OBJECTIVE To establish management index system for rational drug use of key monitoring drugs， and provide reference for the management of key monitoring drugs in the hospitals. METHODS First， the management index system for rational drug use of key monitoring drugs was drafted by collecting the evidence from related medical literature. Next， using a modified Delphi method， twenty experienced experts from the fields of pharmacy， medical practice， healthcare insurance， and finance were selected to participate in two rounds of questionnaire consultations. Based on the expert enthusiasm coefficient， authority coefficient， degree of opinion concentration， and degree of coordination， the final indicators were determined to establish a management index system for rational drug use of key monitored drugs in medical institutions. RESULTS The expert enthusiasm coefficients reached 100% in both rounds of consultation. In first-level， second-level and third-level indicators， the authority coefficients of experts were 0.89， 0.86 and 0.87， and coordination coefficients of the experts in importance score were 0.300 （P＜ 0.05）， 0.125 （P＜0.05） and 0.139 （P＜0.05）， respectively. The average score for the importance of all indicators reached over 3.5， in which the full score ratio ranged from 35% to 100%. Except that the variation coefficient of a third-level indicator “number of specifications purchased for key monitored drugs” was 0.26， the variation coefficients of rest indicators were less than or equal to 0.25. Based on the results of expert consultation， final version of the management index system established in this study， including two first-level indicators （drug procurement and use， and rational drug use）， five second-level indicators （such as the accessibility， cost-effectiveness） and twenty third-level indicators （such as the number of specifications purchased for key monitored drugs， the increase in the cost of key monitored drugs）. CONCLUSIONS The management index system established in this study possesses high reliability and strong operability， and may provide a reference for the management of key monitoring drugs in the hospitals.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: To analyze the distribution characteristics of fluoride and trichloromethane in drinking water in rural schools in Guizhou Province and assess their health risks, so as to provide a scientific basis for ensuring the safety of drinking water in rural schools. Methods: During the dry season (March to May) and wet season (July to September) of 2020 to 2022, 788 rural primary and secondary schools in agricultural counties (districts) in Guizhou Province were selected for investigation by using a direct sampling method. A total of 1 566 drinking water samples were collected from these schools, and the mass concentrations of fluoride and trichloromethane in the water samples were detected. The Mann-Whitney U test was used for intergroup comparison, and a health risk assessment model was employed to evaluate the health risks of students oral intake of fluoride and trichloromethane. Results: From 2020 to 2022, the mass concentrations of fluoride and trichloromethane in the drinking water of rural schools in Guizhou Province all met the standards, and the ranges were no detection to 0.99 mg/L and (no detection to 0.06)×10 -3 mg/L, respectively. The mass concentrations of fluoride in dry and wet seasons were 0.05(0.05,0.10), 0.05(0.05,0.10) mg/L, the mass concentrations of trichloromethane were [0.02(0.02,1.00)]×10 -3 , [0.02(0.02,1.00)]×10 -3 mg/L, the mass concentrations of fluoride in factory water and terminal water were 0.05(0.05,0.05), 0.05(0.05,0.10) mg/L, and the differences were not statistically significant ( Z=-0.04, -0.88, - 0.98 , P >0.05). There was a statistically significant difference in the mass concentration of trichloromethane between factory water and peripheral water [0.02(0.02,0.02)×10 -3 , 0.02(0.02,1.05)×10 -3 mg/L]( Z=-2.16, P < 0.05 ). The non-carcinogenic risk assessment values for students oral exposure to fluoride and trichloromethane were in the range of 0.01(0.01,0.03)-0.03(0.03,0.06) and [0.26( 0.26 ,14.54)]×10 -4 -[0.52(0.52,48.62)]×10 -4 , respectively, all of which were at acceptable levels; the carcinogenic risk assessment values for oral exposure to trichloromethane were in the range of [0.08(0.08, 4.51 )]×10 -7 -[0.16(0.16,15.07)]×10 -7 , indicating a low risk. Conclusions The health risks of students expore to fluoride and trichloromethane in drinking water in rural schools of Guizhou Province are low. It is necessary to strengthen the standardized management of disinfection in some rural drinking water projects and the monitoring of fluoride in water sources to reduce the exposure risk to children.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Aim This study aims to investigate the therapeutic effect and underlying mechanism of Todda-lia asiatica in the treatment of ischemic stroke(IS),utilizing network pharmacology,molecular docking technology,and animal experiments.Methods To screen the chemical components of Toddalia asiatica and its targets related to IS,a database was utilized.A protein-protein interaction(PPI)network was con-structed,followed by KEGG pathway enrichment anal-ysis.Molecular docking was performed to investigate the interaction between the components and target pro-teins.Finally,the effects of the drug on the PI3K/AKT/mTOR pathway and autophagy were validated through animal experiments.We established a middle cerebral artery occlusion(MCAO)rat model and di-vided the rats into the model group,Donepezil hydro-chloride group,Toddalia asiatica group,and sham op-eration group randomly.Observed the pathological changes in neurons of the rat hippocampal and cortical regions induced by the drug,performed immunohisto-chemical analysis to detect and localize mTOR expres-sion,and used Western blot to assess the expression levels of PI3K,p-PI3K,AKT,p-AKT,mTOR,as well as autophagy markers(LC3-Ⅱ and p62).Re-sults A total of 22 active ingredients from Toddalia asiatica,including AKT1 and MAPK3,were identified through screening.Additionally,194 signaling path-ways,such as PI3K/AKT and MAPK,were analyzed.The active compounds in Toddalia asiatica demonstra-ted stable binding affinity with targets associated with ischemic stroke.The results of the animal experiment indicated that,compared to the sham-operated group,the neuronal distribution in the hippocampal and corti-cal regions of the model group rats became sparser and more disorganized.There was a decrease in the number of Nissl bodies and cytoplasmic vacuolization.The ex-pression of mTOR-positive cells in the hippocampal and cortical regions was reduced.Additionally,the ex-pression levels of p-PI3K,p-AKT,mTOR,and p62 in the rat hippocampal tissue decreased(P＜0.05,P＜0.01),while the expression of LC3-Ⅱ increased(P＜0.01).Compared with the model group,the rats in the Toddalia asiatica and the Donepezil hydrochloride groups effectively improved the aforementioned indica-tors in rats.Conclusions Network pharmacology a-nalysis has revealed the promising potential of Toddalia asiatica in treating ischemic stroke,attributed to its di-verse components,targets,and pathways.The animal experiment showed that Toddalia asiatica can protect the neuronal structure in the hippocampal and cortical regions,which may be related to the inhibition of ex-cessive autophagy mediated by the PI3 K/AKT/mTOR pathway.

