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.

Background/Aims: Treatment indications for patients with chronic hepatitis B (CHB) remain contentious, particularly for patients with mild alanine aminotransferase (ALT) elevation. We aimed to evaluate treatment effects in this patient population. Methods: This rollover study extended a placebo-controlled trial that enrolled non-cirrhotic patients with CHB and ALT levels below two times the upper limit of normal. Following 3 years of randomized intervention with either tenofovir disoproxil fumarate (TDF) or placebo, participants were rolled over to open-label TDF for 3 years. Liver biopsies were performed before and after the treatment to evaluate histopathological changes. Virological, biochemical, and serological outcomes were also assessed (NCT02463019). Results: Of 146 enrolled patients (median age 47 years, 80.8% male), 123 completed the study with paired biopsies. Overall, the Ishak fibrosis score decreased in 74 (60.2%), remained unchanged in 32 (26.0%), and increased in 17 (13.8%) patients (p<0.0001). The Knodell necroinflammation score decreased in 58 (47.2%), remained unchanged in 29 (23.6%), and increased in 36 (29.3%) patients (p=0.0038). The proportion of patients with an Ishak score ≥ 3 significantly decreased from 26.8% (n=33) to 9.8% (n=12) (p=0.0002). Histological improvements were more pronounced in patients switching from placebo. Virological and biochemical outcomes also improved in placebo switchers and remained stable in patients who continued TDF. However, serum HBsAg levels did not change and no patient cleared HBsAg. Conclusions In CHB patients with minimally raised ALT, favorable histopathological, biochemical, and virological outcomes were observed following 3-year TDF treatment, for both treatment-naïve patients and those already on therapy.

Background/Aims: Treatment indications for patients with chronic hepatitis B (CHB) remain contentious, particularly for patients with mild alanine aminotransferase (ALT) elevation. We aimed to evaluate treatment effects in this patient population. Methods: This rollover study extended a placebo-controlled trial that enrolled non-cirrhotic patients with CHB and ALT levels below two times the upper limit of normal. Following 3 years of randomized intervention with either tenofovir disoproxil fumarate (TDF) or placebo, participants were rolled over to open-label TDF for 3 years. Liver biopsies were performed before and after the treatment to evaluate histopathological changes. Virological, biochemical, and serological outcomes were also assessed (NCT02463019). Results: Of 146 enrolled patients (median age 47 years, 80.8% male), 123 completed the study with paired biopsies. Overall, the Ishak fibrosis score decreased in 74 (60.2%), remained unchanged in 32 (26.0%), and increased in 17 (13.8%) patients (p<0.0001). The Knodell necroinflammation score decreased in 58 (47.2%), remained unchanged in 29 (23.6%), and increased in 36 (29.3%) patients (p=0.0038). The proportion of patients with an Ishak score ≥ 3 significantly decreased from 26.8% (n=33) to 9.8% (n=12) (p=0.0002). Histological improvements were more pronounced in patients switching from placebo. Virological and biochemical outcomes also improved in placebo switchers and remained stable in patients who continued TDF. However, serum HBsAg levels did not change and no patient cleared HBsAg. Conclusions In CHB patients with minimally raised ALT, favorable histopathological, biochemical, and virological outcomes were observed following 3-year TDF treatment, for both treatment-naïve patients and those already on therapy.

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.

Background/Aims: Treatment indications for patients with chronic hepatitis B (CHB) remain contentious, particularly for patients with mild alanine aminotransferase (ALT) elevation. We aimed to evaluate treatment effects in this patient population. Methods: This rollover study extended a placebo-controlled trial that enrolled non-cirrhotic patients with CHB and ALT levels below two times the upper limit of normal. Following 3 years of randomized intervention with either tenofovir disoproxil fumarate (TDF) or placebo, participants were rolled over to open-label TDF for 3 years. Liver biopsies were performed before and after the treatment to evaluate histopathological changes. Virological, biochemical, and serological outcomes were also assessed (NCT02463019). Results: Of 146 enrolled patients (median age 47 years, 80.8% male), 123 completed the study with paired biopsies. Overall, the Ishak fibrosis score decreased in 74 (60.2%), remained unchanged in 32 (26.0%), and increased in 17 (13.8%) patients (p<0.0001). The Knodell necroinflammation score decreased in 58 (47.2%), remained unchanged in 29 (23.6%), and increased in 36 (29.3%) patients (p=0.0038). The proportion of patients with an Ishak score ≥ 3 significantly decreased from 26.8% (n=33) to 9.8% (n=12) (p=0.0002). Histological improvements were more pronounced in patients switching from placebo. Virological and biochemical outcomes also improved in placebo switchers and remained stable in patients who continued TDF. However, serum HBsAg levels did not change and no patient cleared HBsAg. Conclusions In CHB patients with minimally raised ALT, favorable histopathological, biochemical, and virological outcomes were observed following 3-year TDF treatment, for both treatment-naïve patients and those already on therapy.

