N-acetyl-p-aminophenol （APAP） is an antipyretic analgesic commonly used in clinical practice， and APAP overdose can cause severe liver injury and even death. In recent years， the incidence rate of APAP-induced liver injury （AILI） tends to increase， and it has become the second most common cause of liver transplantation worldwide. Autophagy is a highly conserved catabolic process that removes unwanted cytosolic proteins and organelles through lysosomal degradation to achieve the metabolic needs of cells themselves and the renewal of organelles. A large number of studies have shown that autophagy plays a key role in the pathophysiology of AILI， involving the mechanisms such as APAP protein conjugates， oxidative stress， JNK activation， mitochondrial dysfunction， inflammatory response and apoptosis. This article elaborates on the biological mechanism of autophagy in AILI， in order to provide a theoretical basis for the treatment of AILI and the development of autophagy regulators.

With the rapid development of sequencing technologies, the detection of alternative polyadenylation (APA) in mammals has become more precise. APA precisely regulates gene expression by altering the length and position of the poly(A) tail, and is involved in various biological processes such as disease occurrence and embryonic development. The research on APA in mammals mainly focuses on the following aspects:(1) identifying APA based on transcriptome data and elucidating their characteristics; (2) investigating the relationship between APA and gene expression regulation to reveal its important role in life regulation;(3) exploring the intrinsic connections between APA and disease occurrence, embryonic development, differentiation, and other life processes to provide new perspectives and methods for disease diagnosis and treatment, as well as uncovering embryonic development regulatory mechanisms. In this review, the classification, mechanisms and functions of APA were elaborated in detail and the methods for APA identifying and APA data resources based on various transcriptome data were systematically summarized. Moreover, we epitomized and provided an outlook on research on APA, emphasizing the role of sequencing technologies in driving studies on APA in mammals. In the future, with the further development of sequencing technology, the regulatory mechanisms of APA in mammals will become clearer.

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.

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 To investigate the therapeutic effect of the MW-9 on ulcerative colitis(UC)and reveal the underlying mechanism, so as to provide a scientific guidance for the MW-9 treatment of UC. Methods The model of lipopolysaccharide(LPS)-stimulated RAW264.7 macrophage cells was established. The effect of MW-9 on RAW264.7 cells viability was detected by MTT assay. The levels of nitric oxide(NO)in RAW264.7 macrophages were measured by Griess assay. Cell supernatants and serum levels of inflammatory cytokines containing IL-6, TNF-α and IL-1β were determined by ELISA kits. Dextran sulfate sodium(DSS)-induced UC model in mice was established and body weight of mice in each group was measured. The histopathological damage degree of colonic tissue was assessed by HE staining. The protein expression of p-p38, p-ERK1/2 and p-JNK was detected by Western blot. Results MW-9 intervention significantly inhibited NO release in RAW264.7 macrophages with IC50 of 20.47 mg·L-1 and decreased the overproduction of inflammatory factors IL-6, IL-1β and TNF-α(P<0.05). MW-9 had no cytotoxicity at the concentrations below 6 mg·L-1. After MW-9 treatment, mouse body weight was gradually reduced, and the serum IL-6, IL-1β and TNF-α levels were significantly down-regulated. Compared with the model group, MW-9 significantly decreased the expression of p-p38 and p-ERK1/2 protein. Conclusions MW-9 has significant anti-inflammatory activities both in vitro and in vivo, and its underlying mechanism for the treatment of UC may be associated with the inhibition of MAPK signaling pathway.

Objective To analyze the safety of idarubicin or daunorubicin combined with cytarabine in the treatment of patients with acute myeloid leukemia.Methods The Chinese Biomedical Database,Chinese Journal Full-text Database,Chinese Science and Technology Journal Full-text Database,Wanfang Database,Embase,PubMed,Cochrane Library were searched.The randomized controlled trials(RCTs)of idarubicin combined with cytarabine(treatment group)and daunorubicin combined with cytarabine(control group)were collected.The search time was from 2014-01-01 to 2024-02-23.RevMan 5.4 software was used to perform meta-analysis,sensitivity analysis and publication bias analysis on adverse drug reactions of the included studies.Results A total of 11 RCTs involving 1 818 patients were included in the Meta-analysis.The treatment and control groups were enrolled 912 and 906 cases,respectively.The results of meta-analysis showed that the total incidence of adverse drug reactions in the treatment group and the control group was 22.9％(56 cases/245 cases)and 42.9％(105 cases/245 cases),respectively,and the difference was statistically significant[relative risk(RR)=0.53,95％confidence interval(CI)=0.41-0.69,P＜0.001)].In the specific adverse drug reactions,there were no statistically significant differences in the incidence of hematological toxicity,digestive system toxicity,cardiac toxicity,liver and kidney toxicity,infection,bleeding between the two groups(all P＞0.05).Sensitivity analysis showed that the results were stable and reliable.The results of publication bias analysis showed that this study was less likely to have publication bias.Conclusion The incidence of adverse drug reactions of idarubicin combined with cytarabine in the treatment of patients with acute myeloid leukemia is significantly lower than that of daunorubicin combined with cytarabine,so the former has better safety.

Objective To compare the efficacy and safety of Saccharomyces boulardii and other probiotics in the treatment of pediatric diarrhea.Methods Retrieved from PubMed,Embase,CBM,Wanfang data,CNKI and VIP,randomized controlled trial(RCTs)about Saccharomyces boulardii(treatment group)vs other probiotics(control group)were collected.After screening the literature,extracting data and evaluating the quality,Meta-analysis was performed by using RevMan 5.3 and Stata 17.0 software.Results A total of 30 RCTs were included,involving 3 082 children.Results of Meta-analysis showed there was no statistical significance in the duration of diarrhea[mean difference(MD)=-0.65,95％confidence interval(CI)=-1.44-0.14,P＞0.05]or the total incidence of adverse reactions[odds ratio(OR)=0.85,95％CI=0.44-1.62,P＞0.05].The total effective rate(OR=1.60,95％CI=1.08-2.36,P＜0.05)and the number of stools(MD=-0.66,95％CI=-1.00--0.32,P＜0.01)in the treatment group were significantly better than control group.There was statistically significant difference in the duration of diarrhea between the two groups treated with diarrhea with fever(MD=1.81,95％CI=1.12-2.49,P＜0.01)and rotavirus gastroenteritis(MD=-0.92,95％CI=-1.20--0.64,P＜0.01).In the treatment of diarrhea with fever,the duration of diarrhea in the control group was significantly shorter than that in the treatment group.The duration of diarrhea in the treatment group was significantly shorter than that control group.At the same time,the duration of diarrhea in children with diarrhea treated with treatment group was significantly shorter than that of control group Bifidobacterium tetragenous viable(MD=-0.84,95％CI=-1.09--0.58,P＜0.01)and control group combined Bacillus subtilis and Enterococcus faecium enteric-coated(MD=-1.35,95％CI=-2.30-0.39,P＜0.01).Conclusion The safety of Saccharomyces boulardii are similar to other probiotics.The efficacy and the number of stools with Saccharomyces boulardii are significantly better than other probiotics in the treatment of diarrhea.The duration of diarrhea in Saccharomyces boulardii group was significantly shorter than other probiotics,while in the treatment of antibiotic-associated diarrhea,the duration of diarrhea of Saccharomyces boulardii was the same as that of other probiotics.However,the duration of diarrhea in children with diarrhea treated with Saccharomyces boulardii was significantly longer than other probiotics.