trend of self-decomposition reaction at TD24 = 87.2 °C.Under adiabatic conditions, when the sample was at 100.8 °C (TD8), the initial decomposition reaction rate, decomposition reaction conversion rate, and sample temperature slowly increased. At 4.4 h, the decomposition reaction conversion rate increased to 16.1% and the decomposition reaction rate significantly increased. The decomposition reaction rate reached its maximum at 8 h, and the decomposition reaction conversion rate increased to 83.8%. At 8.8 h, all of the materials were decomposed.Under adiabatic conditions, when the sample was at 87.2 °C (TD24), the initial decomposition reaction rate, decomposition reaction conversion rate, and sample temperature slowly increased. At 12 h, the decomposition reaction conversion rate increased to 13.9%, and the decomposition reaction rate significantly increased. The decomposition reaction rate reached its maximum at 24 h, and the decomposition reaction conversion rate increased to 88.5%. At 25.4 h, all of the materials were decomposed.4ConclusionsThe reaction mechanism and process safety for epoxidation of triazolenes and hydrogen peroxide as the oxygen source were studied herein, providing technical bases for their production, storage, and transportation. The results are summarized as follows:In the epoxidation of 1-(2-chlorophenyl)-2-(4-fluorophenyl)-3-(1,2,4-triazole) propene in the presence of maleic anhydride and with hydrogen peroxide as the oxygen source, maleic acid peroxide is produced first, and then, maleic acid peroxide reacts with triazolene to form the epoxiconazole. The former reaction is fast and is controlled by hydrogen peroxide feeding, while the latter is slow and is controlled by kinetics.The epoxidation process is complicated, involving solid maleic anhydride dissolution and epoxiconazole precipitation. The epoxidation process is exothermic, and the apparent reaction heat was 1340.0 kJ·kg–1, the adiabatic temperature rise was 45.60 K, and the maximum temperature of the synthesis reaction (MTSR) was 47.5 °C when the reaction runaway occurred.The reaction runaway occurred at 35–50 °C at different stages along the complete process with a significant release of heat and gas, thereby raising serious safety concerns. At the end of hydrogen peroxide feeding, the temperature and pressure increase rates of decomposition are at their maximum values. The decomposition kinetics study showed that the temperatures corresponding to the time of maximum reaction rate are 89.9 °C (TD24) and 104.1 °C (TD8). The maximum temperature of the epoxidation reaction process was 47.5 °C when the reaction runaway occurred, which exceeds the temperature at which the mixture decomposes violently at different stages of the reaction, thus posing a potential safety hazard.In the process of epoxidation reaction scale up and industrialization, control measures for emergency quenching and overpressure explosion relief should be established, and control measures should be started in a timely manner after the thermal runaway of the reaction system to avoid the violent decomposition of the materials leading to an accident.

PMC

Heliyon

PMC10241862

5-25-2023

10.1016/j.heliyon.2023.e16386

Antidiarrheal activities of methanolic crude extract and solvent fractions of the root of

Worku Solomon Ayenew, Tadesse Solomon Asmamaw, Abdelwuhab Mohammedbrhan, Asrie Assefa Belay

BackgroundIn Ethiopian traditional medicine, V. sinaiticum is one of the most often utilized medicinal herbs for the treatment of diarrhea. Therefore, this study was conducted to validate the use of the plant for the treatment of diarrhea in the traditional medical practice of Ethiopia.MethodsCastor oil-induced diarrhea, enteropooling, and intestinal motility test models in mice were used to evaluate the antidiarrheal properties of the 80% methanol crude extract and the solvent fractions of the root component of V. sinaiticum. The effects of the crude extract and the fractions on time for onset, frequency, weight, and water content of diarrheal feces, intestinal fluid accumulation, and intestinal transit of charcoal meal were evaluated and compared with the corresponding results in the negative control.ResultsThe crude extract (CE), aqueous fraction (AQF), and ethyl acetate fraction (EAF) at 400 mg/kg (p < 0.001) significantly delayed the onset of diarrhea. Besides, the CE and AQF at 200 and 400 mg/kg (p < 0.001) of the doses, and EAF at 200 (p < 0.01) and 400 mg/kg (p < 0.001) significantly decreased the frequency of diarrheal stools. Furthermore, CE, AQF, and EAF at their three serial doses (p < 0.001), significantly reduced the weights of the fresh diarrheal stools as compared to the negative control. The CE and AQF at 100 (p < 0.01), and 200 and 400 mg/kg (p < 0.001) of their doses and EAF at 200 (p < 0.01) and 400 mg/kg (p < 0.001) significantly decreased the fluid contents of diarrheal stools compared to the negative control. In the enteropooling test, the CE at 100 (p < 0.05), and 200 and 400 mg/kg (p < 0.001), AQF at 200 (P < 0.05) and 400 mg/kg (p < 0.01), and EAF at 200 (p < 0.01) and 400 mg/kg (p < 0.001) significantly decreased the weights of intestinal contents compared to the negative control. Additionally, the CE at 100 and 200 (p < 0.05) and 400 mg/kg (p < 0.001), AQF at 100 (p < 0.05), 200 (p < 0.01), and 400 mg/kg (p < 0.001) of the doses, and EAF at 400 mg/kg (p < 0.05), produced significant reductions in the volumes of intestinal contents. In the intestinal motility test model, the CE, AQF, and EAF at all their serial doses (p < 0.001), significantly suppressed the intestinal transit of charcoal meal and peristaltic index compared to the negative control.ConclusionOverall, the results of this study showed that the crude extract and the solvent fractions of the root parts of V. sinaiticum had considerable in vivo antidiarrheal activities. Besides, the crude extract, especially at 400 mg/kg, produced the highest effect followed by the aqueous fraction at the same dose. This might indicate that the bioactive compounds responsible for the effects are more of hydrophilic in nature. Moreover, the antidiarrheal index values were increased with the doses of the extract and the fractions, suggesting that the treatments might have dose-dependent antidiarrheal effects. Additionally, the extract was shown to be free of observable acute toxic effects. Thus, this study corroborates the use the root parts of V. sinaiticum to treat diarrhea in the traditional settings. Furthermore, the findings of this study are encouraging and may be used as the basis to conduct further studies in the area including chemical characterization and molecular based mechanism of actions of the plant for its confirmed antidiarrheal effects.

