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.

Objective To clarify the risk factors of diaphragmatic dysfunction (DD) after cardiac surgery with extracorporeal circulation. Methods A retrospective analysis was conducted on the data of patients who underwent cardiac surgery with extracorporeal circulation in the Department of Cardiovascular Surgery of Peking University People's Hospital from January 2023 to March 2024. Patients were divided into two groups according to the results of bedside diaphragm ultrasound: a DD group and a control group. The preoperative, intraoperative, and postoperative indicators of the patients were compared and analyzed, and independent risk factors for DD were screened using multivariate logistic regression analysis. Results A total of 281 patients were included, with 32 patients in the DD group, including 23 males and 9 females, with an average age of (64.0±13.5) years. There were 249 patients in the control group, including 189 males and 60 females, with an average age of (58.0±11.2) years. The body mass index of the DD group was lower than that of the control group [(18.4±1.5) kg/m2 vs. (21.9±1.8) kg/m2, P=0.004], and the prevalence of hypertension, chronic obstructive pulmonary disease, heart failure, and renal insufficiency was higher in the DD group (P<0.05). There was no statistical difference in intraoperative indicators (operation method, extracorporeal circulation time, aortic clamping time, and intraoperative nasopharyngeal temperature) between the two groups (P>0.05). In terms of postoperative aspects, the peak postoperative blood glucose in the DD group was significantly higher than that in the control group (P=0.001), and the proportion of patients requiring continuous renal replacement therapy was significantly higher than that in the control group (P=0.001). The postoperative reintubation rate, tracheotomy rate, mechanical ventilation time, and intensive care unit stay time in the DD group were higher or longer than those in the control group (P<0.05). Multivariate logistic regression analysis showed that low body mass index [OR=0.72, 95%CI (0.41, 0.88), P=0.011], preoperative dialysis [OR=2.51, 95%CI (1.89, 4.14), P=0.027], low left ventricular ejection fraction [OR=0.88, 95%CI (0.71, 0.93), P=0.046], and postoperative hyperglycemia [OR=3.27, 95%CI (2.58, 5.32), P=0.009] were independent risk factors for DD. Conclusion The incidence of DD is relatively high after cardiac surgery, and low body mass index, preoperative renal insufficiency requiring dialysis, low left ventricular ejection fraction, and postoperative hyperglycemia are risk factors for DD.

eToyBox is a learning management system for preschool teachers to improve their health literacy, which ultimately aims to improve children’s obesity-related behaviour. As part of the development process of eToyBox, assessment on digital literacy, acceptance of digitization of education materials, and perceived barriers in adopting online learning is needed. Fifty-four preschool teachers under the Community Development Department (KEMAS) in Kuala Lumpur, Selangor, and Sarawak, who participated in ToyBox Study Malaysia intervention in 2018, took part in this cross-sectional study. An online self-administered questionnaire was used to assess sociodemographic background, use of communication tools and media, and teacher’s views on adapting the ToyBox modules to digital education materials. Respondents were contacted, and questionnaire link was shared through WhatsApp messages. Most participants (74.0%) were Malay females aged 31 to 40 years old. Most participants had internet access (94.4%) and owned at least a smart phone, laptop or tablet (94.4%). Participants perceived their computer skills to be average (75.0%). Majority of respondents (65.0%) reported advanced and higher abilities in word processing and email, but only 22.0% in spreadsheet skills. The main barrier to accessing online material was unstable internet connection (74.1%). Most respondents (90.0%) agree that adapting effective modules to online learning will be beneficial for professional development and teaching practices. In conclusion, most participants supported digitizing Toybox Study Malaysia educational content and were comfortable 72 with its implementation via an online learning platform. The findings from this study can advise future development of online learning materials for preschool teachers in Malaysia.

Aim To investigate the effect of quercetin on the aging model of bone marrow mesenchymal stem cells established under microgravity. Methods Using 3D gyroscope, a aging model of bone marrow mesenchymal stem cells was constructed, and after receiving quercetin and microgravity treatment, the anti-aging effect of the quercetin was evaluated by detecting related proteins and oxidation indexes. Results Compared to the control group, the expressions of age-related proteins p21, pi6, p53 and RB in the microgravity group significantly increased, while the expressions of cyclin D1 and lamin B1 significantly decreased, with statistical significance (P<0.05). In the microgravity group, mitochondrial membrane potential significantly decreased (P<0.05), ROS accumulation significantly increased (P <0.05), SOD content significantly decreased and MDA content significantly increased (P<0.05). Compared to the microgravity group, the expressions of age-related proteins p21, pi6, p53 and RB in the quercetin group significantly decreased, while the expressions of cyclin D1 and lamin B1 significantly increased, with statistical significance (P<0.05). In the quercetin group, mitochondrial membrane potential significantly increased (P<0.05), ROS accumulation significantly decreased (P<0.05), SOD content significantly increased and MDA content significantly decreased (P<0.05). Conclusions Quercetin can resist oxidation, protect mitochondrial function and normal cell cycle, thus delaying the aging of bone marrow mesenchymal stem cells induced by microgravity.

Aim To investigate the effect of benzyl iso-thiocyanate (BITC) on the proliferation of mouse U14 cervical cancer cells and to explore the mechanism of cytotoxicity based on transcriptomic data analysis. Methods The effect of BITC on U14 cell activity was detected by MTT, nuclear morphological changes were observed by Hochest 33258 and fluorescent inverted microscope, cell cycle and apoptosis were determined by flow cytometry, and the transcriptome database of U14 cells before and after BITC (20 μmol · L

Objective To investigate the morphological typing and clinical significance of the distal tibiofibular syndesmosis fibular notch based on CT images. Methods According to the inclusion and exclusion ceiteria‚ the imaging data of patients undergoing ankle joint CT examination were analyzed‚ and the inferior tibiofibular joint fibula notch was classified according to the morphological characteristics. The measurements included 8 distances. There were 123 males and 102 females‚ all of whom were Han nationality‚ aged 18-60 years old. Results Retrospectively analyzed the result of 225 patients from December 2013 to December 2022. The distal tibiofibular syndesmosis fibular notch was divided into four types according to morphological characteristics‚ C-shaped (50. 67%)‚ V-shaped (26. 67%)‚ flat-shaped (15. 11%) and L-shaped (7. 56%). The angle between the anterior and posterior facets of the flat shape (145. 56 ± 9. 25)° was the largest and the angle between the anterior and posterior facets of the L shape (125. 07 ± 13. 54)° was the smallest(P< 0. 05); the depth of the notch in the flat shape (3. 11 ± 0. 83) mm was the smallest and in the L shape (4. 47±1. 11) mm was the largest(P<0. 05);The posterior facet length (13. 06 ± 3. 56) mm and anterior tibiofibular gap (3. 83±1. 49) mm on left were larger than on the right side (P<0. 05); The posterior facet length (13. 36 ± 3. 46) mm‚ fibular notch depth (3. 93 ± 1. 10) mm and vertical distance of tibiofibular overlap (9. 10 ± 2. 55) mm larger in men than in women (P<0. 05). Conclusion In this study‚ the data related to the inferior tibiofibular syndesmosis notch were measured and divided into four types according to the shape. The flat inferior tibiofibular syndesmosis notch is more likely to have chronic ankle instability‚ and the fibula is more likely to move forward during anatomical reduction. The inferior tibiofibular syndesmosis of L-shaped and C-shaped notches is more prone to posterior displacement of fibula or poor rotation reduction during anatomical reduction.