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.

In view of the few studies on the influence of Armillaria spp. infection on the content of the chemical components in different parts of Polyporus umbellatus sclerotia, this study determined the biomass of P. umbellatus sclerotia and the contents of ergosterol, polyporusterone A, polyporusterone B and polysaccharide in the separated cavity wall of the sclerotia and the uninfected part of the sclerotia in different harvesting years under the conditions of A. gallica and A. mellea infection respectively. According to the difference of content and dynamic changes of the polysaccharide and the steroid substances, the superior Armillaria sp. was screened to obtain the best harvest years of P. umbellatus. Using HPLC and UV-VIS spectrophotometry methods, the contents of ergosterol, polyporusterone A, polyporusterone B and polysaccharide in P. umbellatus sclerotia infected by the two Armillaria spp. in different years were determined. In addition, the differentially expressed genes related to P. umbellatus polysaccharide synthesis were screened according to the transcriptomic data of different parts of P. umbellatus after A. mellea infection. With the increase of years, the biomass of sclerotia infected by different Armillaria spp. had significant differences, and there were significant differences in the four components of sclerotia. The four components of the separated cavity wall of the sclerotia were significantly higher than those of the uninfected part. The best harvest time was the third year after cultivation. Transcriptomic analysis showed that the infection of Armillaria spp. could significantly promote polysaccharide synthesis, which provided a basis for polysaccharide content determination at the molecular level. The study clarified the influence of different Armillaria spp. infection on the accumulation of chemical components of P. umbellatus sclerotia, laying a foundation for exploring the symbiosis mechanism and provided a scientific clue for screening superior Armillaria sp. and guiding the artificial cultivation of P. umbellatus sclerotia.

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 evaluate the effect of Pien Tze Huang(PTH)on influenza virus susceptibility in re-straint stress-induced H1N1 influenza susceptibility model in mice with emotional disorders and internal heat,guided by the theory of emotional pathogenesis.Methods Mice were infected with H1N1 influenza vi-rus following 18 h of restraint stress.The signs and weight changes of mice were recorded,and the morbid-ity of mice were analyzed.On the fourth day post viral infection,the lung tissue was collected.The pathologi-cal changes and inflammatory factors in lungs were de-tected by HE staining.The expression of NP was as-sessed by immunohistochemistry and Western blot.The level of lipid peroxidation end products was detected u-sing a commercial kit and western blot.Redox phos-pholipidomics was analyzed in lung tissue by HPLC-MS/MS.Results PTH significantly reduced the mor-tality of influenza-susceptible mice induced by emotion-al stress,inhibited the expression of NP and the re-lease of inflammatory factors,improved inflammation in lung tissue,and alleviated the accumulation of lipid peroxidation end products.Phospholipid oxidation a-nalysis revealed the elevated levels of oxidized phos-pholipid choline and phosphatidylethanolamine in lung tissue of influenza-susceptible mice,which were signif-icantly reduced by PTH administration.Conclusions PTH exhibits promising efficacy in ameliorating influ-enza virus susceptibility induced by internal heat,and its mechanism of action may be related to the regulation of phospholipid peroxidation.

Major depressive disorder(MDD)is a prevalent con-dition associated with substantial impairment and low remission rates.Traditional antidepressants demonstrate delayed effects,low cure rate,and inadequate therapeutic effectiveness for man-aging treatment-resistant depression(TRD).Several studies have shown that ketamine,a non-selective N-methyl-D-aspartate receptor(NMDAR)antagonist,can produce rapid and sustained antidepressant effects.Ketamine has demonstrated efficacy for reducing suicidality in TRD patients.However,the pharmaco-logical mechanism for ketamine's antidepressant effects remains incompletely understood.Previous research suggests that the an-tidepressant effects of ketamine may involve the monoaminergic,glutamatergic and dopaminergic systems.This paper provides an overview of the pharmacological mechanism for ketamine's anti-depressant effects and discuss the potential directions for future research.

Objective To compare the incidence of superior facet joint violation in patients with different preoperative diagnoses in posterior lumbar interbody fusion.Methods The clinical data of 320 patients who underwent posterior lumbar interbody fusion from January 2018 to December 2021 in our hospital were retrospectively analyzed,and the preoperative diagnoses were including the lumbar disc herniation/lumbar spinal canal stenosis,degenerative lumbar spondylolisthesis,and isthmic lumbar spondylolisthesis.The superior facet joint violation was graded based on the postoperative lumbar CT,the incidences of superior facet joint intrusion of patients with different preoperative diagnosis were compared,and the correlations between relevant parameters(including the apical vertebrae of fixation segments,facet joint angle,depth of lamina,introversion angle of pedicle screw,axial diameter of facet joint,and slippage length of vertebrae)with superior facet joint violation and preoperative diagnoses were investigated.Results The incidence of superior facet joint violation in the patients with isthmic lumbar spondylolisthesis(57.4%)was higher than that in the patients with lumbar disc herniation/lumbar spinal canal stenosis(41.2%)and degenerative lumbar spondylolisthesis(35.9%),with statistically significant differences(P<0.05).The apical vertebrae of fixation segments,facet joint angle,axial diameter of facet joint and introversion angle of pedicle screw were related with the occurrence of superior facet joint violation(P<0.05).There were statistically significant differences in the facet joint angle,axial diameter of facet joint and apical vertebrae of fixation segments among patients with different preoperative diagnoses(P<0.05).Conclusion Patients with isthmiclumbar spondylolisthesis are more likely to occur superior facet joint violation than patients with lumbar disc herniation/lumbar spinal canal stenosis and degenerative lumbar spondylolisthesis.

Objective To compare the effects of transumbilical single-incision appendectomy and laparoscopic appendectomy in the treatment of acute appendicitis in pediatric patients.Methods The clinical data of 162 pediatric patients under 14 years old with acute appendicitis admitted to our department were retrospectively analyzed,and the pediatric patients were divided into the transumbilical single-incision surgery group(n=59)and the laparoscopic group(n=103)according to their surgeries.The completion of the operation was recorded,and the operation time,intraoperative blood loss,gastrointestinal function recovery time,hospitalization cost,hospital stay and complications were compared between the two groups.The postoperative wound healing was observed.The wound infection,abdominal pain,abdominal distension,vomiting and abnormal stool were observed during the 6-month follow-up after operation.Results The operations of pediatric patients in the laparoscopic group were successfully completed without conversion to open surgery.The appendixes of 2 cases in the transumbilical single-incision surgery group were found by transumbilical laparoscopy during the operation due to the difficulty in finding the appendixes.The operation time and hospital stay in the transumbilical single-incision surgery group were shorter than those in the laparoscopic group(P<0.05).There was no significant difference in the hospitalization cost,intraoperative blood loss,gastrointestinal function recovery time,or incidence of complications between the two groups(P>0.05).The incision healing in the transumbilical single-incision surgery group was more beautiful.During the follow-up period,the incision of the pediatric patients in the two groups healed well,and there was no abdominal pain,abdominal distension,vomiting or abnormal stool.Conclusion Compared with the traditional laparoscopic appendectomy,transumbilical single-incision appendectomy has the advantages of simple operation,short operation time and hospital stay,and more beautiful incision healing.