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.

OBJECTIVES: To study the incidence and disease burden of congenital birth defects in children under five in China from 1990 to 2021 and to predict the incidence of congenital birth defects in this population from 2022 to 2036, providing a reference for the prevention of congenital birth defects in children. METHODS: Using the Global Burden of Disease Study 2021 (GBD 2021) database, the incidence and disability-adjusted life years (DALY) were employed to describe the disease burden. The Joinpoint regression model was used to analyze the trends in incidence and DALY rates of congenital birth defects in children under five. A grey prediction model GM(1,1) was applied to fit the trend of incidence rates of congenital birth defects in this age group and to predict the incidence from 2022 to 2036. RESULTS: In 2021, the incidence rate of congenital birth defects among children under five in China was 737.28 per 100 000. Among these, congenital musculoskeletal and limb deformities had the highest incidence rate at 307.15 per 100 000, followed by congenital heart defects (223.53 per 100 000), congenital urinary and genital tract malformations (74.99 per 100 000), and congenital gastrointestinal malformations (62.61 per 100 000). From 1990 to 2021, the incidence rate and DALY rate of congenital birth defects in children under five in China decreased at an average annual rate of 1.73% and 5.42%, respectively. The prediction analysis indicated a decreasing trend in the incidence of congenital birth defects among children under five in China from 2022 to 2036, with the incidence rate dropping from 892.36 per 100 000 in 2022 to 783.35 per 100 000 in 2036. CONCLUSIONS The incidence and disease burden of congenital birth defects in children under five in China showed a significant declining trend from 1990 to 2021. It is predicted that this incidence will continue to decrease until 2036.
Humans ; Congenital Abnormalities/epidemiology* ; China/epidemiology* ; Incidence ; Infant ; Infant, Newborn ; Child, Preschool ; Female ; Male ; Forecasting ; Disability-Adjusted Life Years

Coronavirus disease 2019 (COVID-19) is an acute infectious respiratory disease that has been prevalent since December 2019. Chinese medicine (CM) has demonstrated its unique advantages in the fight against COVID-19 in the areas of disease prevention, improvement of clinical symptoms, and control of disease progression. This review summarized the relevant material components of CM in the treatment of COVID-19 by searching the relevant literature and reports on CM in the treatment of COVID-19 and combining with the physiological and pathological characteristics of the novel coronavirus. On the basis of sorting out experimental methods in vivo and in vitro, the mechanism of herb action was further clarified in terms of inhibiting virus invasion and replication and improving related complications. The aim of the article is to explore the strengths and characteristics of CM in the treatment of COVID-19, and to provide a basis for the research and scientific, standardized treatment of COVID-19 with CM.
Humans ; Drugs, Chinese Herbal/pharmacology* ; COVID-19 Drug Treatment ; SARS-CoV-2/drug effects* ; COVID-19/therapy* ; Medicine, Chinese Traditional/methods* ; Antiviral Agents/pharmacology* ; Animals

Transcranial temporal interference stimulation (tTIS) is a novel non-invasive neuromodulation technique with the potential to precisely target deep brain structures. This study explores the neural and behavioral effects of tTIS on the superior colliculus (SC), a region involved in eye movement control, in mice. Computational modeling revealed that tTIS delivers more focused stimulation to the SC than traditional transcranial alternating current stimulation. In vivo experiments, including Ca²⁺ signal recordings and eye movement tracking, showed that tTIS effectively modulates SC neural activity and induces eye movements. A significant correlation was found between stimulation frequency and saccade frequency, suggesting direct tTIS-induced modulation of SC activity. These results demonstrate the precision of tTIS in targeting deep brain regions and regulating eye movements, highlighting its potential for neuroscientific research and therapeutic applications.
Animals ; Superior Colliculi/physiology* ; Transcranial Direct Current Stimulation/methods* ; Eye Movements/physiology* ; Male ; Mice ; Mice, Inbred C57BL

OBJECTIVES: To evaluate the osteogenic efficacy of three-dimensional printing individualized titanium mesh (3D-PITM) as a scaffold material in guided bone regeneration (GBR). METHODS: 1) Patients undergoing GBR for alveolar bone defects were enrolled as study subjects, and postoperative healing complications were recorded. 2) Postoperative cone beam computed tomography (CBCT) scans acquired at least 6 months post-surgery were used to calculate the percentage of actual bone formation volume. 3) Alveolar bone specimens were collected during the first-stage implant surgery for histomorphometric analysis. This analysis quantitatively measured the proportions of newly formed bone and newly formed unmineralized bone within the specimens. Specimens were categorized into three groups based on healing complications (good healing group, wound dehiscence group, 3D-PITM exposure group) to compare differences in the proportions of newly formed bone and newly formed unmineralized bone. RESULTS: 1) Twelve patients were included. Guided bone regeneration failed in one patient, and 3D-PITM exposure occurred in three patients (exposure rate: 25%). 2) The mean percentage of actual bone formation volume in the 11 successful guided bone regeneration cases was 95.23%±28.85%. 3) Histomorphometric analysis revealed that newly formed bone constituted 40.35% of the alveolar bone specimens, with newly formed unmineralized bone accounting for 13.84% of the newly formed bone. Intergroup comparisons showed no statistically significant differences (P>0.05) in the proportions of newly formed bone or newly formed unmineralized bone between the good healing group and the wound dehiscence group or the 3D-PITM exposure group. CONCLUSIONS 3D-PITM enables effective bone augmentation. Radiographic assessment demonstrated favorable bone formation volume, while histological analysis confirmed substantial formation of newly formed mineralized bone within the surgical site.
Humans ; Printing, Three-Dimensional ; Titanium ; Cone-Beam Computed Tomography ; Bone Regeneration ; Osteogenesis ; Surgical Mesh ; Tissue Scaffolds ; Alveolar Process/surgery* ; Adult ; Male ; Middle Aged ; Female ; Wound Healing ; Guided Tissue Regeneration, Periodontal/methods* ; Alveolar Bone Loss/surgery*