Hepatic encephalopathy is a difficult and critical disease with rapid progression and limited treatment methods in the field of liver disease， and it is urgently needed to make breakthroughs in its pathogenesis. Selection of appropriate prevention and treatment strategies is of great importance in delaying disease progression and reducing the incidence and mortality rates. This article reviews the theory of “turbid toxin pathogenesis” and related prevention and treatment strategies for hepatic encephalopathy in traditional Chinese medicine/Zhuang medicine， proposes a new theory of “turbid toxin pathogenesis”， analyzes the scientific connotations of “turbid”， “toxin”， and the theory of “turbid toxin pathogenesis”， and constructs the “four-step” prevention and treatment strategies for hepatic encephalopathy， thereby establishing the new clinical prevention and treatment regimen for hepatic encephalopathy represented by “four prescriptions and two techniques” and clarifying the effect mechanism and biological basis of core prescriptions and techniques in the prevention and treatment of hepatic encephalopathy， in order to provide a reference for the prevention and treatment of hepatic encephalopathy.

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.

ObjectiveTo investigate the role of 4-week high-intensity interval training (HIIT) in modulating the metabolic homeostasis of the pyruvate-lactate axis in the hippocampus of rats with chronic unpredictable mild stress (CUMS) to improve their depressive-like behavior. MethodsForty-eight SPF-grade 8-week-old male SD rats were randomly divided into 4 groups: the normal quiet group (C), the CUMS quiet group (M), the normal exercise group (HC), and the CUMS exercise group (HM). The M and HM groups received 8 weeks of CUMS modeling, while the HC and HM groups were exposed to 4 weeks of HIIT starting from the 5th week (3 min (85%-90%) S_max+1 min (50%-55%) S_max, 3-5 cycles, S_max is the maximum movement speed). A lactate analyzer was used to detect the blood lactate concentration in the quiet state of rats in the HC and HM groups at week 4 and in the 0, 2, 4, 8, 12, and 24 h after exercise, as well as in the quiet state of rats in each group at week 8. Behavioral indexes such as sucrose preference rate, number of times of uprightness and number of traversing frames in the absenteeism experiment, and other behavioral indexes were used to assess the depressive-like behavior of the rats at week 4 and week 8. The rats were anesthetized on the next day after the behavioral test in week 8, and hippocampal tissues were taken for assay. LC-MS non-targeted metabolomics, target quantification, ELISA and Western blot were used to detect the changes in metabolite content, lactate and pyruvate concentration, the content of key metabolic enzymes in the pyruvate-lactate axis, and the protein expression levels of monocarboxylate transporters (MCTs). Results4-week HIIT intervention significantly increased the sucrose preference rate, the number of uprights and the number of traversed frames in the absent field experiment in CUMS rats; non-targeted metabolomics assay found that 21 metabolites were significantly changed in group M compared to group C, and 14 and 11 differential metabolites were significantly dialed back in the HC and HM groups, respectively, after the 4-week HIIT intervention; the quantitative results of the targeting showed that, compared to group C, lactate concentration in the hippocampal tissues of M group, compared with group C, lactate concentration in hippocampal tissue was significantly reduced and pyruvate concentration was significantly increased, and 4-week HIIT intervention significantly increased the concentration of lactate and pyruvate in hippocampal tissue of HM group; the trend of changes in blood lactate concentration was consistent with the change in lactate concentration in hippocampal tissue; compared with group C, the LDHB content of group M was significantly increased, the content of PKM2 and PDH, as well as the protein expression level of MCT2 and MCT4 were significantly reduced. The 4-week HIIT intervention upregulated the PKM2 and PDH content as well as the protein expression levels of MCT2 and MCT4 in the HM group. ConclusionThe 4-week HIIT intervention upregulated blood lactate concentration and PKM2 and PDH metabolizing enzymes in hippocampal tissues of CUMS rats, and upregulated the expression of MCT2 and MCT4 transport carrier proteins to promote central lactate uptake and utilization, which regulated metabolic homeostasis of the pyruvate-lactate axis and improved depressive-like behaviors.

Objective: To investigate the therapeutic effect of patchouli alcohol on mice with lung-heat syndrome based on the phosphoinositide 3-kinase(PI3K)/protein kinase B(Akt)/nuclear factor-kappa B(NF-κB) signaling pathway. Methods: First, network pharmacology was used to predict the potential targets of patchouli alcohol in the treatment of lung-heat syndrome, and a "component-disease-key target" network was constructed for pathway analysis. Then, 40 BALB/c mice were assigned to the normal, lung-heat model, honeysuckle, and low-dose and high-dose patchouli alcohol groups. All groups, except the blank group, were intranasally infected with 50 μL (103 TCID50) of influenza virus solution. After two hours of infection, mice were treated once a day for seven consecutive days. The therapeutic mechanism of patchouli alcohol was explored by measuring pulmonary inflammatory factors, the PI3K/Akt/NF-κB pathway, hypothalamic fever markers (PGE2, cAMP, cGMP levels), rectal temperature, and tissue energy metabolism. Results: Network pharmacology identified 135 target genes related to patchouli alcohol and lung-heat syndrome, with the key targets being STAT3, H1F1A, and NF-κB1. In animal experiments, patchouli alcohol significantly alleviated influenza virus-induced lung inflammatory damage in mice with lung-heat syndrome, inhibited the expression of TNF-α and IL-6 in lung tissues(P<0.01), and suppressed the activation of the PI3K/Akt/NF-κB pathway. It also reduced hypothalamic levels of PGE2 and cAMP(P<0.01), suppressed the increase in rectal temperature, significantly decreased liver glycogen and pyruvate levels(P<0.01), and increased the activities of SDH, LDH, and Na+ -K+ -ATPase in the liver(P<0.01) Conclusion Patchouli alcohol improves the symptoms of lung-heat syndrome in mice by inhibiting the activation of the PI3K/Akt/NF-κB pathway, reducing proinflammatory cytokines and inflammatory damage, and regulating hypothalamic fever markers and energy metabolism.