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 epidemiological characteristics of cross-county imported dengue fever cases within Yunnan province in 2023, so as to provide insights into formulation of preventive and control measures for intra-provincial spread of dengue fever. Methods All data pertaining cross-county imported dengue fever cases within Yunnan Province in 2023 were collected, and the temporal, regional and population distributions of the cases were descriptively analyzed. Results A total of 1 664 intra-provincial cross-county imported dengue fever cases were reported in 95 counties (cities, districts) cross 16 profectures (cities) in Yunnan Province in 2023, accounting for 12.34% of total cases in the province. Cross-county imported dengue fever cases were predominantly reported during the period between August and October (1 516 cases, 91.11% of total cases), and peaked in September (659 cases), with a single-day peak on October 8 (36 cases). During the period from September 4 to 10, five counties (cities) with local dengue fever epidemics, including Jinghong City of Xishuangbanna Dai Autonomous Prefecture, Gengma Dai and Wa Autonomous County of Lincang City, Ruili City of Dehong Dai and Jingpo Autonomous Prefecture, Mengla Coun ty of Xishuangbanna Dai Autonomous Prefecture, and Zhenkang County of Lincang City, exported 165 cross-county imported dengue fever cases to the rest of the province. Among the 1 644 intra-provincial cross-county imported dengue fever cases, the male to female ratio was 1.40∶1.00, and 1 329 cases were at ages of 15 to 55 years (79.87%), with farmers as the predominant occupation (886 cases, 53.25%). The top 5 counties (cities/districts) reporting the highest number of intra-provincial cross-county imported dengue fever cases included Simao District (266 cases) and Lancang Lahu Autonomous County (118 cases) of Pu’er City, Mengla County (91 cases) and Menghai County (91 cases) of Xishuangbanna Dai Autonomous Prefecture, and Mangshi City (73 cases) of Dehong Dai and Jingpo Autonomous Prefecture, which accounting for 38.40% of total imported cases. These intra-provincial cross-county imported dengue fever cases originated from 7 counties (cities/districts) in 4 prefectures (cities), including 1 261 cases (76.70%) from Jinghong City of Xishuangbanna Dai Autonomous Prefecture, 224 cases (13.63%) from Ruili City of Dehong Dai and Jingpo Autonomous Prefecture, 103 cases (6.27%) from Gengma Dai and Wa Autonomous County of Lincang City, 31 cases (1.89%) from Mengla County of Xishuangbanna Dai Autonomous Prefecture, 30 cases (1.82%) from Zhenkang County of Lincang City, 10 cases (0.61%) from Cangyuan Wa Autonomous County of Lincang City, and 5 cases (0.30%) from Mohan-Boten Economic Cooperation Zone of Kunming City. In addition, local dengue fever epidemics following intra-provincial cross-county importation of dengue fevers cases in Simao District, Jinggu Dai and Yi Autonomous County, Mangshi City, Longchuan County, and Cangyuan Wa Autonomous County. Conclusions Farmers and students are high-risk populations for intra-provincial cross-county imported dengue fever cases in Yunnan Province, and health education pertaining personal protection against dengue fever should be strengthened among these high-risk populations by governments at all levels. There is a high risk of local out-break of dengue fever following continuous introduction of intra-provincial cross-county imported cases. Standardized management of intra-provincial cross-county imported dengue fever cases should be reinforced to reduce the risk of local epidemics.