1IntroductionDiarrhea is defined as defecation of three or more loose or liquid stools in a day . It comes about as a result of an imbalance between the bowel's secretory and absorptive processes . Based on WHO criteria, it can be classified into three types: as acute, persistent, and chronic diarrhea . Acute diarrhea is the passage of stool with increased water content, volume, or frequency that lasts less than 2 weeks . Pathogens such as V. cholerae or E. coli, as well as rotavirus are common causes of acute watery diarrhea . Persistent diarrhea is defined as diarrhea that lasts at least 14 days and may include blood, and chronic if it lasts more than 4 weeks in duration. Children who are malnourished or who have other illnesses, such as AIDS, are more likely to have chronic diarrhea .Diarrhea is the second leading cause of pediatric mortality following pneumonia, with an estimated 688 million morbidities and 499,000 deaths globally among children under the age of five. Sub-Saharan African and South Asian countries account for 90% of all diarrhea related deaths in the world . A review study also reported that about 1.6 million people died from diarrheal diseases globally in 2017 and one-third of them were children under five years old . Evidence from demographic and health surveys of 34 sub-Saharan countries also reported that there was a significant clustering of diarrheal disease among under-five children across the communities and the overall prevalence of diarrhea in this age group was 15.3% . Furthermore, a meta-analysis study showed that in three east African countries, the prevalence of diarrheal diseases in children of less than five years of old was 27% from 2012 to 2017 and varied from 11% to 54% between different studies . Ethiopia, like sub-Saharan African nations, has a high level of morbidity and mortality due to acute diarrhea. The Ethiopian Demographic and Health Survey (EDHS) conducted in 2016 reported a 12.0% prevalence of diarrhea in the population .Many cases of sudden onset of diarrhea are self-limiting requiring no intervention. In severe cases, however, excessive fluid loss and electrolyte imbalance are the main concerns, especially in infants, children, and elderly patients, necessitating either nonpharmacologic treatments, such as oral rehydration therapy (ORT) and zinc supplements, or pharmacological treatments or both . Agents that suppress secretion and/or motility of the intestine are used in the symptomatic treatment of diarrhea. Of them, opioid drugs and their derivatives are being widely used in the management of diarrhea. Opioid drugs including diphenoxylate, loperamide, and difenoxin are commonly used opioids for this purpose. There are also many other drugs having antimotility or antisecretory effects on the intestine and used in treating diarrhea [12,13]. Antimicrobials are used for the treatment of infectious diarrhea and can reduce its severity and duration . The majority of the enteropathogens causing persistent diarrhea are treatable with antimicrobial drugs .The current drugs used for treatment of diarrhea have many problems, like drug resistance, drug-drug interaction, and adverse effects . Because of this, investigation of alternative medications derived from natural products is mandatory. Approximately 80% of the population in developing countries such as Ethiopia depends on traditional medicines for primary healthcare . In particular, the use of medicinal plants to treat gastrointestinal disorders such as diarrhea and dysentery occupied a major place in the traditional medicine of the Ethiopian community . There are a variety of medicinal plants used for the treatment of diarrheal diseases in Ethiopia. Verbascum sinaiticum, Cordia africana, Rumex nepalensis, Zehneria scabra, Verbena officinalis, Amaranthus caudatus, Calpurnia aurea, and Coffea arabica are some of the most commonly used medicinal plants .V. sinaiticum (ՙkutitina’ or ‘yeahiya jero’ in Amharic) is one of the medicinal plants used to treat diarrheal diseases [, , ]. The root and the leaf parts of the plant are used for the treatment of diarrheal diseases. The leaf part is crushed, homogenized in water and drunk. Similarly, the root is crushed and drunk with water or the juice of the root is taken orally . The herb is also utilized for the treatment of other ailments including hepatitis , mental illness, amnesia, tapeworm infestation, syphilis, gonorrhea, relapsing fever, rheumatic pain, elephantiasis, wound, and measles in Ethiopian traditional medicine . In addition, the plant has experimentally verified antibacterial, antitrypanosomal, hepatoprotective, and anti-proliferative activities [, , , ].V.sinaiticum is among the most commonly used medicinal plants to treat diarrhea in the traditional medicine of Ethiopia . However, the traditional claim of the plant for this use is not determined yet using scientific methods. Therefore, this study was conducted to validate the use of the plant to treat diarrhea in the traditional medical practice of Ethiopia. In addition, the findings of this study may initiate the research community in the field of pharmaceutical science to further investigate the chemical constituents of the plant for its antidiarrheal activity and their mechanism of action.2Materials and methods2.1Chemicals, drugs, and reagentsDistilled water, absolute methanol 99.9% (Hrego Chemical Ethiopia PLC), loperamide hydrochloride (Medochemie Ltd, Cyprus), atropine sulfate (Humanwell Pharmaceutical PLC, Ethiopia), castor oil (Amman Pharmaceutical Industries, Jordan), ethyl acetate (Alpha Chemika, India), activated charcoal (SD Fine Chemicals Limited, India), Tween 80 (Atlas Chemical Industries, India) were chemicals, drugs or reagents used. Additional lab reagents and chemicals were also used in the phytochemical screening test.2.2Instruments, apparatuses, and suppliesDigital electrical balance (Abron Exports, India), hot air oven (Medit Medizintechnik Vertriebs-GmbH, Germany), rotary evaporator (Yamato Scientific CO. Ltd., Japan), Whatman filter paper №1 (Schleicher & Schuell Microscience GmbH, Germany), surgical blade (SteriLance Medical Inc., China), oral gavage, gloves, gauze bandage, absorbent cotton, and syringes with needles were also used in this study.2.3Collection of the plant material and authenticationThe roots of V. sinaiticum were collected from Tara Gedam Monastery Forest which is located in South Gondar Zone, Amhara National Regional State, Ethiopia. In the mean while the plant specimen showing its full feature was collected for identification. Authentication of the plant specimen was done by a botanist at the Department of Biology, College of Natural and Computational Sciences, University of Gondar and the voucher number (SA01) was deposited there for future reference.2.4Extraction procedureThe collected roots were thoroughly washed with distilled water to remove dirt, soil, and any other foreign materials. The cleaned roots were chopped into smaller pieces manually and dried under shade at room temperature. The dried material was then grinded to coarse powder using mortar and pestle and extracted by cold maceration using 80% methanol as a solvent. Three flasks were taken and 650 g of the powder was soaked in a liter of 80% methanol in each flask and kept for 72 h at room temperature with ocassional shaking. Then the extract in each flask was filtered by using muslin cloth and Whatman grade №1 filter paper and the marc was re-extracted and filtered two times in the same fashion by using fresh 80% methanol. The methanol part of the filtrates was evaporated using a rotary evaporator set at 40 °C. The residue was then put in deep freezer at −20 °C and the aqueous portion was removed using a lyophilizer. The dried crude extract from each flask was combined and stored in a closed container and placed in a deep freezer until used for intended experiment.2.5FractionationSixty five g of 80% methanol crude extract was taken and successively fractionated using n-hexane, ethyl acetate, and distilled water. First, the crude extract was suspended in 390 ml of distilled water and an equal volume of n-hexane was added. The mixture was then shaken well in a separatory funnel and the n-hexane phase was separated. The aqueous residue was fractionated twice more using the same volume of n-hexane and separated similarly. The n-hexane portions from the three separate fractionation processes were combined. In the same fashion, the aqueous residue was fractionated in three rounds using 390 ml of ethyl acetate in each round and the ethyl acetate portions were combined. The n-hexane and ethyl acetate portions were concentrated by using a rotary evaporator. Then the ethyl acetate concentrate was stored in a tight container. But the n-hexane concentrate was found insignificant (all most null) and excluded from further consideration. The aqueous residue was also lyophilized and the dried aqueous fraction was stored in a tightly closed container. Finally, the dried ethyl acetate and aqueous fractions were placed in a deep freezer set at −20 °C until used.2.6Experimental animalsA total of 239 healthy Swiss albino mice of either sex weighing 20–30 g and aged 6–8 weeks were used in the study. The mice were bred under standard conditions. They were housed in plastic cages with softwood shavings as bedding, in the animal house of Department of Pharmacology, University of Gondar with a 12:12 dark-to-light period, at room temperature, and with free access to clean water and pelletized food ad libitum. All mice were acclimatized to the working laboratory environment one week prior to the experiment .2.7Animal grouping and dosingThe animals were randomly assigned to different groups for evaluation of the activities of the 80% methanol crude extract and the solvent fractions on castor oil-induced diarrhea, enteropooling, and charcoal meal transit models.In the evaluation of the effects of the crude extract, five groups each containing six mice were used for each model and dosed as follows.Group 1: received 10 ml/kg of 2% Tween 80 (negative control).Group 2: received 3 mg/kg of loperamide (in castor oil-induced diarrhea and enteropooling models) and 1 mg/kg of atropine (in gastrointestinal motility model) (positive control).Groups 3, 4, and 5: received 100, 200, and 400 mg/kg of 80% methanol extract.In each of the three models, eight groups each containing six mice were also used in the evaluation of the activities of the solvent fractions and received the treatments as follows.Group 1: received 10 ml/kg of 2% Tween 80 (negative control).Group 2: received 3 mg/kg of loperamide (in castor oil-induced diarrhea and enteropooling models) and 1 mg/kg of atropine (in gastrointestinal motility model) (positive control).Groups 3, 4, and 5: received 100, 200, and 400 mg/kg of the aqueous fraction, respectively.Groups 6, 7, and 8: treated with 100, 200, and 400 mg/kg of the ethyl acetate fraction, respectively.2.8Acute oral toxicity testThe acute toxicity of 80% methanol extract of V. sinaiticum roots was assessed according to OECD criteria for chemical testing . For the experiment, five female Swiss albino mice were chosen at random. First, a limit test dose of 2000 mg/kg body weight of the extract was administered to a single animal, which was then monitored for 24 h. Next, the limit dose was given to each of the remaining four animals because the first animal was still survived after a 24-h follow-up. The mice were closely observed for any signs of toxicity in the first 4 h, and then occasionally for the next 24 h. Thereafter, the mice were kept for up to 14 days with daily follow-up for the occurrence of any signs of morbidity or mortality.2.9Antidiarrheal activity determination2.9.1Castor oil-induced diarrhea in miceThis was done in accordance with the method employed by Shoba and Thomas . Thirty mice were divided into five groups at random, and each group was prevented access to food for 18 h. Each mouse was then put into a cage with a non-wetting paper sheet floored, which was changed every hour. Following this, each group received the crude extract, either of the fractions, standard drug, or the vehicle as narrated in the grouping and dosing section above. One hour after receiving the treatments, each animal in each group received 0.5 ml of castor oil orally. Following the castor oil delivery, each group's total number and weight of diarrheal drops were determined over the course of a 4-h observation period. The time for the onset of diarrhea in each animal was also determined as the interval between the castor oil delivery and the appearance of the first diarrheal feces. Then, the percentage inhibition of diarrhea from the negative control group was determined using the following formula.%inhibitionofdiarrhea=Meannumberofdiarrhealstoolsofnegativecontrol−treatedgroupMeannumberofdiarrhealstoolsofcontrolgroup×100This procedure was used in testing of the effect of the crude extract of the plant and the aqueous and ethyl acetate fractions of the crude extract.2.9.2Castor oil-induced enteropoolingThe effects of the 80% methanol extract and the solvent fractions on intraluminal fluid buildup were assessed using a method used by Sharma et al. . The mice were starved for 18 h and divided into groups. Then each group was housed in a cage, and given the treatment as described in the grouping and dosing section above. Each animal received 0.5 ml of castor oil an hour after receiving the vehicle (2% Tween 80), the crude extract, the aqueous or ethyl acetate fraction, and was sacrificed an hour later. The abdomen of each animal was then opened and the small intestine was ligated at the pyloric sphincter and the ileocecal junction and dissected out. Immediately after dissection, the intestine was weighed and its contents were collected by milking into a graduated tube and reweighed. The difference in the weights of the intestine before and after milking was noted. Then, using the following formulas, the percentage of decrease in intestinal secretion (in terms of weight and volume) was determined.%reductioninvolumeofintestinalcontent=MVICC−MVICTMVICC×100where, MVICC – mean volume of intestinal content (ml) of the negative control group, MVICT – mean volume of intestinal content (ml) of the treated group,%reductioninweightofintestinalcontent=MWICC−MWICTMWICC×100where, MWICC – mean weight of intestinal content (g) in the negative control, MWICT – mean weight of intestinal content (g) in the treated group.2.9.3Gastrointestinal motility testThe effects of the crude extract and the solvent fractions on gastrointestinal motility (transit) were examined in mice using the technique developed by Than et al. . The mice were chosen at random, fasted for 18 h, divided into groups, and subjected to the corresponding treatments as described above in the grouping and dosing section. After an hour of treatment, 0.5 ml of castor oil was administered orally to each mouse. Then, each animal received 1 ml of a 5% activated charcoal suspension in 2% Tween-80 orally, 1 h after castor oil administration. After 30 min of charcoal meal, each mouse was sacrificed and the small intestine was promptly dissected out from the pylorus to the caecum and placed lengthwise on a white paper sheet. Both the overall length of the intestine and the intestinal length traveled by the charcoal meal from the pylorus were measured. Finally, the peristaltic index (PI) and the percentage inhibition of the intestinal transit were calculated as follows for each animal.PeristalysisindexPI=IntestinallengthtravelledbythecharcoalmealTotallengthofthesmallintestine×100%inhibitionofintestinaltransit=Meanintestinallengthmovedbycharcoalincontrol−treatmentgroupMeanintestinallengthmovedbycharcoalincontrolgroup×1002.9.4In vivo antidiarrheal index (ADI)The in vivo anti-diarrheal index (ADI in vivo) was then expressed according to the formula developed by Than et al. .ADIinvivo=Dfreq×Gmeq×Pfreq3where, D freq is the delay in diarrheal onset (as % of control), G meq is the gut meal travel reduction (as % of control), and P freq is the reduction in the number of diarrheal stools (as % of control).Dfreq=Meantimeofonsetofdiarrheainthetreated−negtativecontrrolgroupMeantimeofonsetofdiarrheainthenegativecontrolgroup×1002.9.5Preliminary phytochemical screeningThe 80% methanol extract and the fractions were all subjected to qualitative phytochemical screening tests according to established testing protocols .2.10Ethical clearanceThe proposal of the study was presented to the Animal Ethics Review Committee of the Department of Pharmacology, University of Gondar, and an ethical approval letter was obtained from the Department of Pharmacology on behalf of the committee (Reference No. SoP 4/101/2013). Moreover, the animals were handed according to the guideline for the care and handling of laboratory animals .2.11Statistical analysisThe results were analyzed using SPSS software version 23 and expressed as mean ± standard error of the mean (SEM). The comparisons between group means were made using One-way Analysis of Variance (ANOVA) followed by Tukey HSD Post-hoc test. The differences between the group means were considered statistically significant at p-value < 0.05.3Results3.1Percentage yieldsA total of 203 g of dried crude extract was obtained from 1950 g of the coarse powder of the plant material. Accordingly, the percentage yield of the CE was 10.41%. From 65 g of the crude extract fractionated, 38 g (58.46%) and 16 g (24.62%) of AQF and EAF were obtained, respectively. The n-hexane concentrate was extremely small and beyond the sensitivity of the digital balance which was being used at the time.3.2Preliminary phytochemical screeningPreliminary phytochemical screening of the 80% methanol extract of the roots V. sinaiticum indicated the presence of anthraquinones, alkaloids, flavonoids, terpenoids, tannins, saponins, and phenols. However, steroids and glycosides were absent in 80% methanol crude extract of the plant. All of the phytochemicals detected in the methanolic extract were also detected in the AQF. Saponins were absent while others were similarly detected in the EAF (Table 1).Table 1Results of phytochemical screening test of the 80% methanolic extract and the solvent fractions.Table 1Secondary metaboliteCrude extractAQFEAFAnthraquinones+++Alkaloids+++Flavonoids+++Glycosides˗˗˗Tannins+++Terpenoids+++Saponins++˗Steroids˗˗˗Phenols+++AQF: aqueous fraction, EAF: ethyl acetate fraction, +present, ˗absent.3.3Acute oral toxicity testOver the course of the 14-day observation period, the limit dose, 2000 mg/kg, of the 80% methanol crude extract of V. sinaiticum roots did not result any significant toxicity or mortality. Furthermore, it was demonstrated that neither food nor liquid intake was decreased during the observation period.3.4The antidiarrheal activity of 80% methanol extract3.4.1Effects on castor oil-induced diarrheaThe crude extract of the plant significantly delayed the onset of diarrhea at 400 mg/kg (p < 0.001), relative to the negative control. The frequency of diarrheal drops was significantly decreased (p < 0.001) in groups received 200 and 400 mg/kg doses of the crude extract as compared to the negative control. The 100, 200, and 400 mg/kg of the extract treatments reduced diarrhea by 6.00, 41.8, and 50.76%, respectively. The three serial doses of the extract significantly reduced (p < 0.001) the weight of diarrheal stool compared to the negative control. Similarly, compared to the negative control, the water content of the fresh diarrheal stool was significantly decreased in groups received 100 (p < 0.01), 200, and 400 mg/kg (p < 0.001) doses of the extract. The effects of the highest dose of the crude extract were comparable with those of the standard drug in all parameters measured. The standard drug produced significant effects (p < 0.001) on the time for onset of diarrhea, frequency of diarrheal feces, and weights and water contents of the fresh diarrheal drops compared to the negative control (Table 2).Table 2Effect of 80% methanol extract of the root of V. sinaiticum on castor oil-induced diarrhea in mice.Table 2Group (treatment)Dose (mg/kg)Onset of diarrhea (min)Frequency of diarrheal stool% inhibition of diarrheaWeight of diarrheal stool (g)Water content of diarrheal stool (g)Group 1 (2% Tween 80)–61.50 ± 9.2211.17 ± 0.60–1.72 ± 0.050.82 ± 0.06Group 2 (Loperamide)3185.33 ± 19.29a3b3c14.67 ± 0.76a3b358.190.38 ± 0.05a3b30.23 ± 0.03a3Group 3 (CE)10072.17 ± 8.4310.50 ± 0.566.000.99 ± 0.09a30.43 ± 0.08a2Group 4 (CE)200120.50 ± 24.326.50 ± 0.43a3b341.810.60 ± 0.06a3b20.31 ± 0.07a3Group 5 (CE)400198.33 ± 8.12a3b3c15.50 ± 0.62a3b350.760.44 ± 0.05a3b30.30 ± 0.05a3Results are expressed as mean ± SEM (n = 6). acompared to Group 1(negative control), bcompared to Group 3, ccompared to Group 4, 1p < 0.05, 2p < 0.01, 3p < 0.001, CE: crude extract, Group 1: mice received 10 ml/kg of 2% Tween 80 in water and designated as negative control.3.4.2Effects on castor oil-induced enteropoolingIn the enteropooling assay, the 80% methanol extract of the root of V. sinaiticum demonstrated a significant reduction in the weight of intestinal contents at 100 (p < 0.05), 200 (p < 0.001), and 400 mg/kg (p < 0.001) of the doses. The percentage inhibitions in the weights of intestinal contents were found to be, 28.13, 45.31, and 43.75% at 100, 200, and 400 mg/kg doses of the extract in their order and 46.88% by the standard drug (Fig. 1(a) and (b)). The extract also significantly reduced the volume of intestinal contents at 100, 200 (p < 0.05), and 400 mg/kg (p < 0.001) doses as compared to the negative control. The percentage reductions in the volume of intestinal contents from that of the negative control were 35.37, 36.59, and 59.76%, at 100, 200, and 400 mg/kg doses of the crude extract, respectively. The standard drug produced 60.98% reduction in the volume of intestinal contents from the negative control (Fig. 1(c) and (d)).Fig. 1Effect of 80% methanol extract of the root of V. sinaiticum on castor oil-induced enteropooling in mice. (a) effect on weight of intestinal content, (b) % reduction in weight of intestinal content, (c) effect on volume of intestinal content, (d) % reduction in volume of intestinal content. Results are expressed as mean ± SEM (n = 6). acompared to Group 1(negative control), 1p < 0.05, 3p < 0.001. Group 1: mice received 10 ml/kg of 2% Tween 80 in distilled water (negative control), Group 2: mice treated with 3 mg/kg of loperamide (positive control), Group 3, 4, and 5: mice received 100, 200, and 400 mg/kg of the crude extract, respectively.Fig. 13.4.3Effects on intestinal motilityThe crude extract significantly reduced the intestinal transit of charcoal meal at all tested doses (p < 0.001) compared to the negative control. Compared to the lowest dose, the middle and the highest doses of the crude extract significantly reduced gastrointestinal transit of charcoal meal (p < 0.001) (Fig. 2 (a)). The three serial doses of the extract also showed significant reduction (p < 0.001) in peristaltic index compared to the negative control (Fig. 2 (b)). The percentage reductions in gastrointestinal transit were 44.52, 61.81, and 68.10% at 100, 200, and 400 mg/kg doses of the extract, respectively. The highest percentage reduction was produced by atropine, 70.03% (Fig. 2 (c)).Fig. 2Effect of 80% methanol extract of the root of V. sinaiticum on gastrointestinal transit in mice. (a) effect on length of small intestine moved by charcoal meal (intestinal transit), (b) effect on peristaltic index, (c) % reduction in intestinal transit. Results are expressed as mean ± SEM (n = 6). acompared to Group 1(negative control), bcompared to Group 3, ccompared to Group 4, 2p < 0.01, 3p < 0.001. Group 1: mice received 10 ml/kg of 2% Tween 80 in water (negative control), Group 2: mice treated with 1 mg/kg of atropine (positive control), Group 3, 4, and 5: mice received 100, 200, and 400 mg/kg of the crude extract, respectively.Fig. 2Fig. 3Diagrammatic representations of proposed antidiarrheal effects of the crude