Objective: To investigate the biological effects of carvacrol on rats with stomach-heat and stomach-cold and its regulation on transient receptor potential(TRP) channels in rats with stomach-heat, and to study the cold and heat properties of carvacrol and its possible mechanism. Methods: According to the random number method, 100 SD rats were divided into stomach-heat blank group, stomach-heat model group, Coptidis Rhizoma group, stomach-heat low-dose and high-dose carvacrol group, stomach-cold blank group, stomach-cold model group, Baked ginger group, stomach-cold low-dose group and high-dose carvacrol group, 10 rats in each group. The rat model of stomach-heat was established by intragastric administration of pepper aqueous solution (0.80 g/kg) and anhydrous ethanol, and the rat model of stomach-cold was established by intragastric administration of water extract of Anemarrhena asphodeloides and sodium hydroxide (10.40 g/kg). On the day of modeling, the rats in the Baked ginger group were given Baked ginger decoction (0.78 g/kg), and the rats in the Coptidis Rhizoma group were given Coptidis Rhizoma decoction (0.43 g/kg).The stomach-cold and stomach-heat low-dose group of carvacrol was given carvacrol emulsion (40 mg/kg), high-dose group was given carvacrol emulsion (80 mg/kg).All rats of the blank and model groups were given the equal volume of emulsion prepared by 5% dimethyl sulfoxide, 1% Tween 80, 1% polyethylene glycol 400, and 93% normal saline, once a day, for 7 days. The general condition of rats was observed and the body mass was recorded. The pathological morphology of gastric tissue was observed by hematoxylin-eosin staining. The changes of material and energy metabolism, cyclic nucleotide (cAMP), thyroid hormone and gastrointestinal hormone in each group were determined by enzyme-linked immunosorbent assay. The expression levels of transient receptor potential vanilloid subtype 1 (TRPV1), transient receptor potential channel M8 (TRPM8) and uncoupling protein-1 (UCP1) in rats with gastric fever were detected by Western blotting. Results: Compared with the stomach-heat blank group, the body mass of rats in the stomach-heat model group decreased at the fifth and seventh day (P<0.05). The contents (or ratio) of hepatic glycogen (HGlyc), total cholesterol (TC), triglyceride (TG), and vasoactive intestinal peptide (VIP) were decreased (P<0.05), and Na+ -K+ -ATPase, Ca2+ -Mg2+ -ATPase, cytochrome C oxidase (COX), NADH dehydrogenase (ND), cyclic adenosine phosphate (cAMP), cAMP/cyclic guanosine phosphate (cGMP), triiodothyronine (T3), thyroxine (T4), gastrin (GAS), motilin (MTL), and α-amylase (α-AMS) all increased (P<0.05). Compared with the stomach-heat model group, the body mass of rats in the Coptidis Rhizoma group decreased at the third, fifth, and seventh day, the contents (or ratio) of HGlyc, TC, TG, VIP and α-AMS were increased, and Na+ -K+ -ATPase, COX, ND, cAMP, cAMP/cGMP, T3, T4, and GAS all decreased (P<0.05). The body mass of rats in the stomach-heat low-dose carvacrol group decreased at the seventh day. The contents (or ratio) of HGlyc, TC, and VIP were increased, Na+ -K+ -ATPase, COX, ND, cAMP, cAMP/cGMP, T3, T4, and MTL all decreased, the expression of TRPV1 and UCP1 in gastric tissue decreased, while TRPM8 increased (P<0.05) in rats of the stomach-heat low-dose and high-dose carvacrol groups. Compared with the stomach-cold blank group, the body mass of rats in the stomach-cold model group decreased at the third, fifth, and seventh day, the contents (or ratio) of HGlyc, TC, TG, α-AMS, and VIP all increased, while Na+ -K+ -ATPase, Ca2+ -Mg2+ -ATPase, COX, ND, cAMP, cAMP/cGMP, T3, T4, GAS, and MTL all decreased (P<0.05). Compared with the stomach-cold model group, the body mass of rats in the Baked ginger group was increased at the seventh day, and the contents (or ratio) of HGlyc, VIP, and α-AMS all decreased, while Na+ -K+ -ATPase, COX, ND, cAMP/cGMP, T3, T4, GAS, and MTL all increased (P<0.05). The contents of HGlyc, cAMP, α-AMS, and VIP of rats in the stomach-cold low and high-dose carvacrol group all decreased (P<0.05). TG in the stomach-cold low-dose carvacrol group was increased. TC, Ca2+ -Mg2+ -ATPase, and cGMP all increased, while cAMP/cGMP decreased (P<0.05) in the high-dose carvacrol group. Conclusion In this study, the rat model of stomach-cold and stomach-heat were successfully established by using cold and heat factors. The result showed that carvacrol had a certain inhibitory effect on body mass, material energy metabolism, cyclic nucleotide level, thyroid hormone and gastrointestinal function in rats with stomach-heat, indicating that the drug was cold. Carvacrol′s cold medicinal property could be biologically explained by TRPV1 activation, UCP1 induction, and TRPM8 suppression.