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.

ObjectiveThis study aimed to investigate the effects of 4-week high-intensity interval training (HIIT) on synaptic plasticity in the prefrontal cortex (PFC) of rats exposed to chronic unpredictable mild stress (CUMS), and to explore its potential mechanisms. MethodsA total of 48 male Sprague-Dawley rats were randomly divided into 4 groups: control (C), model (M), control plus HIIT (HC), and model plus HIIT (HM). Rats in groups M and HM underwent 8 weeks of CUMS to establish depression-like behaviors, while groups HC and HM received HIIT intervention beginning from the 5th week for 4 consecutive weeks. The HIIT protocol consisted of repeated intervals of 3 min at high speed (85%-90% maximal training speed, S_max) alternated with one minute at low speed (50%-55% S_max), with 3 to 5 sets per session, conducted 5 d per week. Behavioral assessments and tail-vein blood lactate levels were measured at the end of the 4th and 8th weeks. After the intervention, rat PFC tissues were collected for Golgi staining to analyze synaptic morphology. Enzyme-linked immunosorbent assays (ELISA) were employed to detect brain-derived neurotrophic factor (BDNF), monocarboxylate transporter 1 (MCT1), lactate, and glutamate levels in the PFC, as well as serotonin (5-HT) levels in serum. Additionally, Western blot analysis was conducted to quantify the expression of synaptic plasticity-related proteins, including c-Fos, activity-regulated cytoskeleton-associated protein (Arc), and N-methyl-D-aspartate receptor 1 (NMDAR1). ResultsCompared to the control group (C), the CUMS-exposed rats (group M) exhibited significant reductions in sucrose preference rates, number of grid crossings, frequency of upright postures, and entries into and duration spent in open arms of the elevated plus maze, indicating marked depressive-like behaviors. Additionally, the group M showed significantly reduced dendritic spine density in the PFC, along with elevated levels of c-Fos, Arc, NMDAR1 protein expression, and increased concentrations of lactate and glutamate. Conversely, BDNF and MCT1 contents in the PFC and 5-HT levels in serum were significantly decreased. Following HIIT intervention, rats in the group HM displayed considerable improvement in behavioral indicators compared with the group M, accompanied by significant elevations in PFC MCT1 and lactate concentrations. Furthermore, HIIT notably normalized the expression levels of c-Fos, Arc, NMDAR1, as well as glutamate and BDNF contents in the PFC. Synaptic spine density also exhibited significant recovery. ConclusionFour weeks of HIIT intervention may alleviate depressive-like behaviors in CUMS rats by increasing lactate levels and reducing glutamate concentration in the PFC, thereby downregulating the overexpression of NMDAR, attenuating excitotoxicity, and enhancing synaptic plasticity.

Aim To explore the effect of total flavonoid extract(TFE)of Ampelopsis megalophylla on the pro-liferation and apoptosis of human breast cancer cells and its mechanism in autophagy inhibition.Methods For human cervical cancer cell Hela,human lung cancer cell A549,human liver cancer cell SMMC-7721,human breast cancer cell MCF-7,MDA-MB-231 and human normal liver cell L-02,MTT method was used to select sensitive cell lines.The inhibitory effect of TFE combined with autophagy inhibitor 3-methylade-nine(3-MA)on sensitive cell proliferation was detec-ted using MTT assay.The morphological changes of cells were observed using transmission electron micros-copy and Hoechst 33258 single staining method.The changes in cell apoptosis rate were detected using An-nexin V-FITC/PI dual staining method.The expression levels of apoptosis related proteins and pathway pro-teins(death receptor pathway,mitochondrial pathway,endoplasmic reticulum stress pathway)were detected uisng Western blot.The expression of the key protein Cyt-c in mitochondrial pathway was determiend by im-munofluorescence,and the autophagy agonist rapamy-cin was selected for reverse validation.Results TFE could inhibit the proliferation of human breast cancer cells in a concentration-dependent manner,and MCF-7 cells were sensitive cell lines.Compared with the TFE group,the TFE+3-MA group significantly increased the inhibition rate of MCF-7 cells at 24,48,and 72 h(P＜0.01).The number of cells decreased,the gap increased,the number of apoptotic bodies increased,and the apoptosis rate increased(P＜0.01).The ex-pression levels of Bax/Bcl-2(P＜0.01),cleaved-caspase3(P＜0.01),Cyt-c(P＜0.05),FADD,and cleaved-caspase-12 all increased,and the expres-sion of apoptotic protein Cyt-c in nucleus increased.The fluorescence of the TFE+RA group decreased,re-versing the mitochondrial pathway apoptosis induced by TFE.Conclusions TFE can significantly inhibit the proliferation of human breast cancer cells.When inhib-iting autophagy,it may promote the apoptosis of MCF-7 cells through the mitochondrial pathway,and activa-ting autophagy can reverse apoptosis.