extract and the solvent fractions. *evidence from effects on castor oil-induced enteropooling, ♯evidence from effects on gastrointestinal motility, ⸸evidence from effects on castor oil-induced diarrhea.Fig. 33.4.4In vivo antidiarrheal index (ADI)The in vivo antidiarrheal indices of the crude extract were 16.67, 63.27, and 91.62 at the doses of 100, 200, and 400 mg/kg, respectively (Table 3).Table 3In vivo antidiarrheal index of 80% methanol extract of the root of V. sinaiticum.Table 3Group (treatment)Dose (mg/kg)Delay in onset of diarrhea (D freq)Reduction in intestinal length traveled by charcoal meal (G meq)Reduction in diarrheal stools (P freq)Antidiarrheal index (ADI)Group 1 (negative control)–––––Group 2 (positive control)–201.3570.0358.1993.62Group 3 (CE)10017.3544.526.0016.67Group 4 (CE)20095.9363.1641.8163.27Group 5 (CE)400222.4968.0950.7691.62Negative control: a group of mice that received 10 ml/kg of 2% Tween 80. Positive control: a group of mice received loperamide (3 mg/kg) in castor oil-induced diarrhea model and atropine (1 mg/kg) in gastrointestinal motility test model.3.5Antidiarrheal activities of the solvent fractions3.5.1Effects on castor oil-induced diarrheaThe AQF and EAF significantly delayed (p < 0.001) the onset of diarrhea at 400 mg/kg. Significant reductions in the frequency of diarrheal feces were produced by the AQF at 200 and 400 mg/kg (p < 0.001) and EAF at 200 (p < 0.01) and 400 mg/kg (p < 0.001) doses compared to the negative control. The percentage inhibitions of diarrhea were 8.57, 35.73, and 45.78% in groups treated with 100, 200, and 400 mg/kg of AQF and 8.57, 28.62, and 32.90% in groups received 100, 200, and 400 mg/kg of EAF, respectively. The highest percentage inhibition of diarrhea, 64.27%, was produced by loperamide. Furthermore, both the AQF and EAF at all of their serial doses (p < 0.001) showed significant reduction in the weight of fresh diarrheal stools compared to the negative control. Significant reductions on the fluid content of diarrheal stool were also produced by AQF at 100 (p < 0.01), 200, and 400 mg/kg (p < 0.001) and EAF at 200 (p < 0.01) and 400 mg/kg (p < 0.001) compared to the negative control. The highest doses of AQF and EAF showed comparable effects to the standard drug in all parameters measured in this model. The standard drug significantly (p < 0.001) delay the time of diarrhea onset and decreased the number, weight, and fluid content of diarrheal feces as compared to the negative control (Table 4).Table 4Effects of the solvent fractions of the crude extract of the root of V. sinaiticum on castor oil-induced diarrhea in mice.Table 4Group (treatment)Dose (mg/kg)Onset of diarrhea (min)Frequency of diarrheal stool% inhibition of diarrheaWeight of diarrheal stool (g)Fluid content of diarrheal stoolGroup 1 (2% Tween 80)–61.67 ± 4.9211.67 ± 0.49–1.70 ± 0.030.82 ± 0.06Group 2 (loperamide)3181.67 ± 19.15a34.17 ± 0.75a364.270.36 ± 0.04a30.21 ± 0.04a3Group 3 (AQF)10073.67 ± 4.58b310.67 ± 0.76b3d28.571.05 ± 0.09a3b30.50 ± 0.09a2b1Group 4 (AQF)200112.17 ± 23.31b17.50 ± 0.43a3b2c235.730.65 ± 0.05a3b2c30.40 ± 0.02a3Group 5 (AQF)400172.33 ± 7.25a3c36.33 ± 0.56a3c345.780.52 ± 0.05a3b30.40 ± 0.07a3Group 6 (EAF)10065.50 ± 5.47b310.67 ± 0.61b38.571.08 ± 0.06a3b30.65 ± 0.05b3Group 7 (EAF)200107.17 ± 23.31b28.33 ± 0.50a2b328.620.70 ± 0.05a3b3e30.45 ± 0.09a2Group 8 (EAF)400168.50 ± 8.25a3e3f17.83 ± 0.54a3b2e132.900.54 ± 0.06a3e30.36 ± 0.09a3e1Results are expressed as mean ± SEM (n = 6). acompared to Group 1(negative control), bcompared to Group 2 (positive control), ccompared to Group 3 (100 mg/kg AQF), dcompared to Group 4 (200 mg/kg AQF), ecompared to Group 6 (100 mg/kg EAF), fcompared to Group 7 (200 mg/kg EAF), 1p < 0.05, 2p < 0.01, 3p < 0.001, AQF: aqueous fraction, EAF: ethyl acetate fraction.3.5.2Effects on castor oil-induced enteropoolingThe AQF significantly reduced the weight of the intestinal contents at 200 (P < 0.05) and 400 mg/kg (p < 0.01) and the volume of the intestinal content at 100 (p < 0.05), 200 (p < 0.01), and 400 mg/kg (p < 0.001) of the doses, respectively, compared to the negative control. The percent reductions in the weight of intestinal contents were 26.98, 36.51, and 42.86% at 100, 200, and 400 mg/kg doses of this fraction, respectively. Similarly, this fraction produced 30.77, 42.31, and 50.00% reductions in the volume of intestinal contents at 100, 200, and 400 mg/kg of the doses, respectively. Significant reductions in the weight of the intestinal contents were also produced by 200 (p < 0.01) and 400 mg/kg (p < 0.001) doses of the EAF, while only 400 mg/kg of the fraction significantly decreased (p < 0.05) the volume of intestinal contents compared to the negative control. This fraction produced of 25.40, 36.51, and 42.86% reductions in the weight of intestinal contents at 100, 200, and 400 mg/kg of its doses, respectively. The fraction also produced 29.49, 39.74, and 48.72% reductions in the volume of intestinal contents at 100, 200, and 400 mg/kg of the doses, respectively. The standard drug, loperamide 3 mg/kg, showed a significant reduction in the weight (p < 0.01) and volume (p < 0.01) of intestinal fluid accumulation relative to the negative control. It decreased the weight and volume of intestinal fluid by 44.44 and 70.03%, respectively, relative to the negative control. There was no statistically significant difference between the effects of all doses of AQF and EAF. Similarly, there was no statistically significant difference between the effects of the standard drug and the solvent fractions on intestinal fluid accumulation (Table 5).Table 5Effects of the solvent fractions of the crude extract of the root of V. sinaiticum on castor oil-induced enteropooling in mice.Table 5Group (treatment)Dose (mg/kg)Weight of intestinal content (g)% reduction in weight of intestinal contentVolume of intestinal content (ml)% reduction in volume of intestinal contentGroup 1 (2% Tween 80)–0.63 ± 0.06–0.78 ± 0.04–Group 2 (loperamide)30.35 ± 0.02a244.440.32 ± 0.04a373.02Group 3 (AQF)1000.46 ± 0.0226.980.54 ± 0.07a130.77Group 4 (AQF)2000.40 ± 0.08a136.510.45 ± 0.09a242.31Group 5 (AQF)4000.36 ± 0.05a242.860.39 ± 0.03a350.00Group 6 (EAF)1000.47 ± 0.0525.400.55 ± 0.0929.49Group 7 (EAF)2000.40 ± 0.03a236.510.47 ± 0.1439.74Group 8 (EAF)4000.36 ± 0.02a342.860.40 ± 0.04a148.72Results are expressed as mean ± SEM (n = 6). acompared to Group 1(negative control), bcompared to Group 2 (positive control, 1p < 0.05, 2p < 0.01, 3p < 0.001, AQF: aqueous fraction, EAF: ethyl acetate fraction.3.5.3Effects on intestinal motilityAll the serial test doses of both of the solvent fractions of the root of V. sinaiticum significantly decreased (p < 0.001) the intestinal transit of charcoal and peristaltic index compared to the negative control. The 200 and 400 mg/kg doses of both fractions showed significant reductions (p < 0.001) in the intestinal transit of charcoal meal and peristaltic index compared to 100 mg/kg. The 100, 200, and 400 mg/kg doses of the AQF produced 40.05, 49.65, and 58.32% reductions in the gastrointestinal transit of the charcoal meal, respectively. The EAF inhibited intestinal transit of charcoal meal by 30.76, 49.69, and 59.11% at the doses of 100, 200, and 400 mg/kg, respectively. The standard drug, atropine 1 mg/kg, significantly reduced (p < 0.001) intestinal transit and peristaltic index compared to the negative control. The effect of the standard drug against gastrointestinal transit and peristaltic index was also significantly greater than those of the three serial doses of each fraction. The highest percent reduction in the gastrointestinal transit was produced by atropine, which was 69.59% (Table 6).Table 6Effect of the solvent fractions of the crude extract of the root of V. sinaiticum on gastrointestinal transit in mice.Table 6Group (treatment)Dose (mg/kg)Length of small intestine (cm)Length moved by the charcoal meal (cm)Peristaltic index% inhibition in intestinal transitGroup 1 (2% Tween 80)–60.62 ± 1.6945.32 ± 1.8474.62 ± 1.06–Group 2 (Atropine)160.05 ± 1.7713.78 ± 0.80a322.91 ± 0.98a369.59Group 3 (AQF)10058.40 ± 1.6027.17 ± 0.80a3b346.53 ± 0.48a3b340.05Group 4 (AQF)20055.85 ± 1.5122.82 ± 0.33a3b3c140.95 ± 0.79a3b3c349.65Group 5 (AQF)40059.37 ± 0.4918.89 ± 0.33a3b2c331.82 ± 0.61a3b3c3d358.32Group 6 (EAF)10061.07 ± 0.7931.38 ± 0.81a3b351.37 ± 0.95a3b330.76Group 7 (EAF)20056.13 ± 0.7022.80 ± 0.94a3b3e340.61 ± 1.58a3b3e349.69Group 8 (EAF)40058.35 ± 0.8518.53 ± 0.42a3b1e331.77 ± 0.71a3b3e3f359.11Results are expressed as mean ± SEM (n = 6). acompared to Group 1(negative control), bcompared to Group 2 (positive control), ccompared to Group 3 (100 mg/kg AQF), dcompared to Group 4 (200 mg/kg AQF), ecompared to Group 6 (100 mg/kg EAF), fcompared to Group 7 (200 mg/kg EAF), 1p < 0.05, 2p < 0.01, 3p < 0.001, AQF: aqueous fraction, EAF: ethyl acetate fraction.3.5.4In vivo antidiarrheal index (ADI)The in vivo antidiarrheal indices of AQF were 18.83, 52.57, and 78.25, while that of EAF were 11.79, 47.17, and 69.58 at the doses of 100, 200, and 400 mg/kg, respectively. The highest antidiarrheal index, 95.47%, was shown by atropine (Table 7).Table 7In vivo antidiarrheal index of the solvent fractions of the root of V. sinaiticum in mice.Table 7Group (treatment)Dose (mg)Delay in onset of diarrhea (D freq)Reduction in length traveled by charcoal meal (G meq)Reduction in diarrheal stools (P freq)Antidiarrheal Index (ADI)Group 1 (negative control)–––––Group 2 (positive control)–194.5869.5964.2795.47Group 3 (AQF)10019.4640.058.5718.83Group 4 (AQF)20081.8949.6535.7352.57Group 5 (AQF)400179.4458.3245.7878.25Group 6 (EAF)1006.2130.768.5711.79Group 7 (EAF)20073.7849.6928.6247.17Group 8 (EAF)400173.2359.1132.9069.58Negative control: a group of mice that received 10 ml/kg of 2% Tween 80. Positive control: group of mice received loperamide (3 mg/kg) in castor oil-induced diarrhea model and atropine (1 mg/kg) in gastrointestinal motility test model.4DiscussionAccording to published ethnobotanical study reports from Ethiopia [, , ], either the roots or leaves of V. sinaiticum are used to alleviate diarrheal diseases in the traditional medical practice. However, no prior investigation has been done to verify this assertion. Therefore, the reports were used as the foundation for the current experimental work. The antidiarrheal effects of various plants have been scientifically validated through analyzing their effects on different animal models. In light of this, castor oil-induced diarrhea, enteropooling, and gastrointestinal motility models in mice were used in this work to assess the antidiarrheal effects of the crude extract of the plant and the solvent fractions. The number and characteristics of the fecal outputs, time for the onset of diarrhea, intestinal transit ratio, and intestinal fluid accumulation are the commonly measured parameters in assessing the antidiarrheal effects of medicinal plants [, , ]. The models employed and the parameters considered in this investigation are consistent with those used in previously conducted similar studies.Due to their expanded polarity index, hydroalcoholic solvent combinations are often thought to provide good extraction yields. In general, hydroalcoholic co-solvents like 80% methanol appear to have the best solubility properties for initial extraction . Therefore, 80% methanol was preferred to extract the plant material. In addition, the 80% methanolic crude extract was fractionated with solvents of different polarities to get insight about the polarity of the phytochemical components of the plant.As shown in Tables 1, at least at the highest dose, the plant extract significantly delayed the onset of diarrhea and reduced the frequency, weight, and water contents of diarrheal drops in castor oil-treated mice in the 4-h observation period compared with the negative controls. Additionally, it was noted that an increase in the dose of the plant extract was accompanied by an increase in the percent reduction in the diarrheal outputs. Besides, the effect of the maximum dose of the crude extract was comparable to the effects of the standard treatment in all parameters examined. The significant delay in the onset of diarrhea caused by the highest dose of the extract, combined with an increasing pattern of percent inhibition in diarrheal episodes, suggests that the plant extract inhibits diarrhea more effectively at relatively higher doses. The percentage inhibition of diarrhea produced by the crude extract is comparable with the effects of the hydromethanolic root extract of Idigofera spicata and greater than that of Tetrastigma leucostaphylum leaves . However, the inhibitory effects of the extract is less than the extracts of other medicinal plants including Myrtus communis , Justicia schimperiana , and Ophiorrhiza rugosa .The results in castor oil-induced enteropooling model revealed that the 80% methanolic extract V. sinaiticum significantly reduced the weight and volume of intestinal contents at all the three serial doses in comparison to the vehicle. The percentage reductions in the weight and volume of intestinal contents were remarkably increased with the dose of the extract. The results demonstrated that the effect of the plant extract on percentage inhibition of castor oil-induced enteropooling is increased the the doses. Moreover, the results in this model revealed that the effect of the highest dose of the extract on intestinal fluid accumulation was found to be closer to and comparable with the inhibitory effect of loperamide. The findings in this model may indicate that the extract has a significant antisecretory effect and this contributes to its antidiarrheal effect noted in castor oil-induced diarrhea model.The reduction of gastrointestinal motility is one of the mechanisms by which antidiarrheal agents can act . The crude extract was found to decrease intestinal motility as shown by significant reduction (p < 0.001) in the intestinal transit of charcoal meal and peristaltic index compared to the negative control. Furthermore, the results in this model showed that the antimotility effect of the highest dose of the extract is comparable to that of the standard drug, atropine. This can be viewed, for example, in terms of the percentage reductions in gastrointestinal transit by 400 mg/kg of the extract and atropine, which were 68.10 and 70.03%, respectively. A decrease in the intestinal motility increases the stay of intestinal contents in the intestine and this might significantly increase the time for the absorption of water and electrolytes from the small intestine. This may in turn be attributed to the observed effects of the extract in castor oil-induced diarrhea and enteropooling models.Regarding the effect of the solvent fractions against castor oil-induced diarrhea, the AQF and EAF significantly delayed (p < 0.001) the onset of diarrhea at 400 mg/kg. Additionally, the middle (p < 0.01) and the highest doses (p < 0.001) of the fractions significantly reduced the frequency of diarrheal feces compared to the negative control. The antidiarrheal effects of AQF and EAF were further shown by the progressive percentage inhibitions of diarrhea with increasing doses. Furthermore, the fractions were shown to produce significant reductions in the weights of diarrheal stools as compared to the negative control. Besides, they also significantly decreased the fluid contents of diarrheal stools at all the serial doses compared to the negative control. Overall, the results in this model indicated that the AQF and EAF had significant activities against castor oil-induced diarrhea.The effects of the solvent fractions against castor oil-induced enteropooling were also assessed, and the results (Table 5) revealed that the fractions produced remarkable effects against intestinal fluid accumulation. Both fractions produced increasing reductions in the weight and volume of intestinal contents with increasing doses. Their effects were further elaborated by the progressive increments in the percentage reductions of the weight and volume of intestinal contents in the treated groups. The effects of AQF and EAF determined in this model could be attributed to their antidiarrheal effects demonstrated in the castor oil-induced diarrhea model.In the testing of the effects on intestinal transit of charcoal, all the serial doses of the fractions significantly decreased (p < 0.001) the intestinal transit of charcoal and peristaltic index compared to the negative control. The fractions were found to produce remarkable and consistent effects with increasing doses, as shown by a corresponding reduction in the mean intestinal length traveled by the charcoal meal and peristaltic index and an increasing percentage inhibition in intestinal transit. Therefore, the results in this model are in support of the effects of the fractions against castor oil-induced diarrhea.The ADI value often provides a more reliable measurement of the effectiveness of extracts in treating diarrhea . The ADI values increased with the doses of the crude extract and the fractions, suggesting that the crude extract and the solvent fractions caused dose-dependent antidiarrheal effects. Additionally, the crude extract exhibited the highest ADI value across all the test treatments at the corresponding doses, showing that it might have higher antidiarrheal activity than the solvent fractions. Regarding viewing the antidiarrheal effect in consideration of the ADI value, the result of this study is not consistent with a result reported by Ayalew et al. which shows that the highest antidiarrheal activity might be produced by the chloroform fraction, whereas the highest activity was produced by the methanolic crude extract followed by the aqueous fraction in this study. The results are also differed from those of a study on the antidiarrheal effects of various solvent extracts of Tetrastigma leucostaphylum which reported that the percentage inhibition of diarrhea and antidiarrheal index values produced by the various organic solvents are generally greater than resulted by the methanolic extract .The pathophysiologic mechanisms that cause diarrhea include altered intestinal motility that results in a shorter intestinal transit time, increased luminal osmolality and electrolyte release, and decreased electrolyte absorption [46,47]. Castor oil has been commonly employed to induce diarrhea in antidiarrheal activity studies because it releases ricinoleic acid, a metabolite that causes diarrhea, upon metabolism in the gut . Ricinoleic acid causes diarrhea by irritating the GI mucosa and promoting the release of prostaglandin, which in turn accelerates gut motility and electrolyte secretion and lowers electrolyte absorption from the small intestine and colon . In light of this, the effects of the crude extract and the solvent fractions against diarrhea may be the result of actions that counteract the activities of this metabolite to cause the pathophysiologic changes leading to diarrhea. It has been demonstrated that both the crude extract and the fractions significantly reduced the accumulation of fluid in the intestine. This suggests that they may promote water and electrolyte absorption and/or decrease the secretion. This in turn may lessen the overload and distension of the intestine. As a result, the intestinal motility may be decreased and this gives more time for the absorption of its contents. Hence, relatively lower water contents and frequency of diarrheal stools and longer onset of time for diarrhea episodes in groups received treatments may be due to underlying activities of the treatments to decrease secretion and/or promote the absorption of intestinal contents. This notion is compatible with the literature-presented mechanism of action of loperamide for its antidiarrheal effect . Reduced motility and secretion of the intestine may be also through atropine activities .The phytochemicals identified in the screening test may be principally responsible for the antidiarrheal effects of the extract and its solvent fractions. Anthraquinones, alkaloids, flavonoids, terpenoids, tannins, saponins (not in EAF), and phenols were identified in the extract and the fractions. The contents are similar to those of other plants having scientifically verified antidiarrheal activities, with some variations, including Zehneria scabra , Justicia schimperiana , Indigofera spicata , and Ophiorrhiza rugosa . Literature reports revealed that alkaloids and terpenoids (especially the monoterpenoid group) have antispasmodic activity , saponins suppress ilium contraction , flavonoids inhibit intestinal contractions and possess antispasmodic activity [52,54], tannins have an antispasmodic and muscle relaxant effect, and phenols reduce intestinal secretion and transit and have an astringent action . All of these activities could be the undelaying mechanisms for the antidiarrheal effects of the test treatments of the study. These chemical components may therefore be responsible for the antidiarrheal properties of the plant extract and the solvent fractions. Regarding the phytochemical test results, there is some discrepancy with a previous report on the phytochemical contents of the methanolic root extract of the plant . This difference may be attributed to seasonal variations in the collection of the plant material and/or differences in geographical area from which the plant material was collected.Furthermore, the CE of the plant was determined to be safe because no considerable signs of toxicity were noted from the acute toxicity test. This shows that the plant may not have observable toxicity in short-term use, even at doses higher than those utilized in the three antidiarrheal models of this investigation. This supports that the plant is most probably safe in short-course usage in traditional settings as well.This study did not include quantitative phytochemical determination and chemical characterization works. In addition, the study did not determine the possible chronic toxicities of the medicinal plant in the long run. These issues are considered the limitations of this study, and we recommend additional investigation on the plant to address these issues.5ConclusionOverall, the results of this study showed that the crude extract and the solvent fractions of the root parts of V. sinaiticum had considerable in vivo antidiarrheal activities. The crude extract, especially at 400 mg/kg, produced the highest effect followed by the aqueous fraction at the same dose. This might indicate that the bioactive compounds responsible for the effects are more of hydrophilic in nature. Moreover, the antidiarrheal index values were increased with the doses of the extract and the fractions, suggesting that the treatments might have dose-dependent antidiarrheal effects. Additionally, the extract was shown to be free of observable acute toxic effects. Thus, this study corroborates the use of the root parts V. sinaiticum to treat diarrhea in the traditional settings. Furthermore, the findings of this study are encouraging and may be used as the basis to conduct further studies in the area including chemical characterization and molecular-based mechanism of actions of the plant for its confirmed antidiarrheal effects.Author contributionsAll authors took part in the title suggestion, proposal development, and report writing processes. SAW carried out the lab works, data organization, and analysis. ABA prepared the manuscript. Finally the manuscript was reviewed by SAT, and some changes were made in response to his feedback. Furthermore, all authors are responsible for the accuracy and originality of this work.FundingSome financial support was received from 10.13039/501100007861University of Gondar, Ethiopia and used to purchase laboratory chemicals and supplies used in the study.Data availabilityThe data of this article can be obtained upon request.Additional informationNo additional information is available pertaining to this article.Declaration of competing interestThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

