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.

BACKGROUND:With the increase of patients with cubital tunnel syndrome,ulnar groove plasty does not affect the normal anatomical structure and distribution of the ulnar nerve,which is one of the main surgical procedures for the treatment of cubital tunnel syndrome.3D printing combined with ulnar groove plasty can more accurately position the expansion depth and width of the ulnar groove to avoid some surgical complications. OBJECTIVE:To investigate the effect of 3D printing technology combined with ulnar groove plasty on nerve electrophysiology and prognosis in patients with cubital tunnel syndrome. METHODS:A total of 70 patients with moderate and severe cubital tunnel syndrome who were treated in Cangzhou Integrated Traditional Chinese and Western Medicine Hospital from March 2020 to March 2022 were selected as the study subjects.They were divided into two groups,with 35 cases in each group.The control group underwent traditional ulnar groove plasty.The observation group underwent 3D printing technology combined with ulnar groove plasty.The patients were followed up for 3 months.The clinical efficacy,latency,amplitude of compound muscle action potential of abductor pollicis brevis of the affected limb and ulnar nerve motor conduction velocity,grip strength on the affected side,pinch strength of the middle and thumb fingers,S-W monofilament of the little finger,two-point discrimination of the little finger,and Disabilities of the Arm,Shoulder and Hand Questionnaire score were compared between the two groups. RESULTS AND CONCLUSION:(1)Compared with the control group(74%),the excellent and good rate was significantly higher in the observation group(91%)(P<0.05).(2)Compared with pre-treatment,the latency of compound muscle action potential of abductor pollicis brevis of affected limb was significantly shorter and the wave amplitude and ulnar nerve motor conduction velocity were significantly higher in the two groups after treatment.The latency was significantly shorter and the wave amplitude and ulnar nerve motor conduction velocity were significantly higher in the observation group than those in the control group(P<0.05).(3)Compared with pre-treatment,the grip strength,middle finger and thumb pinch strength of the affected side,S-W monofilament of the little finger and two-point discrimination of the little finger were significantly decreased in the two groups after treatment.The grip strength,middle finger and thumb pinch strength on the affected side were greater,S-W monofilament of the little finger and two-point discrimination of the little finger were significantly smaller in the observation group than those in the control group(P<0.05).(4)Compared with pre-treatment,the Disabilities of the Arm,Shoulder and Hand Questionnaire scores of the two groups were significantly reduced after treatment,and the Disabilities of the Arm,Shoulder and Hand Questionnaire scores of the observation group were significantly lower than those of the control group(P<0.05).(5)It is concluded that 3D printing technology combined with ulnar groove plasty in the treatment of cubital tunnel syndrome can effectively improve its clinical efficacy,promote the neurophysiological recovery of patients,and enhance the function of fingers and upper limbs,which has high clinical application value.

Objective:To study the expression levels and clinical significance of microR-NA-183-5p(miR-183-5p)and thioesterase superfamily member 4(THEM4)in colon cancer tissues.Methods:A total of 96 patients with colon cancer who in Hebei China Petroleum Central Hospital gathered as the research objects.During the course of radical resection of colon cancer patients,the colon cancer tissues and adjacent normal tissues were collected.The relative expression levels of miR-183-5p and THEM4 mRNA in colon cancer tissues and adjacent normal tissues were detected.Analysis of the correlation between miR-183-5pand THEM4mRNA in colon cancer and their relation-ship with prognosis.COX regression was used to analyze the risk factors affecting the prognosis of pa-tients with colon cancer.Results:Compared with adjacent normal tissues,the expression level of miR-183-5p in colon cancer tissues increased(P＜0.05),and the expression level of THEM4 mRNA decreased(P＜0.05).MiR-183-5p was negatively correlated with THEM4 mRNA expression in colon cancer tissue(r=-0.529,P＜0.05).The survival rate of the high expression group of miR-183-5p lower than that of the low expression group(P＜0.05),the survival rate of the high expression group of THEM4 was obviously higher than that of the low expression group(P＜0.05).TNM stage(Ⅲ-Ⅳ),high expres-sion of miR-183-5p and low expression of THEM4 were risk factors for poor prognosis in patients with colon cancer(P＜0.05).Conclusion:The expression level of miR-183-5p in cancer tissues of patients with colon cancer is increased,and the expression level of THEM4 is decreased,both are closely relat-ed to the clinicopathological characteristics and prognosis of patients.

Quercetin can mediate the activation of mitogen-activated protein kinase(MAPK)signaling pathways to prevent osteoporosis(OP).This paper comprehensively discusses the interrelationship between MAPK and osteoporosis-related cells based on the latest domestic and international research.Additionally,it elucidates the research progress of quercetin in mediating the MAPK signaling pathway for OP prevention.The aim is to provide an effective foundation for the clinical prevention and treatment of OP and the in-depth development of quercetin.