Objective To explore clinical effect of distal tibial tubercle-high tibial osteotomy(DTT-HTO)in treating knee osteoarthritis(KO A)with medial meniscus posterior root tear(MMPRT).Methods A retrospective analysis was performed on 21 patients with varus KOA with MMPRT from May 2020 to December 2021,including 3 males and 18 females,aged from 49 to 75 years old with an average of(63.81±6.56)years old,the courses of disease ranged from 0.5 to 18.0 years with an average of(5.9±4.2)years,and 4 patients with grade Ⅱ,14 patients with grade Ⅲ,and 3 patients with grade Ⅳ according to Kellgren-Lawrence;14 patients with type 1 and 7 patients with type 2 according to MMPRT damage classification.The distance of medi-al meniscusextrusion(MME)and weight-bearing line ratio(WBLR)of lower extremity were compared before and 12 months after operation.Visual analogue scale(V AS),Western Ontarioand and McMaster Universities(WOMAC)osteoarthritis index,and Lysholm knee score were used to evaluate knee pain and functional improvement before operation,1,6 and 12 months after operation,respectively.Results Twenty-one patients were followed up for 12 to 18 months with an average of(13.52±1.72)months.MME distance was improved from(4.99±1.05)mm before operation to(1.87±0.76)mm at 12 months after operation(P＜0.05).WBLR was increased from(15.49±7.04)％before operation to(62.71±2.27)％at 12 months after operation(P＜0.05).VAS was decreased from(7.00±1.14)before operation to(2.04±0.80),(0.90±0.62)and(0.61±0.50)at 1,6 and 12 months after operation.WOMAC were decreased from preoperative(147.90±9.88)to postoperative(103.43±8.52),(74.00±9.54)and(47.62±9.53)at 1,6 and 12 months,and the difference were statistically significant(P＜0.05).Lysholm scores were increased from(46.04±7.34)before oepration to(63.19±8.93),(81.10±6.41)and(89.29±3.04)at 1,6 and 12 months after operation(P＜0.05).Conclusion For the treatment of varus KOA with MMPRT,DTT-HTO could reduce medial meniscus pro-trusion distance,improve the ratio of lower limb force line,and effectively reduce knee pain and improve knee joint function.

Objective To investigate the effects of different disinfection methods on ultrasound imaging and application firmness of ultrasound-guided subclavian vein puncture.Methods A total of 138 patients received ultrasound-guided subclavian vein puncture were selected as the study subjects and randomly divided into the chlorhexidine alcohol group,the iodophor group and the chlorhexidine alcohol+ iodophor group,with 46 cases in each group.They were disinfected with chlorhexidine alcohol,iodophor,and chlorhexidine alcohol and iodophor,respectively.The disinfection effect,ultrasound imaging and application fixation of patients in the three groups were compared.Results There was no statistically significant difference in the bacterial count or disinfection qualification rate of the puncture sites before and after disinfection of patients among the three groups(P>0.05).The ultrasound imaging clarity rate during puncture in the chlorhexidine alcohol group was significantly lower than those in the iodophor group and the chlorhexidine alcohol+iodophor group(P<0.05);while the rate of sticking up or shedding of application in the iodophor group 24 hours after puncture was significantly higher than those in the chlorhexidine alcohol group and the chlorhexidine alcohol+iodophor group(P<0.05).Conclusion Disinfecting the puncture area twice with chlorhexidine alcohol and then disinfecting the ultrasound exploration area once with iodophor can obtain satisfactory disinfection effect for patients undergoing ultrasound-guided subclavian vein puncture,with clearer ultrasound imaging and stronger adhesion of the application at the same time.