PMC

ACS Omega

PMC10357566

07-05-2023

10.1021/acsomega.3c02968

Metronidazole and Ketoprofen-Loaded Mesoporous Magnesium Carbonate for Rapid Treatment of Acute Periodontitis

Yu Zhaohan, Xiong Yan, Fan Menglin, Li Jiyao, Liang Kunneng

In the clinical pharmacological treatment of acute periodontitis, local periodontal administration is expected to be preferable to systemic administration. However, the action of the active medicine component is hindered and diminished by the limitation of drug solubility, which does not provide timely relief of the enormous pain being suffered by patients. This study aimed to develop a mesoporous magnesium carbonate (MMC) medicine loading system consisting of MMC, metronidazole (MET), and ketoprofen (KET), which was noted as MET-KET@MMC. A solvent evaporation process was utilized to load MET and KET in MMC. Scanning electron microscopy, nitrogen sorption, thermogravimetric analysis, and X-ray diffraction were performed on the MET-KET@MMC. The rapid drug release properties were also investigated through the drug release curve. The rapid antiseptic property against Porphyromonas gingivalis (P. gingivalis) and the rapid anti-inflammatory property (within 1 min) were analyzed in vitro. The cytotoxicity of MET-KET@MMC was tested in direct contact with human gingival cells and human oral keratinocytes. Crystallizations of MET and KET were completely suppressed in MMC. As compared to crystalline MET and KET, MMC induced higher apparent solubility and rapid drug release, resulting in 8.76 times and 3.43 times higher release percentages of the drugs, respectively. Over 70.11% of MET and 85.97% of KET were released from MMC within 1 min, resisting bacteria and reducing inflammation. MET-KET@MMC nanoparticles enhanced the solubility of drugs and possess rapid antimicrobial and anti-inflammatory properties. The MET-KET@MMC is a promising candidate for the pharmacotherapy of acute periodontitis with drugs, highlighting a significant clinical potential of MMC-based immediate drug release systems.