Aim To investigate the regulatory mecha-nism of arrhythmia of sodium channel ubiquitination af-ter MI and to study the electrophysiological remodeling mechanism of lncRNA-CCRR after MI for the preven-tion and treatment of arrhythmia after MI.Methods LncRNA-CCRR transgenic mice and C57BL/6 mice injected with lncRNA-CCRR overexpressed adeno-asso-ciated virus were used.Four weeks after infection,the left anterior descending branch of the coronary artery was ligated for 12 h to establish a mouse acute myocar-dial infarction model,and the incidence of arrhythmia was detected by programmed electrical stimulation.Ln-cRNA-CCRR overexpression/knockdown adeno-associ-ated virus and negative control were transfected into neonatal mouse cardiomyocytes(NMCMs),and the model was prepared by hypoxia for 12 h.LncRNA-CCRR expression was detected by FISH,Nav1.5 and UBA6 protein and Nav.1.5 mRNA expression were de-tected by Western blot and real-time quantitative poly-merase chain reaction(qRT-PCR),Nav1.5 and UBA6 expressions were detected by immunofluores-cence,and the relationship between lncRNA-CCRR and UBA6 was detected by RIP.INa current density af-ter CCRR overexpression and knockdown was detected by Whole-cell clamp patch.Results In MI mice,the expression of lncRNA-CCRR decreased,the incidence of arrhythmia increased,the expression of CCRR and Nav1.5 mRNA was down-regulated,the protein ex-pression of Nav1.5 was down-regulated,and the pro-tein expression of UBA6 was up-regulated compared with sham group.Overexpression of CCRR could re-verse the above changes.AAV-CCRR could reverse the down-regulated CCRR and Nav1.5 mRNA levels af-ter hypoxia,and improve the expression of Nav1.5 and UBA6 protein.The direct relationship between ln-cRNA-CCRR and UBA6 was identified by RIP analy-sis.The INa density increased after transfection with AAV-CCRR.The INa density decreased after transfec-tion with AAV-si-CCRR.Conclusions The expres-sion of lncRNA-CCRR decreases after MI,and ln-cRNA-CCRR can improve arrhythmia induced by MI by inhibiting UBA6 to increase the protein expression level of Nav1.5 and the density of INa.

Objective:To investigate the expression and mechanism of microRNA-98-5p(miR-98-5p)and DNA methyltransferase 3A(DNMT3A)in patients with ulcerative colitis(UC)and animal model of ulcerative colitis.Methods:One-hundred UC patients in our hospital from January 2021 to December 2022 were collected as the observation group,another 100 healthy subjects were collected as the control group,serum miR-98-5p,DNMT3A mRNA and protein ex-pression were detected,the relationship between miR-98-5p,DNMT3A mRNA and clinical pathological characteristics of UC was analyzed;the UC rat model was established and randomly grouped into a blank group(CT group),a model group(UC group),a miR-98-5p control group(antagomiR-NC group),and a miR-98-5p inhibitor group(antagomiR-98-5p group),ELISA was applied to measure the levels of serum interleukin-1(IL-1),IL-6,and tumor necrosis factor(TNF),HE staining was applied to observe the histopathology changes of the colon,RT-qPCR and Western blot were applied to detect miR-98-5p,DNMT3A mRNA and protein expression levels in colon tissue.Results:Compared with the control group,the serum miR-98-5p expression of UC patients was increased,DNMT3A mRNA and DNMT3A were decreased,the expression of miR-98-5p and DNMT3A mRNA were associated with disease grading,mucopurulent bloody stools,mucosal acute and chronic inflammation,and atypical hyperplasia(P＜0.05);compared with the CT group,the colon mucosal layer of rats in the UC group showed defects,and obvious infil-tration of inflammatory cells,serum IL-1,IL-6,TNF and colon miR-98-5p expression were in-creased,the DNMT3A mRNA and protein expression were decreased(P＜0.05);compared with the antagomiR-NC group,the mucosal layer defect and inflammatory cell infiltration in the an-tagomiR-98-5p group were reduced,and the structure was clearer,serum IL-1,IL-6,TNF,and colon miR-98-5p expression were decreased,DNMT3A mRNA and protein expression were in-creased(P＜0.05);miR-98-5p had a targeted binding site with DNMT3A.Conclusion:Serum miR-98-5p expression of UC patients and the colon tissue of model rats was increased,while DNMT3A expression was decreased.Down-regulating miR-98-5p expression can promote the expression of DNMT3A and improve UC symptoms.