Objective:To analyze the activity of lithium chloride (LiCl) against influenza virus A (H3N2) in human non-small cell lung cancer cells (A549).Methods:Different concentrations of LiCl were incubated with A549 cells, and the cytopathic effect (CPE) was observed after 24 hours, and the effect of LiCl on cell activity was determined by CCK-8 method. After H3N2 (MOI=1) infected A549 cells, different concentrations of LiCl were added and incubated for 24 hours, and the viral load was measured by real time/reverse transcription quantitative polymerase chain reaction (RT-qPCR), and the CPE was observed, and the viral titer was determined. Different concentrations of LiCl were incubated with A549 at 37 ℃ and 5% CO 2 for 2 hours, virus was added and incubated for 24 hours, and the viral load was determined by RT-qPCR. LiCl, H3N2 and A549 were incubated at 4 ℃ for 1 hour, 35 ℃, 5% CO 2 for 24 hours, and viral load was determined by RT-qPCR. H3N2 and A549 were incubated at 4 ℃for 1 hour, then different concentrations of LiCl were added, incubated at 35 ℃ with 5% CO 2 for 24 hours, and the viral load was determined by RT-qPCR. After H3N2 infected A549 cells, different concentrations of LiCl were added and incubated for 24 hours, and the viral RNA load and viral titer of the supernatant and cells were measured, respectively, and then the corresponding ratios of the supernatant and the cells were calculated. After H3N2 (MOI=10) and BV (MOI=1) infected A549 cells, different concentrations of LiCl were added for 24 h, and the viral load was determined by RT-qPCR. Results:When the concentration of LiCl was<50 mmol/L, the cell viability of A549>90%. Different concentrations of LiCl could significantly reduce the viral load of H3N2 ( P<0.000 1), and the CPE of the LiCl treatment group was more dose-dependent than that of the control group. LiCl did not inhibit viral replication by affecting the cell itself; Different concentrations of LiCl significantly inhibited the entry of H3N2 into A549 ( P<0.000 1), and also had a certain inhibitory effect on the adsorption of A549 cells ( P<0.1). LiCl did not affect the assembly and release of H3N2 ( P>0.05), and it was also found that LiCl had a broad spectrum of antiviral effects against multiple influenza virus strains ( P<0.000 1). Conclusions:LiCl may exert antiviral effect by inhibiting the adsorption and entry of H3N2 into A549 cells and the replication of H3N2 in A549 cells, which provides a data reference for the prevention and treatment of viral infection by LiCl.

Objective To investigate the distribution of the traditional Chinese medicine(TCM)constitution types in the patients with nonspecific low back pain(NLBP)and to explore the correlation of the thickness of ligamentum flavum with the age,body mass index(BMI),gender,the presence of diabetes mellitus,and the grading of hypertension.Methods Sixty patients with NLBP admitted to Guangdong Second Traditional Chinese Medicine Hospital from January 2023 to June 2023 were selected as the study subjects.The TCM constitution types of the patients were identified,the thickness of the ligamentum flavum at lumbar vertebrae 4/5 segment(L4/5)disc level was measured by computerized tomography(CT)scanning,and the patients'age,genders,TCM constitution types,BMI,the presence or absence of diabetes mellitus,and hypertension grading were recorded.Correlation analysis and linear regression analysis were used for the exploration of the relevant influencing factors for the thickness of the ligamentum flavum of patients with NLBP.Results(1)The average thickness of ligamentum flavum in the 60 patients with NLBP was(2.60±0.72)mm.(2)The TCM constitutions of NLBP patients were classified into four types,of which blood stasis constitution was the most common,accounting for 21 cases(35.0%),followed by 19 cases(31.7%)of damp-heat constitution,12 cases(20.0%)of phlegm-damp constitution,and 8 cases(13.3%)of qi deficiency constitution.(3)The results of correlation analysis showed that BMI,gender,TCM constitution type and the presence or absence of diabetes mellitus had no influence on the thickness of ligamentum flavum in NLBP patients(P＞0.05),while the age and hypertension grading had an influence on the thickness of ligamentum flavum(P＜0.01).(4)The results of linear regression analysis showed that the age had an influence on the thickness of the ligamentum flavum(b = 0.034,t = 6.282,P＜0.01),while the influence of the hypertension grading had no influence on the thickness of the ligamentum flavum(P＞0.05).Conclusion The TCM constitution type of NLBP patients is predominated by blood stasis constitution,the thickness of ligamentum flavum is significantly affected by the age,and hypertension may be a potential factor affecting the thickness of ligamentum flavum.