IntroductionEpidemiologically, periodontitis is among the most worldwide epidemic, afflicting 70% of the adult population older than 65 years in the US.1 Periodontitis originates from oral pathogens, for instance, Porphyromonas gingivalis (P. gingivalis), and pathogen-associated inflammation, which cause collateral tissue damage as well as clinical attachment loss.2 Patients with acute periodontitis, in particular, may experience severe pain, periodontal abscesses, or even systemic complications.3 One of the main reasons patients seek help from their dentists is to manage periodontal infection or severe pain; hence, appropriate antimicrobial and anti-inflammatory agents were utilized to combat acute infection and sharp inflammation as an adjunct strategy to the surgical approach.4,5Antibiotics and nonsteroidal anti-inflammatory drugs (NSAIDs) are routinely utilized to assist in the medication of periodontal diseases.6 For instance, metronidazole [MET, 1-(2hydroxyethyl)-2-methyl-5-nitroimidazole] is one of the primary antipathogen agents to treat acute periodontitis.7 Unfortunately, the antibiotics abuse in the medical domain has posed the risk of leading to antibiotic-resistant bacterial species.8 In addition, orally administered antibiotics have low concentrations in gingival crevicular fluid and may lead to antibiotic resistance in bacteria.9 Ibuprofen (IBU) is frequently prescribed as an analgesic in dental treatment due to its efficacy in alleviating mild to moderate pain.4 Ketoprofen (KET) exhibits superior efficacy compared to IBU in the management of topical pain at therapeutic doses, while also possessing a more favorable benefit–risk profile.10 Nevertheless, all NSAIDs increase the risk of potentially fatal bleeding and heart attacks or strokes when the drugs are given systemically.11Consequently, local drug delivery has been verified to have the ability to considerably improve medication concentration at the target site while decreasing adverse effects, treatment expense, and drug dosage.12 In recent years, many underdeveloped local periodontal materials have been proven to be effective.13 Low-solubility drugs exhibit delayed attainment of loading dose and inadequate local tissue concentration for the therapeutic efficacy within a short timeframe.13 Approximately 90% of agents in study and 40% of commercial agents are poorly soluble.14 The low solubility of these agents is, accordingly, among the primary causes of their low bioavailability.15 Besides, most recent trials on topical periodontal administration have focused on extended-release materials, which are ineffective for treating acute periodontitis.16 To date, there is still an unresolved problem with the poor water solubility of topical drugs used for periodontal administration. Additionally, there has been no investigation into the rapid effects of topical periodontal medications.Various solutions have been proposed to tackle this issue, including formulations of crystalline salts, reductions in active pharmaceutical ingredient (API) particle size, and co–ground combinations.17 Nevertheless, the effectiveness of formulation techniques is determined by the chemical composition of the agents as well as actual manufacturing issues.18 For example, the reduction of API particle size may lead to static charge accumulation, resulting in difficulties in handling certain medications.18 Yao and co-workers loaded 5-fluorouracil into azobenzene-functionalized interfacial cross-linked reverse micelles, relying on the high permeability of small molecules for drug delivery. Although this strategy enhanced the local drug accumulation, it failed to consider the poor solubility of the insoluble drug itself.19 However, recent studies have revealed that the crystallization of drugs could be restrained when they are embarked into mesoporous holes with pore diameters, ranging from 2 to 50 nm.20 Mesoporous magnesium carbonate (MMC) has recently been synthesized, exhibiting a narrow distribution of pore sizes and a wide superficial area.21,22 The amorphous forms of several poorly water-soluble compounds have been stabilized by loading them into MMC, resulting in increased solubilities and faster dissolution rates.23−25 This implies that MMC could fundamentally alter the solubility characteristics of the drug, converting crystalline low water-soluble medications into amorphous water-soluble medications.26 MMC improves solubility by increasing the amount of insoluble drug dissolved. Drug solubilization enables a rapid increase in the local drug concentration in a short period of time, resulting in rapid release.26,27 Therefore, the utilization of MMC is anticipated to serve as a highly efficacious strategy in enhancing the bioavailability of poorly water-soluble medications for treating acute periodontitis.28 Nonetheless, there have been no studies on MMC as a topical treatment for acute periodontitis.Herein, a MET-KET@MMC drug immediate-release system was established through a simple solvent evaporation method, using the commonly used oral antibiotic MET and KET. The MET and KET in MMC were rapidly released to achieve effective drug concentrations within 1 min, providing antibacterial and anti-inflammatory effects. Our work has led to the first use of the immediate-release carrier MMC in the field of pharmacological treatment of acute periodontitis.Results and DiscussionIn this study, MET and KET were loaded into MMC simultaneously for the first time. Both MET and KET have small molecular weights and could therefore access the pores of MMC.22,23,25 After dissolving in ethanol, the drug molecules free in solution were adsorbed into the pores of the MMC surface.22 As shown in Figure 1A, MET, KET, and MET-KET@MMC were white powder. Scanning electron microscopy (SEM) of MET, KET, MMC, and MET-KET@MMC is shown in Figure 1B. The surface of MMC had a concave and convex porous morphology. No major morphological changes were noted after loading drugs. Figure 1C illustrates the distribution of Mg, C, N, and O elements, thus indicating the incorporation of MET and KET. The results of thermogravimetric analysis (TGA) are shown in Figure 1D. The decomposition of the unloaded MMC took place at approximately 380 °C. At lower temperatures, crystallographic MET and KET decomposed almost completely (280 °C for MET and 350 °C for KET). The TGA curves of MET-KET@MMC indicate that no discernible mass loss occurred at the temperatures where crystalline MET and KET would typically decompose. The TGA profiles revealed a significant disparity in weight loss between MMC and MET-KET@MMC, which corresponded to the loading of both MET and KET (Figure 1D). Since MMC tends to absorb water, the quality decreased as the water in MMC and MET-KET@MMC evaporated in the range of 100–200 °C. Notably, the decomposition temperature of drug-loaded MMC was higher than that of unloaded MMC. The temperature elevation in this process can be accounted for by the Kelvin equation, which states that within the pores of MMC, the boiling point of the liquid will increase due to a rise in vapor pressure.23,29 This appearance has previously been noticed in the mesoporous drug carrier.23,29 In the differential scanning calorimetry (DSC) pattern, the absence of peaks corresponding to MET (at 160 °C) and KET (at 95 °C) indicates that drugs were incorporated in an amorphous state (Figure 1E). Previous attempts to incorporate additional APIs into the MMC resulted in drug crystallization within the pores of carrier materials.23 In contrast, the X-ray diffraction (XRD) of this experiment indicates that neither 10% wt of MET nor KET showed crystallization, demonstrating that there was no crystallization outside the pores of MMC (Figure 1F). The lack of the peaks of crystalline drugs indicated that the MET and KET incorporated into MMC were amorphous.25 Without MMC, the drug molecules in solution precipitated as crystals when the solvent evaporated. In contrast, after the drug molecules were absorbed into the pores of MMC, the mesoporous structure had an area-bound effect when the solvent was evaporated. This effect stopped the nucleation and crystal growth of identical molecules, which kept the drug in an amorphous form.30 The absence of crystalline drug peaks suggests that the MET and KET incorporated into MMC were amorphous.25 In the Fourier transform infrared spectroscopy (FTIR) spectra (Figure 1G), the absorbance band at ∼3440 cm–1 corresponded to adsorbed water. Bands at ∼1400 cm–1 correspond to the carbonate group. The overlapping absorption bands from MET and KET (1541 and 1697 cm–1) in MET-KET@MMC indicated that MET and KET were loaded into MMC.31 The FTIR spectra of MET-KET@MMC samples did not exhibit any new absorption bands, except for those observed in the absorption spectra of free KET and MET samples, indicating that the adsorption of MET and KET onto the pore walls was physical rather than chemical.23 The Brunauer–Emmett–Teller (BET) surface areas and pore volumes are presented in Table 1. The surface area and pore volume were reduced after drugs were loaded. This supports the results of XRD and DSC, indicating that MET and KET had actually entered the mesoporous structure of MMC. From the foregoing, we could consider that MET and KET were successfully loaded into MMC. The crystallizations of MET and KET were completely suppressed by the mesoporous structure of MMC.32Figure 1(A) Morphology of MET, KET, MMC, and MET-KET@MMC. (B) SEM images of MET, KET, MMC, and MET-KET@MMC. (C) Elemental mapping of MET-KET@MMC. (D) Normalized mass TGA of MET, KET, MMC, and MET-KET@MMC. (E) DSC curves for MET, KET, MMC, and MET-KET@MMC. (F) XRD patterns for MET, KET, MMC, and MET-KET@MMC. (G) FTIR transmittance spectra for MET, KET, MMC, and [email protected] 1SSA and Pore Volume of Unloaded and MET-KET@MMC [email protected] m2/g103.2685 m2/gpore volume0.83 cm3/g0.21 cm3/gDissolution is the rate-limiting process of absorption and consequently the limiting step of bioavailability.33 The rapid dissolution of the drug allows for its absorption in the body at the fastest possible rate, thus allowing the drug to react in a short time, which is significant for the relief of the sufferings of patients. Albeit short-term, quick release offers the benefit of the immediate therapeutic effect.34 It is imperative to investigate the capacity of MMC for prompt co-delivery of both drugs. Figure 2A illustrates the dissolution curves of crystallography. Figure 2A shows the dissolution profiles of MET, KET, and MET-KET@MMC in phosphate buffer saline (PBS). According to the concentration released from MET-KET@MMC, the entrapment efficiency and drug loading of MET and KET were 88.33% ± 0.99 and 7.36% ± 0.08, and 94.82% ± 1.23 and 7.90% ± 0.10, respectively (Table 2). Evidently, the dissolution kinetics of amorphous MET and KET in MMC were superior to those of their crystalline counterparts. During the initial minute, the release of MET from MMC was 8.76-fold higher than that from crystalline MET; similarly, the release of KET from MMC was 3.43-fold higher than that from crystalline KET (Figure 2A). Apparently, within the MMC, the dissolution efficiencies of amorphous MET and KET were higher than those of the free crystalline substances. Over 70.11% of MET and 85.97% of KET were released from MMC within 1 min (Figure 2B–E). After 2 h, the final concentrations of MET and KET released from MMC were 8.37 and 9.46 μg/mL. For a comprehensive assessment of the impact of the carrier on drug release, we recorded and presented 5 time points of the drug concentration (as shown in Figure 2B–E). The concentrations of MET and KET released from MMC were significantly higher than those of crystalline MET and KET in all 5 time points (P < 0.0001). The observed rapid release and dissolution of the drugs within the carrier pore structure do not affect the ultimate dissolved drug levels. The final concentrations of MET and KET were 8.37 and 9.46 μg/mL, while the final concentrations of MET and KET in MMC were elevated to 58.21 and 42.37 μg/mL, respectively. It can be observed that both drugs were released rapidly from MMC. The input amounts in PBS of crystalline MET and KET were basically the same as the actual drug loading in MMC, while more drugs were released in MMC during the same time. Crystalline drugs were dissolved in alcohol and turned into a free state (Figure 2G). The drug molecules were subsequently adsorbed into the mesoporous structure of the MMC. The crystalline propagation was blocked by the confinement of the mesoporous structure; hence, the drugs were stabilized in their amorphous forms, which is attributed to the interactions between APIs and pore walls, alterations in nucleation mechanisms, and kinetics within mesopores.23,32,35 The mechanisms for the increased solubility are as follows: when the amorphous molecules in MMC were in contact with water molecules, they could diffuse directly into the aqueous solution because there was no process to overcome the intermolecular forces from the crystal structure.21 Thus, the insolvable drugs in MMC could be rapidly dissolved in water (Figure 2G); according to the Noyes–Whitney equation, the rate of dissolution is directly proportional to the surface of particles and the solubility of their respective solvents. Therefore, it is a viable approach to enhance drug solubility through the expansion of surface area. In the present study, MET and KET were absorbed in the surface pores of MMC in their non-crystalline states, which expanded the contact area with water. The solubilities of MET and KET were boosted, and the drug concentrations elevated rapidly within a short time.Figure 2Drug release properties. (A–F) Dissolution profiles for MET, KET, and MET-KET@MMC in PBS. (G) Possible mechanism of the solubilization of MMC for insoluble crystalline drugs. The crystalline drug was dissolved in alcohol, allowing the drug molecules to infiltrate into the mesoporous structure of the MMC. The mesoporous structure maintained the drug molecules in the non-crystalline state. Mean ± SD is shown (n = 3). **** represents P < 0.0001.Table 2Entrapment and Drug-Loading Efficiencies entrapment ratedrug-loading rateMET88.33% ± 0.997.36% ± 0.08KET94.82% ± 1.237.90% ± 0.10Although MMC has been considered safe, we evaluated the cytotoxicity of MET-KET@MMC. As shown in Figure 3A, the live/dead staining of human gingival cells (hPDLCs) confirmed a non-toxic effect of MET-KET@MMC. The live cells of MET-KET@MMC were morphologically comparable to other groups. Figure 3B,C demonstrates that the viability of hPDLCs and human oral keratinocytes (HOKs) exposed to MET-KET@MMC (1 mg/mL) did not exhibit a significant decrease in comparison to the control cells (P < 0.0001). MMC has been classified as “Generally Recognized as Safe” (GRAS) by the FDA. One study reported that MMC preparations administered orally to male rats did not show toxicity to rats.20 Furthermore, in this study, the clinical application scenario for MMC was a periodontal rinse. Residual MMC was readily removed by the mouthwash, and there was little physical harm from the low cytotoxicity of MMC.Figure 3Cell viability of hPDLCs cultured in studied samples after 24 h and compared to growth media only. (A) Live/dead staining of hPDLCs. (B,C) CCK-8 assay of hPDLCs and HOKs. Viability of samples containing MMC presented acceptable cytotoxicity compared with the control group (1000 μg/mL) (mean ± SD, n = 6) (P < 0.05). n.s. indicates no significant difference.In the pathology of periodontitis, periodontal pathogens and cellular immune responses are two major factors.36P. g LPS has been used to induce immune responses in a number of studies around periodontal diseases.13,37 hPDLCs in periodontal ligaments recognize pathogenic factors and play a significant role in the innate immune response.38 Hence, P. g LPS was utilized to simulate the inflammatory state of hPDLCs infected with P. gingivalis in this study.39 Connective tissue injury and alveolar bone loss are induced by the pro-inflammatory cytokines IL-6 and IL-8.40 Therefore, we assessed the level of cellular inflammation by evaluating IL-6 and IL-8. The translational level and protein quantification, which were separately measured by real-time polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA) after 24 h of culturing with P. g LPS, are shown in Figure 4. After being stimulated by P. g LPS, IL-6 levels increased (∼3-fold at the transcriptional level and ∼3.24-fold regarding protein production) compared to control cells (Figure 4A,C). Relative to the MET-KET group, the expression levels of IL-6 and IL-8 were reduced in the MET-KET@MMC group (Figure 4B,D). The expression of IL-6 and IL-8 was considerably lower in the MET-KET@MMC group compared to the MET-KET group because of the higher KET concentration (P < 0.0001). This indicated that the anti-inflammatory agents released from MET-KET@MMC in only 1 min suppressed LPS-mediated cytokine production in hPDLCs. There was no statistically significant difference observed between the groups treated with P. g LPS and MET-KET. These results indicated that the inflammatory gene expression and the protein level could be significantly reduced by KET, which was rapidly released from MMC in 1 min, while the drug released from crystalline MET-KET in 1 min had no significant anti-inflammatory effect (P < 0.0001).Figure 4Anti-inflammatory effects of MET-KET and MET-KET@MMC on (A,B) cytokine and (C,D) protein expression of IL-6 and IL-8 in hPDLCs and HOKs stimulated with 10μg/mL P. g LPS. Mean ± SD is shown (mean ± SD, n = 3). The drug released from MET-KET@MMC in 1 min has been able to take an anti-inflammatory effect. **** represents P < 0.0001.The spread plate method proved that the antibiotic loaded in MMC was sufficient to kill P. gingivalis. The antibiotic constituents released from crystalline MET within 1 min exhibited a negligible killing effect against P. gingivalis (Figure 5A). The time–kill curves demonstrated the rapid efficacy of MET-KET@MMC against periodontal pathogens, demonstrating that the antibiotic agents released from MET-KET@MMC in just 1 min are sufficient to eradicate pathogens (Figure 5B). In contrast, a drastic decrement in microbe colonies appeared in the MET-KET@MMC group, where almost 100% of P. gingivalis were killed (Table 3). Damaged bacteria were observed on the titanium disc incubated with MET-KET@MMC, while no remarkable difference in bacterial form was found between the titanium disc incubated with MMC, MET-KET, and the control titanium disc (Figure 5C). The immediate killing effects were verified by live/dead staining (Figure 5D). P. gingivalis in the control group, MMC group, and the MET group displayed obvious green fluorescence. In contrast, 96.21% of dead bacteria (red fluorescence) was observed in the MET-KET@MMC group, indicating that MET released within 1 m resulted in rapid bacterial killing. These consequences were due to the rapid release of MET from MMC. In this study, a high concentration of MET was released from MMC in a short time, while crystalline MET only possessed a little antimicrobial property on account of its poor solubility. The rapid antibiotics process corresponds to the clinical treatment principle of using a large dose of antibiotics as the infection appears.22 Additionally, a previous study has demonstrated that MMC possesses antimicrobial properties attributed to the generation of reactive oxygen species, direct contact with microorganisms, and its alkaline effect.40 From these results, the drug released from MMC in 1 min showed antibiotic bacteriostasis against P. gingivalis, while the APIs released from crystalline drugs had no antibacterial effect due to the lower medicine concentration, according to the release curve and antiseptic test results. Given the above, MMC nanoparticles possess a wide field of application prospects in the treatment of acute periodontitis as well as various acute microbial infections.Figure 5Rapid antibacterial ability of MET-KET@MMC within 1 min. (A) BHI and agar plates of each group. The BHI of the MET-KET@MMC group is clarified, and there is no colony present on the agar plate. (B) Growth curves of MMC, MET-KET, MET-KET@MMC, and the control group without antibacterial agent. The antibiotic released from MET-KET@MMC in 1 min prevented the growth of P. gingivalis. (C) SEM micrographs of titanium discs incubated with studied samples after 2 d. The P. gingivalis were in normal shape in the MMC group and MET-KET group compared with the control group, while damaged bacteria could be observed on the titanium discs in the MET-KET@MMC group. (D) Live/dead staining images. The green fluorescence represented the live germs, while the red one denoted the dead. The MET-KET@MMC group shows the strongest red fluorescent signal.Table 3Antibacterial Efficiencies antibacterial rateacontrol1.48% ± 0.01aMMC3.14% ± 0.01aMET-KET5.35% ± 0.02aMET-KET@MMC100% ± 0.00baDissimilar letters (a and b) indicate significantly different values (mean ± SD, n = 3, P < 0.05).Our study demonstrated that MMC released both drugs rapidly and enabled drug concentrations to reach effective antibacterial and anti-inflammatory levels within brief periods. MET-KET@MMC could be applied in injectable pastes or flushes to rinse swollen gums and periodontal pockets in patients with acute periodontitis. The rapid release of MET and KET from MMC would provide a rapid bactericidal and anti-inflammatory effect, thus relieving the pain of patients and eliminating swelling. Furthermore, MMC is not specific for loading drugs and can be loaded with various insoluble small molecules such as IBU, tolfenamic acid, and rimonabant.23,25 It is conceivable that the use of MMC loaded with insoluble broad-spectrum antibiotics would be beneficial in the treatment of various acute infections. Therefore, MMC is anticipated to be used as a direct topical delivery agent for the treatment of skin infections and bone infections. In view of the intricate and diversified microenvironment of periodontal microbial communities, the effects of MMC as a fast-release carrier on multi-microbial or clinically relevant animal models are worth further proving. In summary, MMC, as an efficient fast-release drug carrier, could load diverse insoluble agents. If further developed, this technology is expected to be applied to a variety of acute dental infections for rapid relief of patient suffering.ConclusionsIn this study, we innovatively prepared a dual-functional topical rapid-release drug formulation, MET-KET@MMC, with antibacterial and anti-inflammatory functions, which was expected for the treatment of acute periodontitis. MET-KET@MMC possessed a faster release and dissolution compared to the dissolution of crystalline MET and KET. The rapid killing efficacy against periodontitis-related pathogens and rapid anti-inflammatory properties have also been confirmed in vitro, which would be beneficial to patients suffering from acute periodontitis. MET-KET@MMC did not show cell toxicity toward hPDLCs and HOKs. This work paves the way for further investigations of MMC as a pharmaceutical carrier in topical formulations targeting different types of acute oral infections and inflammation.Materials and MethodsSynthesis of MMCMMC was prepared by a combination of solvothermal synthesis, as described previously.25 In brief, 3 g of MgO (Sigma-Aldrich Inc., St. Louis, MO, USA) was put into 45 mL of CH3OH (Sigma-Aldrich Inc., St. Louis, MO, USA) with stirring at 500 rpm under 4 bar CO2 for 4 d at room temperature. Subsequently, air pressure was released, and the 5 mL of suspension in the reaction was slowly dropped to 250 mL of ethyl acetate at 25 °C. The resulting suspension was then continuously stirred until all of the solvents had evaporated in a well-ventilated area, leaving only a dried powder. To remove any residual organic groups formed during the reaction, the powder was heated to 250 °C for 30 min at a temperature ramp rate of 1 °C/min. The MMC was stored in a dry environment.25Drug-Loading ProcedureMET and KET (Sigma-Aldrich Inc., St. Louis, MO, USA) were incorporated into MMC via a simple solvent evaporation method to obtain MET-KET@MMC. Specifically, 25 g of MET and 25 g of KET were dissolved in 500 mL of ethanol, followed by the addition of 200 g of MMC into the solution. The mixture was subjected to orbital shaking at 500 rpm for 2 day at a temperature of 25 °C to facilitate drug diffusion into the porous structure of MMC. Subsequently, the solution was subjected to heating at 80 °C in an oven for alcohol evaporation and blended to achieve homogeneity.25Characterization of MET-KET@MMCSEM and Energy-Dispersive SpectroscopyMET, KET, and MET-KET@MMC were examined via SEM equipped with energy-dispersive spectroscopy mapping (Inspect F50, FEI, USA). Images were recorded with an acceleration voltage of 3 kV using the in-lens detector.41Differential Scanning CalorimetryDSC was performed with a DSC instrument (Netzsch, GER). Specimens of 3.5–5.5 mg were frozen at −20 °C and subsequently heated to 270 °C (3 °C min–1) under a N2 atmosphere. The temperature elevation process was accompanied by the recording of heat flow.Thermal Gravimetric AnalysisThe mass of 15 mg of samples was recorded as they were heated from 30 to 600 °C at a rate of 3 °C/min under a nitrogen atmosphere using a TGA instrument (Mettler Toledo TGA/SDTA851e).X-Ray DiffractionXRD was performed after MMC and MET-KET@MMC had been placed on a silicon holder (Ultima IV, Rigaku, Japan). The patterns were collected in the 2θ range from 5° to 80° at a rate of 0.02 °/s. Standard cards for MET and KET were obtained from Jade software.Fourier Transform Infrared SpectroscopySamples were ground with potassium bromide and pressed to slices.42 FTIR analyses were carried out using Fourier transform infrared spectroscopy (NICOLET is 50, Thermo Scientific, USA) from 350 to 4400 cm–1 at 25 °C.43Nitrogen Sorption AnalysisAt first, samples got degassed under vacuum for 12 h at 90 °C. The SSA was determined using the BET method. The total pore capacity was determined through single-point adsorption (P/P0 ≈ 1). The ASAP 2020 (Micromeritics) was employed for all computations.44In Vitro Drug Release Measurement10 mg of MET and 10 mg of KET were dissolved in a vessel containing 1500 mL of PBS at 37 °C with a stirring rate of 100 rpm. 100 mg of MET-KET@MMC was dissolved in another vessel containing 1500 mL of PBS under the same condition.25 As the same volume of fresh PBS was blended into the medium at the same moment, 0.5 mL aliquots were collected from each vessel and filtrated via a 0.2 m nylon filter (Servicebio, China).25 The liquid samples were measured by a Nanodrop 2000 spectrometer (ThermoScientific, Waltham, MA, USA). Absorbance measurements were conducted at 320 nm (MET) and 255 nm (KET).42 The concentrations of MET and KET were obtained via standard curves corresponding to the known dissolved MET and KET values. The measurements were carried out three times, and the standard and mean concentration values were also computed. The entrapment efficiency and drug loading of MET and KET were determined by the following calculationsCell CulturehPDLCs were obtained from healthy human premolar periodontal ligaments. Following extraction, the premolars were rinsed five times using PBS. Peripheral tissues of the middle third surface of tooth roots were scraped off and digested using collagenase type I (1 mg/mL, HyClone, UT, USA) at 37 °C for 0.5 h. The tissues were cultured in α-MEM medium (Gibco, Grand Island, NY, USA) with 10% fetal calf serum (Gibco, Grand Island, NY, USA), penicillin (100 U/mL, Servicebio, China), and streptomycin (100 g/mL, Servicebio, China). hPDLCs were passaged by trypsin after the cells had adhered and spread across the plate wall, and the cultures used for our experiments were those between passages 3 and 7.45,46 Besides, HOKs were grown for 24 h.In Vitro Cytotoxicity/Viability AssayCell viability was accessed using a cell counting kit-8 (CCK-8, Servicebio, China) assay.47 Cytotoxicity was evaluated in hPDLCs. hPDLCs and HOKs were incubated for 1 d and then inoculated in treated 96-well plates with medium containing MET, KET, MMC, and MET-KET@MMC (1 mg/mL) for 24 h, respectively. The seeding density was 5000 cells/well in 100 μL. According to the directions, cell viability was measured every 2 d. The absorbance was measured at 450 nm against a medium-only baseline.The following formula was used to calculate cell viabilityEvaluation of Anti-inflammation ActivitiesIn order to verify the solubilization capacity of MMC, all the samples were dropped in solution (α-MEM medium) for 1 min under the same conditions, and only the solution was collected after being filtrated through a 0.2 mm cylinder filter for the follow-up experiments. To be consistent with the practical drug concentration of MET-KET@MMC, MET and KET (MET-KET) were added to solutions in the same mass. As previously described, ultrapure lipopolysaccharide from P. gingivalis (P. g LPS) (Invivogen, San Diego, USA) in vitro, P. gingivalis was employed to emulate conditions of inflammation: 1 × 106 hPDLCs per well were cultivated in a 25 mm2 culture dish. After 24 h serum starvation, hPDLCs were irritated with P. g LPS (10 μg/mL) for 1 d.45 Then, the hPDLCs were rinsed with normal saline and incubated separately in the culture medium mentioned above. The hPDLCs that were incubated with pure α-MEM medium subsequently were referred t as a negative control group.Enzyme-Linked Immunosorbent AssayHuman IL-6 and IL-8 ELISA kits (Jiangsu Meimian Industrial Co., Ltd) were employed to measure the levels of interleukin-6 (IL-6) and interleukin-8 (IL-8) in the culture supernatants according to the manufacturer’s instructions.45Quantitative Reverse Transcription PCR (RT-qPCR)After a 24 h incubation, TRIzol reagent (Invitrogen) was used to extract mRNA according to the instructions.45,46 The RNA was reverse-transcribed into cDNA using the Rayscript cDNA Synthesis KIT (GENEray, GK8030, Shanghai, China) and subsequently employed in RT-PCR reactions with SYBR Green (GENEray, GK8030, Shanghai, China). Table 4 describes the primers of cytokines. Reactions were carried out on an ABI7500 apparatus (Applied Biosystems Inc., USA). Before being compared with the control, the level of mRNA to be tested was standardized to the level of GAPDH. Relative game expression levels were quantitated using the ΔΔCt method.48Table 4Primers of Cytokinescytokinesprimer sequenceIL-6 FF: TGCAATAACCACCCCTGACCIL-6 RR: GTGCCCATGCTACATTTGCCIL-8 FF: TTTTGCCAAGGAGTGCTAAAGAIL-8 RR: AACCCTCTGCACCCAGTTTTCGADPH FF: CCAGAACATCATCCCTGCCTGADPH RR: CCTGCTTCACCACCTTCTTGEvaluation of Antibacterial ActivityBacteria CultureP. gingivalis (ATCC33277, China) was cultivated in brain–heart infusion broth medium (BHI, Difco, Sparks, MA, USA) with hemin (5 mg/mL) and vitamin K1 (1 mg/mL) at 37 °C under anaerobic conditions (80% N2, 10% H2, and 10% CO2) in 96-well plates for 2 d. The amount of P. gingivalis was estimated by the absorbance of cultured germs at 600 nm utilizing a microplate reader (Multiskan Go, Thermo Scientific), corresponding to 1 × 108 bacteria/mL. The P. gingivalis was diluted to 3 × 107 colony-forming units (CFUs)/mL before use.22Spread Plate MethodThe P. gingivalis suspension mentioned above was incubated with BHI containing MMC (1 mg/mL), MET-KET (1 mg/mL within 1 min), and MET-KET@MMC (1 mg/mL within 1 min) separately. 100 μL of 10-fold serial dilutions from the mixed suspension was spread onto the BHI agar plates mentioned above, and the plates were incubated for 2 d in order to count the visible numbers of CFUs. The number of CFUs was recorded, the number of CFUs was determined, and the antimicrobial efficiency was computed using the equation belowwhere NC is the number of the control group and NE is the number of the experimental groups.49SEMThe bacteria suspension mentioned above was cultivated with sterile titanium discs in a 24-well plate for 2 d. BHI with MMC (1 mg/mL), MET-KET (1 mg/mL within 1 min), and MET-KET@MMC (1 mg/mL, within 1 min) were added into the plates and incubated for another 2 d anaerobically.50 After the incubation, the titanium discs were placed in a 2% glutaraldehyde–cacodylate-buffered fixative solution, dehydrated in graded alcohol, and critical-point dried. The occlusal section was sputter-coated with gold palladium.41 The SEM mentioned above was used to obtain all images of P. gingivalis.Time Kill AssayMMC (1 mg/mL), MET-KET (1 mg/mL within 1 min), and MET-KET@MMC (1 mg/mL, within 1 min) were added into BHI separately, and then, the plates were cultured anaerobically at 37 °C. Growth of P. gingivalis was monitored using a microplate reader every 2 h for 2 d.51Live/Dead Staining of BacteriaP. gingivalis suspensions mentioned above were cultivated in confocal dishes with different kinds of BHI at 1 mg/mL MMC (1 mg/mL), MET-KET, and MET-KET@MMC (1 mg/mL within 1 min), separately. After 48 h, the culture mediums were extracted, and the bacteria were stained by a Baclight Live/dead bacterial viability kit (Servicebio, China) in accordance with the instructions.52 A confocal laser scanning microscope (Olympus FV1000, Japan) was put to use to capture the fluorescence image.53Statistical AnalysesAll data were presented as means ± standard deviations (SD). The IBM SPSS (IBM Corp., New York, NY, USA) was used to analyze the data set. A one-way analysis of variance and Student’s t-test were performed to assess the significant effects of the variables. The means of each group were compared by Tukey’s multiple comparison test. Statistical significance was determined by P < 0.05.

PMC

Quantitative Imaging in Medicine and Surgery

PMC10784106

1-03-2024

10.21037/qims-23-291

Mono+ algorithm assessment of the diagnostic value of dual-energy CT for high-risk factors for colorectal cancer: a preliminary study

Chen Jun-Fan, Yang Jing, Chen Wei-Juan, Wei Xin, Yu Xiang-Ling, Huang Dou-Dou, Deng Hao, Luo Yin-Deng, Liu Xin-Jie

BackgroundRisk factors for colorectal cancer (CRC) affect the way patients are subsequently treated and their prognosis. Dual-energy computerized tomography (DECT) is an advanced imaging technique that enables the quantitative evaluation of lesions. This study aimed to evaluate the quality of DECT images based on the Mono+ algorithm in CRC, and based on this, to assess the value of DECT in the diagnosis of CRC risk factors.MethodsThis prospective study was performed from 2021 to 2023. A dual-phase DECT protocol was established for consecutive patients with primary CRC. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), overall image quality, lesion delineation, and image noise of the dual-phase DECT images were assessed. Next, the optimal energy-level image was selected to analyze the iodine concentration (IC), normalized iodine concentration (NIC), effective atomic number, electron density, dual-energy index (DEI), and slope of the energy spectrum curve within the tumor for the high- and low-risk CRC groups. A multifactor binary logistic regression analysis was used to construct a differential diagnostic regression model for high- and low-risk CRC, receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated to assess the diagnostic value of the model.ResultsA total of 74 patients were enrolled in this study, of whom 41 had high-risk factors and 33 had low-risk factors. The SNR and CNR were best at 40 keV virtual monoenergetic imaging (VMI) based on the Mono+ algorithm (VMI+) (SNR 8.79±1.27, P<0.001; CNR 14.89±1.77, P=0.027). The overall image quality and lesion contours were best at 60 keV VMI+ and 40 keV VMI+, respectively (P=0.001). Among all the DECT parameters, the arterial phase (AP)-IC, NIC, DEI, energy spectrum curve, and venous phase-NIC differed significantly between the two groups. The AP-IC was the optimal DECT parameter for predicting high- and low-risk CRC with AUC, sensitivity, specificity, and cut-off values of 0.96, 97.06%, 87.80%, and 2.94, respectively, and the 95% confidence interval (CI) of the AUC was 0.88–0.99. Integrating the clinical factors and DECT parameters, the AUC, sensitivity, specificity, and predictive accuracy of the model were 0.99, 100.00%, 92.68%, and 94.67%, respectively, and the 95% CI of the AUC was 0.93–1.00.ConclusionsThe DECT parameters based on 40 keV noise-optimized VMI+ reconstruction images depicted the CRC tumors best, and the clinical DECT model may have significant implications for the preoperative prediction of high-risk factors in CRC patients.

IntroductionColorectal cancer (CRC) accounts for approximately 10% of diagnosed cancers and cancer-related deaths worldwide each year, has the second highest mortality rate, with a prognosis influenced by tumor node metastasis (TNM) stage and other high-risk factors, and has 5-year survival rates ranging from 10% to 90% (1-3). High-risk factors for CRC include lymph node metastasis, extramural vascular invasion (EMVI), peripheral nerve invasion (PNI), high-grade tumors (including poorly differentiated adenocarcinomas and undifferentiated carcinomas, both less than 50% gland formation) T4 stage, and tumor deposits (4,5). For high-risk patients, the recommended duration of adjuvant therapy is 6 months if FOLFOX is chosen, while for the low-risk group, the recommended duration of adjuvant therapy is 3 months if the CapeOX chemotherapy regimen is chosen . In addition, patients with stage-II high-risk colon cancer who undergo routine standard management may have a worse prognosis than patients with stage-III low-risk colon cancer . Thus, screening the high-risk factors for CRC is important for the selection and timing of adjuvant chemotherapy regimens.Histopathology is the gold standard for the diagnosis of high-risk factor CRC. However, biopsy is invasive, and the results can be falsely negative due to the site and depth of the tumor. Thus, an accurate, objective and non-invasive preoperative method for evaluating high-risk factors for CRC is needed.Computerized tomography (CT) is one of the most important preoperative examinations for CRC. Compared to magnetic resonance imaging (MRI), CT has a faster imaging time and a better ability to detect distant metastases , but conventional CT provides only limited information to accurately assess high-risk factors, such as EMVI, lymph node metastasis, and tumor deposits.Dual-energy computerized tomography (DECT), which uses both high and low energies to achieve material decomposition and material classification, has the ability to assess the biological behavior of tumors by CT (9,10). Current applications of DECT in CRC include determining the nature of the lymph nodes, pathological staging, and histological grading (11,12). A recent study demonstrated the diagnostic value of DECT for the EMVI of rectal cancer . These previous studies focused on only one high-risk factor for CRC, but in clinical settings, patients may actually have one or more high-risk factors, and the preoperative predictive value of DECT for these patients remains unknown.Recently, the quantitative parameters of DECT have been a major advantage in tumor imaging, and the introduction of the Mono+ algorithm has pushed this to new heights. Mono+ is an algorithm that uses frequency division to superimpose the low spatial frequencies of a 40 keV image with the high spatial frequencies of a 70 keV image to improve the image noise at low keV in virtual monoenergetic imaging (VMI) mode, thus improving the contrast-to-noise ratio (CNR) of the image . Compared to standard linear reconstruction, VMI based on the Mono+ algorithm (VMI+) reduces image noise while improving iodine attenuation, especially at low kilo-electron volt (keV), which has been reported to improve the reliability and diagnostic accuracy of the DECT quantitative size measurements used to assess colorectal liver metastases (CRLMs) . Compared to conventional VMI, VMI+ has proven to be superior in qualitative and quantitative image analysis of cutaneous malignant melanomas, abdominal malignant lymphomas, and CRLMs (16-18). However, the quality of the qualitative and quantitative images based on VMI+ of CRC is unknown.This study had two main objectives. First, it sought to assess the optimal energy level for the qualitative and quantitative analysis of CRC based on the Mono+ algorithm. Second, it sought to analyze the tumor quantitative DECT parameters based on the optimal image and constructed a regression model with clinical factors to evaluate the diagnostic value for high-risk CRC. We present this article in accordance with the STROBE reporting checklist (available at prospective study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The Ethics Committee of the Second Affiliated Hospital of Chongqing Medical University approved the study and waived the requirement for informed consent because the study did not adversely affect the rights and health of the subjects.Between April 2021 to January 2023, the clinical and imaging data of 265 patients diagnosed with CRC were prospectively analyzed at The Second Affiliated Hospital of Chongqing Medical University. Sample size calculations were based on a small sample pre-experiment [venous phase normalized iodine concentration (VP-NIC) of 40.25±7.29 vs. 46.23±7.47 for the high- and low-risk groups] with 90% power at a two-sided alpha level of 0.05. Based on these results, about 34 cases were needed in each of the high- and low-risk groups. Ultimately, 74 patients (a total of 75 tumors) were included in this study, including 41 in the high-risk group and 33 in the low-risk group (Figure 1).Figure 1Flow chart showing the inclusion and exclusion criteria and the final grouping of patients. CT, computed tomography; ROI, region of interest.Based on the pathology of biopsy samples or postoperative lesion samples, the patients in this study were allocated to the high-risk group if they had ≥1 of the following risk factors: lymph node metastasis, EMVI, PNI, high-grade tumors, stage T4, stage M1, and tumor deposits. While the patients were allocated to the low-risk group if they had no risk factors.To be eligible for inclusion in this study, the patients had to meet the following inclusion criteria: (I) have histologically confirmed colon and rectal adenocarcinoma; (II) have no history of pelvic surgery; and (III) not have undergone preoperative chemoradiotherapy before the dual-energy CT examination. Patients were excluded from the study if they met any of the following exclusion criteria: (I) had poor quality images that affected diagnosis; (II) had not undergone surgery or a histological examination within two weeks of the CT examination; (III) had benign lesions or atypical colorectal adenocarcinoma based on the pathological findings; and/or (IV) had tumors that were too small (i.e., <20 mm) to draw regions of interest (ROIs).DECT acquisitionPatients were asked not to take drugs containing heavy metals, such as barium, for a week before the CT scan, and were asked to fast 12 hours before the CT examination and drink 600–1,000 mL of water 10 minutes before the examination. All the examinations were performed on a dual-energy CT system (Drive, Siemens Healthcare, Germany). Two contrast-enhanced CT scans were performed from the apex of the diaphragm to the inferior margin of the pubic symphysis in dual-energy spectral CT imaging mode. The scan parameters were as follows: tube voltages: 100 and 140 kV; tube current: 350 mA; pitch: 1; and gantry rotation time: 0.5 s. The contrast agent (Ultravist, 370 mg/mL, Schering, Berlin, Germany; dose: 1.5 mL/kg, flow rate: 3 mL/s) was injected through an elbow vein using a double-head power injector. The scan was threshold triggered, with the arterial phase (AP) automatically starting after a delay of 15 s when the abdominal aortic threshold reached 120 HU, and with the VP automatically starting after a delay of 17 s.DECT image reconstructionAll the DECT data sets were postprocessed on a commercially available three-dimensional (3D) multimodality workstation (syngo.via, version VA30A, Siemens) using a dedicated soft tissue convolution kernel (Qr40, Siemens) and an iterative reconstruction technique (ADMIRE, Siemens; strength level, 3). Standard linear mixed images were automatically reconstructed using a mixing factor of 0.6 (M0.6, containing 60% 100 keV low tube voltage, and 40% 140 keV high tube voltage) . Given the low attenuation of iodine concentration (IC) at energy levels above 100 keV in previous studies, for the image quality evaluation part of this study, the energy-level range was set at 40–100 keV in 10 keV increments .Quantitative image analysisThe quantitative image analysis was performed by a radiologist with three years of experience in abdominal radiodiagnosis on a Siemens syngo MMWP VE36A workstation. The location of the tumor was confirmed according to the histopathological results. Three circular ROIs with areas of 20–30 mm2 were placed in the largest axial image of the tumor, avoiding necrotic foci and larger vessels, and the final value was the average of these three ROIs. Defining fat standard deviation (SD) as image noise, three circular ROIs with an area of 100 mm2 were placed in the subcutaneous fat on the same slice of the outlined tumor, and the final value was averaged over the three ROIs. Based on previous studies, the signal-to-noise ratio (SNR) and CNR were calculated as follows : SNR=HU(lesion)SD(fat) CNR=[HU(lesion)−HU(fat)]SD(fat) Qualitative image analysisThe qualitative image analysis was performed by two radiologists with three and four years of experience in abdominal radiology, respectively. The observers were only aware of the patients’ clinical information and were not aware of the image reconstruction algorithm. The standard linear hybrid images (M0.6) and noise-optimized VMI+ reconstructed images were rated randomly, and only one random energy level was assessed during each round of evaluation. Based on a five-point Likert scale, the following quality criteria were assessed: overall image quality (where 1 = poor overall image quality and 5 = excellent overall image quality), lesion delineation (where 1 = unable to exclude lesions and 5 = excellent lesion margins), and image noise (where 1 = very pronounced image noise and 5 = barely detectable image noise).Imaging analysisAll the images were individually analyzed by two radiologists with more than 3 years of experience each in abdominal radiology who outlined the ROIs based on the histological examination as a reference. Duplex-enhanced images with a slice thickness of 1 mm were imported into the workstation and opened in dual-energy mode to automatically obtain virtual single-energy images, iodine-based material decomposition images, and effective atomic number images in the range of 40–150 keV. The localization of the lesions and outlining of the ROIs were performed with reference to 60 keV VMI+ on a 40 keV VMI+ single-energy image. Three ROIs of 20–30 mm2 were placed in the largest axis image of the tumor, avoiding large vessels and necrotic lesions, and selecting areas of uniform and distinct enhancement. The final value was the average of these three ROIs. The previously outlined ROIs were then copied to the iodine-based material breakdown image and the effective atomic number image. The final IC, NIC, effective atomic number, electron cloud density, dual-energy index (DEI), and slope of the energy spectrum in the AP and VP were recorded; the NIC of the tumor was obtained by normalizing the IC of the tumor to the IC of the aorta or iliac artery at the same level. The differential iodine concentration (DIC) in the arteriovenous phase was calculated using the following formula: DIC = (AP – VP) IC. The dual-energy curve slope value (λHU) was calculated from the CT values of the 50 and 140 keV virtual monochrome images {λHU = [CT50 value (50 keV) – CT140 value (140 keV)]/[140–50]}.Statistical analysisThe statistical analysis was performed using MedCalc Version 19.2 (Ostende, Belgium) and GraphPad Prism Version 9.4.1 (San Diego, California, USA). The intraclass correlation coefficient (ICC) was used to evaluate the interobserver agreement of the measured parameters (ICC 0.6), and all the measurements are summarized in Table 4. The final values of the DECT parameters of the high- and low-risk groups in the AP and VP are shown in Table 5. Patients in the high-risk group had significantly higher AP-IC (3.09±0.17 vs. 2.80±0.14 mg/mL, P<0.001), AP-NIC (22.30±4.77 vs. 19.65±2.84, P=0.005), AP-DEI [(21.72±1.75)×10–3 vs. (19.85±2.05)×10–3, P<0.001], AP-λHU (1.49±0.19 vs. 1.39±0.19, P=0.029), DIC (0.75±0.24 vs. 0.35±0.22, P<0.001), and VP-NIC (41.03±7.09 vs. 47.13±7.19, P<0.001) than those in the low-risk group, while the differences in the other DECT parameters were not statistically significant (Table 5). Figures 4,5 show the images of low- and high-risk CRC.Table 4Results of the intragroup correlation coefficients measured by two observers in the high- and low-risk groups in the arterial and venous phasesParametersICNICZRhoDEIλHUArterial phase ICC0.820.660.920.840.880.88 95% confidence interval0.73–0.880.51–0.770.87–0.950.75–0.890.82–0.930.81–0.92Venous phase ICC0.730.710.910.750.840.87 95% confidence interval0.60–0.820.58–0.810.86–0.940.63–0.830.76–0.900.80–0.91IC, iodine concentration; NIC, normalized iodine concentration; Z, effective atom number; Rho, electron density; DEI, dual–energy index; λHU, dual-energy curve slope value; ICC, intragroup correlation coefficient.Table 5Results of the DECT parameter measurements in the high- and low-risk groups of patients with CRC in the arterial and venous phasesDECT parametersHigh-risk group (n=41)Low-risk group (n=34)t/Z valueP valueArterial phase IC (mg/mL)3.09±0.172.80±0.146.77 (Z)<0.001 NIC (%)22.30±4.7719.65±2.842.79 (Z)0.005 Z8.86±0.168.80±0.16−1.59 (t)0.116 Rho41.20±6.8941.35±6.210.28 (Z)0.782 DEI (×10–3)21.72±1.7519.85±2.053.77 (Z)<0.001 λHU1.49±0.191.39±0.19−2.23 (t)0.029Venous phase IC (mg/mL)2.35±0.272.45±0.241.72 (t)0.090 NIC (%)41.03±7.0947.13±7.19−3.71 (Z)<0.001 Z8.64±0.188.67±0.170.71 (t)0.480 Rho40.75±5.4339.09±5.200.97 (Z)0.330 DEI (×10–3)16.85±2.6517.28±2.420.72 (t)0.470 λHU1.24±0.201.23±0.19−0.17 (t)0.867 DIC (mg/mL)0.75±0.240.35±0.22−7.35 (t)<0.001Data are shown as the mean ± SD. CRC, colorectal cancer; DECT, dual-energy computed tomography; IC, iodine concentration; NIC, normalized iodine concentration; Z, effective atom number; Rho, electron density; DEI, dual-energy index; λHU, dual-energy curve slope value; DIC, differential iodine concentration in the arteriovenous phase.Figure 4An 80-year-old woman with rectal cancer pathologically confirmed as a moderately highly differentiated adenocarcinoma without metastasis. (A) Images with an optimal SNR and CNR at 40 keV VMI+ in the arterial phase; (B) DECT images of IC in the arterial phase; (C) images of the effective atomic number and electron density in the arterial phase; (D) images of 40 keV VMI+ in the venous phase; (E) DECT images of IC in the venous phase; (F) images of the effective atomic number and electron density in the venous phase. SNR, signal-to-noise; CNR, contrast-to-noise ratio; VMI+, virtual monoenergetic imaging based on Mono+ algorithm; DECT, dual-energy computed tomography; IC, iodine concentration.Figure 5A 70-year-old man with rectal cancer, pathologically confirmed as a moderately differentiated adenocarcinoma but with six lymph node metastases. (A) Images with optimal SNR and CNR at 40 keV VMI+ in the arterial phase; (B) DECT images of IC in the arterial phase; (C) images of the effective atomic number and electron density in the arterial phase; (D) images of 40 keV VMI+ in the venous phase; (E) DECT images of IC in the venous phase; (F) images of the effective atomic number and electron density in the venous phase. SNR, signal-to-noise; CNR, contrast-to-noise ratio; VMI+, virtual monoenergetic imaging based on Mono+ algorithm; DECT, dual-energy computed tomography; IC, iodine concentration.In descending order, the areas under the curve (AUCs) for each parameter were AP-IC: 0.96, DIC: 0.89, AP-DEI: 0.75, VP-NIC: 0.75, AP-NIC: 0.69, and AP-λHU: 0.64 (Table 6; Figure 6). Parameters with P values <0.1 in the one-way binary logistic regression analysis were included in the multifactor binary logistic regression, and the final regression model obtained was as follows: logit (P) = 139.64 – 42.36 × AP-IC – 0.83 × AP-NIC – 4.26 × DIC – 560.57 × AP – DEI – 0.21 × VP-NIC – 2.03 × AP-λHU – 0.58 × CEA – 23.42 × histological grade. The AUC, sensitivity, specificity, and predictive accuracy of the model were 0.99, 100.00%, 92.68%, and 94.67%, respectively, and the AUC had a 95% CI of 0.93–1.00.Table 6Diagnostic efficacy of the DECT quantitative parametersParametersAP-ICAP-NIC (%)AP-DEIAP-λHUDICVP-NIC (%)AUC0.960.690.750.640.890.7595% CI for AUC0.88–0.990.57–0.790.64–0.850.52–0.750.80–0.950.64–0.84Cut-off point2.9421.5219.501.350.4841.50Sensitivity (%)97.0685.2952.9052.9485.2976.47Specificity (%)87.8060.9885.3778.0582.9368.29DECT, dual-energy computed tomography; AP-IC, concentrations of iodine in the arterial phase within the region of interest; AP-NIC, concentrations of normalized iodine in the arterial phase within the region of interest; AP-DEI, dual-energy index in the arterial phase within the region of interest; AP-λHU, dual-energy curve slope value in the arterial phase within the region of interest; DIC, differential iodine concentration in the arteriovenous phase; VP-NIC, concentrations of normalized iodine in the venous phase within the region of interest; AUC, area under the curve; CI, confidence interval.Figure 6Diagnostic efficiency of the DECT parameters and the predictive model in differentiating between high- and low-risk factors for CRC. AP, arterial phase; DE, dual-energy; NIC, normalized iodine concentration; IC, iodine concentration; VP, venous phase; λHU, dual-energy curve slope value; DIC, differential iodine concentration in the arteriovenous phase; AUC, area under the curve; DECT, dual-energy computed tomography.DiscussionThe preoperative prediction of risk factors for CRC is important because it not only influences the survival time of patients but also informs individual treatment plans. In this study, we first evaluated the optimal DECT image of CRC, and we then investigated whether the quantitative parameters of DECT could help in the preoperative selection of CRC patients with high-risk factors and constructed a comprehensive diagnostic model.Previous studies have shown that noise-optimized low-keV VMI+ series reconstructed images significantly reduce image noise while improving tumor saliency compared to conventional VMI series reconstructed images and standard linear hybrid reconstructed series images (16,21,22). The accuracy of DECT for tumor diagnosis has been directly enhanced by the improved contrast and lesion delineation with VMI+. One study found 40 keV VMI+ had significantly higher sensitivity and diagnostic accuracy for detecting CRC liver metastases than standard linear mixed reconstruction (90.6% vs. 80.6%, and 89.1% vs. 81.3%, respectively) . Lee et al. found that compared to standard 40 keV VMI, deep learning-based 40 keV VMI+ had better image quality and a higher detection rate of hypoenhancing hepatic metastasis, which shows that the strengths of VMI+ series reconstructed images could be replicated in the field of deep learning.In our quantitative image analysis, we also found that the 40 keV VMI+ series exhibited the highest tumor attenuation and higher SNR and CNR than the standard linear mixed M0.6 image series, and the results were consistent with those of previous studies . However, the overall image quality analysis in our study showed that the 60 keV VMI+ series scored the highest, indicating that tumor attenuation and noise balanced out at 60 keV VMI+, which is generally consistent with the findings of Lenga et al. . Thus, we further compared the 40 and 60 keV VMI+ CRC images. Notably, the blood supply vessels inside or beside the tumors were easier to identify in the 40 keV VMI+ images than the 60 keV VMI+ images; this finding enabled us to place the ROIs’ more accurately in the tumor, avoiding the interference of larger blood vessels. Thus, considering the advantages of both the 40 and 60 keV VMI+ images, we recommend using the 60 keV VMI+ series images as a lesion reference and then outlining the ROI on the 40 keV VMI+ series image to provide a more accurate delineation of the lesion for DECT quantitative analysis.To the best of our knowledge, the risk factors for CRC include lymph node metastasis, EMVI, PNI, high-grade tumors, and T4 stage. In an analysis of the prognosis of T2N0M0 CRC patients, lymphovascular permeation, perineural invasion, and poor differentiation were found to be risk factors for a poor prognosis . Another prognostic analysis of lymph node-negative CRC found that perineural infiltration, EMVI, and T4 staging were independent prognostic factors, and these patients would benefit from adjuvant or more aggressive treatment . Notably, a recent study demonstrated that high preoperative CEA levels and the presence of vascular cancer emboli were risk factors for lymph node metastasis, which suggests that the risk factors for CRC may be interrelated and coexist . Thus, in our study, we allocated patients with one or more risk factors to the high-risk group to provide a more objective and comprehensive preoperative predictive analysis of risk factors for CRC and evaluated the association between DECT quantitative parameters and high-risk factors in CRC tumors.According to the results, AP-IC, AP-NIC, AP-DEI, AP-λHU, and DEI were significantly higher in the high-risk group than the low-risk group. During tumor progression, tumor cells secrete vascular endothelial factors that increase the permeability of tumor blood vessels, leading to a higher intratissue microvascular density (MVD) and microvascular permeability in high-risk patients. IC, which is positively correlated with blood volume and permeability, directly reflects the angiogenesis and blood supply of the tumor tissue (27,28). Thus, it was not surprising that the AP-IC and AP-NIC values were significantly higher in the high-risk group than the low-risk group, and the results support those of Luo et al. .Additionally, due to the higher X-ray attenuation capacity of iodine compared to soft tissues, the higher the tumor tissue uptake of iodine intake, the greater the attenuation to X-rays and the higher the DEI and AP-λHU value, so the AP-DEI and AP-λHU were also significantly higher in the high-risk group than the low-risk group. However, as we noted that the VP-NIC in the high-risk group was lower than that in the low-risk group (41.59±7.30 vs. 46.56±7.75), we also analyzed the DIC between the two groups in the arteriovenous phase, and the results showed that the DIC was significantly higher in the high-risk group than the low-risk group (0.73±0.25 vs. 0.36±0.22), which may be due to the higher MVD and vascular endothelial growth factor (VEGF) expression in the high-risk group.The high expression of VEGF could increase the local vascular permeability and cause a higher clearance rate of the contrast agent (30,31). Thus, it may be that more contrast agents were retained in the tumor blood vessels in the low-risk group in the VP, resulting in the higher VP-NIC in the low-risk group than in the high-risk group. Nevertheless, there were no significant differences between the two groups in terms of electron density and effective atomic number, and the results support those of Qiu et al. . However, Zhang et al. found that the normalized effective atomic number value of metastatic sentinel lymph nodes in breast cancer patients was significantly larger than that of non-metastatic lymph nodes. This may be related to the different ways of outlining the ROI and the differences in the settings of the DECT scanning parameters, as well as the different tumor types. Thus, there is a need for a standardized dual-energy scanning protocol for different type of tumors.Of all the DECT parameters, AP-IC had the highest AUC. This is probably because IC is a direct reflection of the vascular enhancement of the tumor tissue and was influenced by other factors, such as NIC. This result supports that of Zou et al. . Further, we combined all the DECT parameters with significant differences and CEA and histological grade to establish a clinical DECT model and found that the AUC, sensitivity, specificity, and prediction accuracy of the regression model were 0.99, 100.00%, 92.68%, and 94.67%, respectively. The predictive accuracy of the clinical DECT model was significantly improved compared to the DECT parameter alone, indicating that the combined model may have greater value in high-risk factor predictions. The results were in line with some artificial intelligence (AI) CRC studies (35,36).Li et al. constructed a machine learning-based CT model that combined radiomics and clinical features that could predict early the presence of metachronous liver metastases in patients by measuring the primary lesions of CRC . The model had and AUC of 0.79±0.08 . Zhao et al. confirmed the excellent predictive efficacy of deep learning-based imagingomics models for lymph node metastasis in CRC . The models constructed in these studies have good predictive efficacy for subgroups with high-risk factors for CRC; however, they were based on conventional CT images of the arteriovenous phase. We speculated that the iodograms and effective atomic number maps derived from DECT may provide potential information, and the AI models based on these maps may perform well in studies related to high-risk prediction and the prognosis of CRC patients.Liver metastasis is one of the most important factors in the survival and prognosis of CRC patients. The early diagnosis of CRLMs implies a smaller lesion size, which is an indication for minimally invasive surgery . Compared to traditional open surgery, robotic-assisted minimally invasive surgery not only allows for the simultaneous resection of CRLMs and primary CRC, but also reduces intraoperative bleeding, postoperative complication rates, and postoperative hospital stays, while shortening the learning curve due to enhanced 3D full-high definition (HD) vision and wristed instruments (38,39). One study showed that laparoscopic ultrasound has better sensitivity than MRI for CRLMs, especially for lesions located in the liver dome , which may be due to the abnormal MRI signal in the liver dome. As far as we know, relatively few studies of DECT on CRLM have been conducted compared to studies of MRI. We believe that DECT will play an important role in the development of surgical protocols for CRLM and in the assessment of efficacy following chemotherapy as the benefits of its quantitative and qualitative analysis become better understood.This study had several limitations. First, it was limited to adenocarcinoma CRC, and special histologic types, such as mucinous adenocarcinoma, were not included. In addition, while one of the principles of ROI outlining is to match the biopsy results, ROI outlining is also based on the maximum cross-sectional area of the tumor, so the ROI outlining and biopsy results may not completely match. Finally, the sample size in this study was relatively small, and no further subgroup analysis was performed based on risk stratification. It will be interesting for us to explore the predictive performance of DECT for the risk stratification of CRC in the future.ConclusionsIn conclusion, our study demonstrated that 40 VMI+ images were optimal for CRC DECT quantitative evaluation. Additionally, the AP-IC, AP-NIC, AP-DEI, AP-λHU, and DEI DECT parameters reflected the biological behavior of high-risk CRC tumors; thus, these DECT parameters can serve as additional tools for the potential prediction of high-risk factors in CRC. The diagnostic performance of the composite model constructed by combining DECT parameters with CEA and histological grading appears to be at a clinically acceptable level, allowing a more accurate prediction of high-risk factors in each patient and assisting in clinical decision-making.SupplementaryThe article’s supplementary files as10.21037/qims-23-29110.21037/qims-23-291

PMC

Clinical and Experimental Reproductive Medicine

38035589

PMC10914497

3-01-2024

10.5653/cerm.2023.06009

Criteria for implementing artificial intelligence systems in reproductive medicine

Güell Enric

This review article discusses the integration of artificial intelligence (AI) in assisted reproductive technology and provides key concepts to consider when introducing AI systems into reproductive medicine practices. The article highlights the various applications of AI in reproductive medicine and discusses whether to use commercial or in-house AI systems. This review also provides criteria for implementing new AI systems in the laboratory and discusses the factors that should be considered when introducing AI in the laboratory, including the user interface, scalability, training, support, follow-up, cost, ethics, and data quality. The article emphasises the importance of ethical considerations, data quality, and continuous algorithm updates to ensure the accuracy and safety of AI systems.

IntroductionArtificial intelligence (AI) algorithms have become ubiquitous in our lives, and the field of assisted reproductive technology (ART) is no exception. In recent years, increasingly many publications in scientific journals and conferences have highlighted the various applications of AI in reproductive medicine [1,2]. These applications span a wide range of areas within the field of reproductive medicine [3-5]. As embryologists, as well as physicians, we have the duty to keep abreast of the existing technologies, and above all, their function and results, before accepting the incorporation of any new tool in clinical practice. The present work aims to provide key concepts to be taken into consideration when considering integrating AI systems into reproductive medicine practices.Artificial intelligence in assisted reproductive technologyAmong the numerous published algorithms, we can find predictive models for embryo transfer outcomes on day 2/3 and blastocyst stage [7,8], sperm selection by image recognition correlated with fertilization and blastocyst formation , prediction of obtaining spermatozoa from testicular biopsies , non-invasive oocyte scoring on two-dimensional images , cytoplasmic recognition of the zygote , morphokinetic automated annotation of the embryo [13-15], automated blastocyst quality assessment , embryo implantation potential via morphokinetic biomarkers , euploidy prediction using metabolic footprint analysis , ranking for embryo selection [19-25], blastocoel collapse and its relationship with degeneration and aneuploidy , morphokinetics and clinical features for the prediction of euploidy , prediction of aneuploidy or mosaicism using only patients’ clinical characteristics , tracking of menstrual cycles and prediction of the fertile window , control of culture conditions and quality control of embryologist performance [25,30], intrauterine insemination success , computer decision support for ovarian stimulation , prediction for the day of triggering [33,34], and follicle-stimulating hormone dosage prediction for ovarian stimulation . All the mentioned references are depicted in Figure 1. Machine learning models are listed in Table 1 [6,7,10,17,18,27-29,31-37], while those corresponding to the deep learning subset can be found in Table 2 [8,9,11-16,19-24,26,38-43]. In these tables, the AI models are described with their sample size, results and limitations. The main limitation of all studies was their retrospective nature. A limited sample size, imbalanced dataset, and lack of multi-center evaluation were also common limitations found in the literature review.Commercial platforms or in-house algorithmsThe AI systems used in in vitro fertilization (IVF) clinics can be categorised into two types: commercial products and self-developed in-house solutions. While cloud-based systems can offer advantages for IVF clinics with lower workloads, such as leveraging data from other clinics, they may face challenges in maintaining predictive accuracy due to interference from individual clinic protocols or conditions. Notable examples of cloud-based products include Embryo Ranking Intelligent Classification Algorithm (ERICA) , intelligent Data Analysis Score (iDAScore) , and Life Whisperer .In contrast, adopting an in-house approach could offer certain advantages, such as greater control and customisation over the AI system and its workflow as well as the possibility to test own ideas without having to wait for commercial releases. Single-center studies such as Zeadna et al. or De Gheselle et al. represent this approach to AI in IVF.Requirements for implementing new AI systems in the laboratoryPrior to introducing a new AI system—or any other technique—it is essential to ensure that it satisfies certain criteria in a laboratory setting. At least one of the following criteria should be met for the new technique to be considered suitable: the candidate AI system should have the ability to improve results, such as the live birth (LB) rate, time to pregnancy, or any other key performance indicator. If the results are not worsened, other criteria to be met could include making work easier and more efficient, saving time and resources, offering greater safety through an improved error detection, or providing better explainability.Factors to consider when introducing AI in the laboratoryThere are several factors that cannot be overlooked when considering the integration of a new system into the laboratory. These factors must be carefully evaluated before making a decision. When introducing an AI system, the following factors must be taken into account:1. User interfaceThe user interface (the visual display on the screen) should be easy to understand and navigate.2. ScalabilityThe system should be capable of adapting to the laboratory's needs, including the volume of data and users, as well as being integrated into the laboratory's workflow and protocols. If the AI platform cannot be adapted to the laboratory’s existing workflow, it is necessary to evaluate the impact of adapting the lab workflow and the potential benefit of using that AI platform.3. TrainingThe manufacturer should offer information regarding the required training for users and how it will be delivered.4. SupportThe manufacturer should specify the type of technical support offered, who will be responsible in case of failure, and what the response time will be.5. Follow-upAs AI systems continuously learn, it is crucial to ensure that the algorithms are updated to accommodate new data. The manufacturer should provide information about the maintenance and monitoring plan to ensure that the system continues to provide accurate and unbiased results.6. CostThe cost of a system should be considered in relation to the center’s budget and investment capacity.7. EthicsTo ensure that an AI system is ethically sound, it is important to evaluate its impact on patient care and outcomes. The system should not only improve patient outcomes but also avoid any harm or negative impact on the patient. Moreover, the manufacturer should have measures in place to ensure the confidentiality and security of patient data, such as the ISO 27002-2021 and IEC 62304 standards. The most important ethical issue is the lack of randomised controlled trials. It is premature to implement a technology in the clinical setting before the trial results are made available . The nature of the mathematical algorithms performed during the AI process leads to a spectrum of transparency, ranging from the most interpretable models, such as linear regression-based algorithms, to the most cryptic models, also called black-box, such as neural networks. It is important to know the risks, side effects, benefits and the confidence of each clinical decision before delegating the decision-making process to machines. While transparent models enhance clinical decision-making, black-box systems replace human decisions, leading to uncertainty about the responsibility for treatment success. Black-box algorithms could build predictive models biased by cofounders, and the error-checking processes of each prediction could go unnoticed by human operators . Moreover, opaque models could increase the risk of imbalanced outcomes. For instance, if there exists a correlation between embryo quality assessed by AI and gender, there could be an intrinsic imbalance that could take more time to detect than in interpretable models.8. Data qualityThe quality of data refers to the data’s accuracy, completeness, timeliness, relevance, consistency, and reliability. It is crucial for an AI system to have access to high-quality data to provide accurate and reliable results. If the data used for building the model are not reliable and generalisable, then the AI model will fail when applied to new data in the near future. Some models are based on a concrete and certain population, and if data across populations are not as homogeneous, then the model will not be accurate enough. Furthermore, in embryology, confounding factors such as age should not be used as predictors in embryo quality models if it is desired to develop an embryo quality model instead of an age-based predictive model , as the AI algorithm could base its prediction mostly on data included in the age variable with no importance for embryonic features.9. PerformancePerformance refers to the effectiveness and efficiency of an AI system in achieving its intended objectives, such as accuracy, speed, and reliability. The system's performance should be evaluated based on relevant metrics and benchmarks to ensure that it meets the desired standards.Data annotationThe source of data is crucial in data annotation. The origin of data can vary (tabular, images, videos, audio, the outcome of a previous AI algorithm, etc.), and the annotation of data is expected to be more effective when automated, since automation removes the subjectivity of human-annotated parameters. However, the effectiveness of automated versus manual annotation depends on the degree of intra-individual and inter-individual variability for the target variable when annotated by humans and the reliability of the automatic annotation methods [45,46]. Features with higher variability or lower reliability can lead to lower performance of predictive models, since AI may use different values for data that are actually equivalent. Including such features in the models can introduce noise or inconsistencies, affecting the accuracy of predictions and the model’s overall performance. Determining whether manual or automated annotation is more suitable depends on each specific case. Factors such as data complexity, available resources, and the desired level of accuracy need to be considered. Manual annotation can provide more accurate and reliable results, but can be time-consuming and introduce human biases. Automated annotation methods can be more efficient and scalable, but may be less accurate or reliable, especially in cases with noisy data or lack of proper validation.It is not always possible for all values in a database to be filled. Not available (NA) values represent a problem when building AI algorithms and require proper handling. Several options exist for managing missing values. Some common approaches include discarding observations with NA values, imputing missing values using methods such as mean or median imputation, or utilising other AI algorithms such as k-nearest neighbour for imputation, as well as directly excluding the feature with NA values.Machine learning techniques are also sensitive to data points that deviate significantly from the majority of the data (outliers). Managing outliers involves deciding whether to integrate them into the analysis or discard them.Therefore, careful consideration is required when dealing with NA values and outliers. The choice of appropriate strategies for managing them depends on the specific context, the nature of the data, and the objectives of the analysis.Risk factors affecting data quality in model designEach predictive model has its unique characteristics and objectives, and is based on a specific experimental design that includes certain factors as inclusion and exclusion criteria. It is crucial to carefully review the experimental design, as there could be potential risks that may affect the quality of data used in the model. One such risk would be the possibility of data bias due to the inclusion criteria, which could compromise the generalisability of the results, particularly if there were confounding factors affecting predictive variables [47,48]. Three additional pitfalls to consider, as described by Curchoe et al. , are small sample sizes, imbalanced datasets, and limited performance metrics.Furthermore, in classification cases, there could exist a risk of mislabelling in the output variable. Mislabelling occurs when the categorical variable has incorrect labels for some of the data points. It is important to be aware of this risk, as the inclusion of mislabelled data decreases accuracy [50,51]. A potential example of mislabelling in embryology is evident in two embryo selection models with different labels for classification. One model compares implanted or LB embryos versus non-implanted or non-live birth (NLB) embryos , while the other compares euploid versus aneuploid embryos . In the LB versus NLB comparison, it is important to carefully consider the potential for mislabelling, as high-potential embryos with a negative outcome due to reasons unrelated to the embryo could be incorrectly labelled as NLB, which may negatively impact the performance of machine learning and deep learning algorithms [36,40,52]. Additionally, in ploidy models, undetected mosaicism can also lead to mislabelling. Moreover, the "Schrödinger embryo" paradox makes it impossible to assess the genetic status of the inner cell mass and trophectoderm until the whole embryo has been donated for research. Once an embryo has been donated, it becomes impossible for it to achieve LB, and its real potential for viability will remain unrealised. Besides, the algorithms’ performance may be distorted depending on the inclusion criteria in each experimental design. There is a risk of including embryos with low viability potential, those that have not yet been transferred, or even euploid embryos that were not cryopreserved due to low quality . Specifically, Tran et al. reported that the area under the curve (AUC) could be inflated by including many arrested embryos in the sample used to compute it. That predictive model could be considered proper for justifying automation for the quality assessment of arrested embryos, although random choice was supposed to be used for non-arrested embryos .Machine and deep learning modellingMachine and deep learning modelling refer to the process of creating and training mathematical models that can automatically identify patterns and make predictions or decisions based on data. Deep learning is included in the broader category of machine learning category. These models are built using algorithms and statistical techniques that allow computers to learn from large datasets and improve their performance over time . To emphasise the main differences, it is worth noting that machine learning typically requires fewer data points and provides greater interpretability than deep learning. As a rule of thumb, the sample size should be at least 10 times the number of parameters in an algorithm, and it is generally easier to determine this value for machine learning models than for deep learning models .There are two primary types of machine learning algorithms: supervised and unsupervised. On the one hand, supervised learning is an approach in which a model is trained using labelled data. After introducing input features (independent variables) along with corresponding target labels (dependent variable), supervised learning tries to learn a function or a relationship between the input features and the target labels. Once trained, the model can make predictions or classify new instances based on the input features. Supervised learning is commonly used in prediction and classification problems, where the objective is to predict a specific outcome or category, although numerical values can also be predicted through regression models. Decision trees, scoring systems, generalised additive models, and case-based reasoning are among the primary techniques used in various supervised learning algorithms . Each algorithm has its own specific characteristics and uses. Linear regression involves fitting a linear equation to the data, enabling the prediction of continuous target variables . Logistic regression is mainly used for binary classification tasks, although it could also be useful for multi-class problems, by modelling the probability of an event occurring based on input features . Recursive partitioning is a technique commonly used in decision trees, where the data are recursively split into subsets based on certain conditions of features . Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy and reduce overfitting [17,31]. The k-nearest neighbour method classifies or predicts the value of a data point based on the values of its k-nearest neighbours in the feature space [34,57]. Gradient boosting is an ensemble technique that builds a strong predictive model by iteratively combining multiple weak models, often decision trees, to correct errors made by the previous models . Support vector machines construct hyperplanes in a high-dimensional feature space to separate different classes or estimate continuous target variables [31,57]. Neural networks are complex and versatile machine learning algorithms capable of handling various tasks, including classification, regression and pattern recognition. They are inspired by the structure of the human brain. Image recognition models are based on this type of algorithms [13,16,19,20].On the other hand, unsupervised learning is employed in situations where the training data lack pre-existing labels or outcomes. Its objective is to discover patterns or structures inherent in the data without explicit guidance and to uncover similar groups or clusters of data. This type of learning is useful for exploring and comprehending the underlying structure in data and identifying hidden patterns. Clustering algorithms and dimensionality reduction methods are widely used in the field of unsupervised learning. K-means is a popular clustering algorithm aiming to divide a dataset into distinct groups or clusters based on similarity. The algorithm iteratively assigns data points to the nearest cluster centroid and updates the centroids until convergence . Principal component analysis (PCA) is a dimensionality reduction technique that transforms a high-dimensional dataset into a lower-dimensional space by identifying the principal components that capture the most significant variance in the data. These principal components are orthogonal and ordered in terms of their explanatory power. PCA is useful for simplifying complex datasets, visualising data in lower dimensions, and identifying the most important features driving variability in the data .Thus, the algorithms used in assisted reproduction to predict categories using labelled data are of the supervised learning type. When encountering AI-based predictive models, clinicians and embryologists should be familiar with the machine learning lifecycle (Figure 2): Collect and pre-process data: Collect relevant data and carry out pre-processing (cleaning, normalising, transforming, etc.) to prepare the data for machine learning algorithms. Train a machine learning model: Train a machine learning model on the pre-processed data using a suitable algorithm and hyperparameters. Test and evaluate the model: Test the trained model on a separate test dataset and evaluate its performance using suitable evaluation metrics. Deploy the model: Deploy the trained model to a production environment, such as a web application or a mobile app. Monitor the model: Continuously monitor the performance of the deployed model and collect feedback from users. Refine and update the model: Refine and update the deployed model periodically using new data and feedback to improve its performance and adapt to changing requirements.Performance evaluation and model validationWhen discussing performance, the first step is to define what is being evaluated. If one encounters studies that claim remarkable results on the training dataset, it is advisable to exercise caution. Predicting data that are already in the system makes it easier for the computer to find a previous pattern in the known model, leading to the overfitting effect. It is entirely normal, and almost necessary, for the training set results to be particularly good, as they do not represent the actual predictive potential of the model.As showed in Figure 3, the process of developing a predictive model involves an initial partition of the test set, which is kept separate from the algorithm's training. Cross-validation is performed on the training set by separating a certain percentage and creating the model with the training set, then predicting the validation set. This process can be repeated several times to obtain cross-validation metrics. This prediction can already be considered representative of the model's predictive potential. Cross-validation can be performed through k-fold cross-validation (e.g., 80% of the dataset for training and 20% for validation) [18,28]; as well as training the model with the full dataset except for one specimen, predicting it, and repeating the process for all specimens in the dataset (leave-one-out cross-validation) .Finally, the test dataset is used to validate how the method (training set+validation set) predicts data that are not in the database. Therefore, it can be considered representative of the model's predictive potential .Performance metrics for machine learningDepending on the type of algorithm, different metrics should be chosen to evaluate its performance . For regression models, common metrics include mean squared error, mean absolute error, root mean squared error, and r2 . For classification models, common metrics are obtained from a confusion matrix, which unfortunately is not always provided in studies. Common metrics include accuracy, AUC and AUC precision (positive predictive value), recall (sensitivity), negative predictive value and specificity . The F1-score and Matthews correlation coefficient are also metrics to be considered, especially in imbalanced datasets . It is important to ensure that the positive reference is correctly identified in order to avoid confusion when evaluating model performance. For example, in a comparison of euploidy, it may seem obvious that the aneuploid group should be considered as the negative reference. However, the computer may mistakenly assign the aneuploid group as the positive reference if not explicitly specified, such as in cases where alphabetical ordering is used. Therefore, it is crucial to carefully define the positive and negative references before assessing a model’s performance.Conclusions: time to implement?Different authors have expressed their thoughts on whether or not to implement predictive AI models into the daily practice [59-61]. From my point of view, it is worth considering implementing an algorithm if its result is robust enough to answer the initial question of the requirement. For instance, if the objective was to improve the implantation rate, it is not as crucial whether the embryo selection model is based on viability, genetics, or a combination of both [36,40], nor is the specific value of AUC achieved particularly relevant. While a better AUC is theoretically associated with a better implantation outcome, this cut-off value would not be relevant if the implantation rate with the AI score is superior to that without AI. Nevertheless, external validation should be carried out to verify that the response to the requirement for integrating an AI system in the laboratory is truly satisfactory when applying AI compared to not applying AI. From there, it will be necessary to consider verifying the data either prospectively or in a multi-center setting.

PMC

Journal of Clinical and Translational Science

PMC10129750

10.1017/cts.2023.226

145 A CTS Team Approach to Adapting an Evidence-Based Mindfulness Tool to Increase Trust of Reproductive Healthcare Providers

Nesbit Tyler S., Ronke Coker Karen Awura Adjoa, McKune Sarah, Forthun Larry

OBJECTIVES/GOALS: The goals of this study are to 1) adapt a mindfulness-based intervention that supports the development of trust-promoting behaviors of OBGYN providers with patients who identify as Black women based on the input of providers and patients, and 2) assess the feasibility of implementation for OBGYN healthcare providers. METHODS/STUDY POPULATION: Goal 1: Focus groups will be conducted with members of the populations of providers and Black women patients in Alachua County, Florida to identify essential intervention content to complement the central component of mindfulness and spiritually based practices. This complementary content will serve to address the institutional and cultural context of the intervention setting. Goal 2: Providers will be recruited to participate in interviews about their perceptions of intervention feasibility. These aspects include recruitment potential, acceptability of the intervention content and delivery, implementation practicality, identification of appropriate outcomes, and identifying strategies to recruit Black women patients to participate in program evaluation. RESULTS/ANTICIPATED RESULTS: Goal 1: We will elicit the perspectives of providers and Black women patients regarding the respective roles and relationship of mindfulness and spirituality to increase trust-promoting behaviors with patients who are Black women. We also anticipate identifying additional content to complement the core intervention components that participants perceive as necessary to develop the knowledge, skills, and behaviors which convey the trustworthiness of providers to patients. Goal 2: We expect to gain key insights into intervention design, implementation, and evaluation feasibility from the perspective of providers. Interview data will be aggregated and qualitatively analyzed for themes pertaining to feasibility. DISCUSSION/SIGNIFICANCE: An intervention that builds on mindfulness and spiritual practice is an innovative approach to improving interpersonal outcomes in provider-patient relationships. By investigating the feasibility of such an intervention, we will gain insight into how to design and deliver a program to increase the trust-promoting behaviors of OBGYN providers.

PMC

Jornal Brasileiro de Pneumologia

PMC10171262

10.36416/1806-3756/e20230100

Legal action in sleep medicine: new alternatives need to be sought!

Fagondes Simone Chaves, John Angela Beatriz

In this issue of the Jornal Brasileiro de Pneumologia, the article by Pachito et al. 1 raises the discussion of an increasingly common approach in Brazil, as well as in other countries, which is taking legal action for access to medical procedures and treatments. 2 The evolution of knowledge in health care has introduced more sophisticated diagnostic methods and therapeutic options, and, consequently, costs have increased. However, many of these methods and treatments are not covered by the Sistema Único de Saúde (SUS, Brazilian Unified Health System) or private health insurance plans, which, based on the premise that health is a universal right, makes legal action an alternative, with all the complexity that this approach imposes.From the perspective of sleep medicine, there is a great lack of public services that offer specialized care in this area. A recent study has identified the presence of 36 specialized centers in Brazil, with a great asymmetry in terms of geographic distribution, and 44% of those are concentrated in the southeastern region of the country (personal information). Regarding diagnosis, sleep laboratory beds accredited to perform tests by the SUS are a minority, totaling only 28 centers throughout Brazil (personal information). On the other hand, the use of portable polysomnograms, which are less expensive and dispense with sleep laboratories, still requires improvements in both logistics and operationalization.In addition to diagnostic limitations, we have an even greater challenge when we address issues related to treatment. The main treatment for moderate and severe obstructive sleep apnea is the use of a device that generates CPAP in the upper airways. It is a high-cost piece of equipment that is included neither in the SUS nor in most private health insurance plans. In our daily practice at a public tertiary university hospital, we have observed actions that aim to fulfill this need at the municipal level; however, these actions are generally restricted to patients with more severe disease and are concentrated in larger cities, closer to capitals.The magnitude of the problem, therefore, is directly related to the prevailing socioeconomic reality in our country and the limitations arising from an area of medicine that is still being consolidated, especially in the public sphere, as well as to a highly prevalent medical condition (approximately 30% of the adult population), 3 whose consequences have been widely documented in the literature. 4 , 5 One of the concerns pointed out in the article by Pachito et al. 1 is the high economic costs that the growing practice of legal action in sleep medicine imposes. The study presents an additional cost estimate of 588% for diagnostic tests and of 21.7% for treatment with CPAP. These values are substantially higher when we compare the public health care system with the private health insurance plans.The theme takes on an even more relevant and worrying role considering that the number of lawsuits identified in the manuscript seems to be underestimated. The authors performed an analysis based on information extracted from the judicial system database over a period of five years and identified only 1,462 lawsuits, that is, approximately 292 cases/year. Considering the already mentioned high prevalence of obstructive sleep apnea in a country with an estimated adult population of 159.2 million individuals, 6 the number of patients who would potentially seek public health care assistance should be much higher. Another aspect that deserves attention is the decrease in the number of lawsuits between 2017 and 2019 reported in that study. 1 This finding differs from our experience in a public hospital. In recent years, with the deepening of the socioeconomic crisis in Brazil and the consequent decrease in income, making it difficult to maintain a private health insurance plan and to acquire a CPAP device, we have observed a substantial growth in the number of patients referred to our sleep outpatient clinic.As it was already pointed out by the authors, 1 the need for public policies that include the training of physicians to care for those patients, the dissemination of diagnostic methods, a detailed review of health care staff wages, and the establishment of partnerships is vital in order to improve the offer of CPAP treatment. It is also necessary that these patients have access to follow-up by a qualified medical team in the various regions of the country.Definitely, legal actions regarding sleep medicine are far from being a solution. They should be an exception, and it is urgent that we seek new alternatives!

PMC

Preventive Medicine Reports

PMC10518787

7-27-2023

10.1016/j.pmedr.2023.102345

Correspondence to Corrigendum

We thank Dr. Li and his team for noting the error in the abstract of our paper: Rosal M.C., Lemon S.C., Borg A., Lopez-Cepero A., Sreedhara M., Silfee V., Pbert L., Kane K., Li W. . The Healthy Kids & Families study: Outcomes of a 24-month childhood obesity prevention intervention. Prev. Med. Rep. Dec 7;31:102086. PMID: 36820371; PMCID: PMC9938323. A corrigendum with the corrected abstract has been published in this journal (Corrigendum to “The Healthy Kids & Families study: Outcomes of a 24-month childhood obesity prevention intervention” [Prev. Med. Rep. 31 102086] – ScienceDirect). We regret any inconvenience this may have caused.Funding/SupportThis publication is a product of a Health Promotion and Disease Prevention Research Center supported by Cooperative Agreement Number U48DP005031 from the Centers for Disease Control and Prevention.Declaration of Competing InterestThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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37724508

PMC10510351

9-19-2023

10.1177/10732748231202470

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37321570

PMC10839191

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