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.

This study identified 8 members including NjBIN2 of the GSK3 family in Nardostachys jatamansi by bioinformatics analysis. Moreover, the phylogenetic tree revealed that the GKS3 family members of N. jatamansi had a close relationship with those of Arabidopsis. RT-qPCR results showed that NjBIN2 presented a tissue-specific expression pattern with the highest expression in roots, suggesting that NjBIN2 played a role in root growth and development. In addition, the application of epibrassinolide or the brassinosteroid(BR) synthesis inhibitor(brassinazole) altered the expression pattern of NjBIN2 and influenced the photomorphogenesis(cotyledon opening) and root development of N. jatamansi, which provided direct evidence about the functions of NjBIN2. In conclusion, this study highlights the roles of BIN2 in regulating the growth and development of N. jatamansi by analyzing the expression pattern and biological function of NjBIN2. It not only enriches the understanding about the regulatory mechanism of the growth and development of N. jatamansi but also provides a theoretical basis and potential gene targets for molecular breeding of N. jatamansi with improved quality in the future.
Brassinosteroids/metabolism* ; Steroids, Heterocyclic/metabolism* ; Gene Expression Regulation, Plant/drug effects* ; Plant Proteins/metabolism* ; Phylogeny ; Nardostachys/metabolism* ; Plant Growth Regulators/pharmacology* ; Plant Roots/drug effects*

Jiuwei Xifeng Granules have become a Chinese patent medicine in the market. Because the formula contains Os Draconis, a top-level protected fossil of ancient organisms, the formula was to be improved by replacing Os Draconis with Ostreae Concha. To evaluate whether the improved formula has the same effectiveness and safety as the original formula, a randomized, double-blind, parallel-controlled, equivalence clinical trial was conducted. This study enrolled 288 tic disorder(TD) of children and assigned them into two groups in 1∶1. The treatment group and control group took the modified formula and original formula, respectively. The treatment lasted for 6 weeks, and follow-up visits were conducted at weeks 2, 4, and 6. The primary efficacy endpoint was the difference in Yale global tic severity scale(YGTSS)-total tic severity(TTS) score from baseline after 6 weeks of treatment. The results showed that after 6 weeks of treatment, the declines in YGTSS-TSS score showed no statistically significant difference between the two groups. The difference in YGTSS-TSS score(treatment group-control group) and the 95%CI of the full analysis set(FAS) were-0.17[-1.42, 1.08] and those of per-protocol set(PPS) were 0.29[-0.97, 1.56], which were within the equivalence boundary [-3, 3]. The equivalence test was therefore concluded. The two groups showed no significant differences in the secondary efficacy endpoints of effective rate for TD, total score and factor scores of YGTSS, clinical global impressions-severity(CGI-S) score, traditional Chinese medicine(TCM) response rate, or symptom disappearance rate, and thus a complete evidence chain with the primary outcome was formed. A total of 6 adverse reactions were reported, including 4(2.82%) cases in the treatment group and 2(1.41%) cases in the control group, which showed no statistically significant difference between the two groups. No serious suspected unexpected adverse reactions were reported, and no laboratory test results indicated serious clinically significant abnormalities. The results support the replacement of Os Draconis by Ostreae Concha in the original formula, and the efficacy and safety of the modified formula are consistent with those of the original formula.
Adolescent ; Child ; Child, Preschool ; Female ; Humans ; Male ; Double-Blind Method ; Drugs, Chinese Herbal/therapeutic use* ; Tic Disorders/drug therapy* ; Treatment Outcome

As a new type of production factor that can empower the development of new quality productivity, the data element is an important engine to promote the high quality development of the industry. Traditional Chinese medicine(TCM) dispensing is the most basic work of TCM clinical pharmacy, and its quality directly affects the clinical efficacy of TCM. The integration of data elements and TCM dispensing can stimulate the innovation and vitality of the TCM dispensing industry and promote the high-quality and sustainable development of the industry. A large-scale, detailed, and systematic study on TCM dispensing was conducted. The innovative practice path of data fusion construction in the whole process of TCM dispensing was investigated by integrating the digital resources "nine full activities" of TCM dispensing, creating the digital dictionary of "TCM clinical information data elements", and exploring innovative applications of TCM dispensing driven by data and technology, so as to promote the standardized, digital, and intelligent development of TCM dispensing in medical health services. The research content of this project was successfully selected as the second batch of "Data element×" typical cases of National Data Administration in 2024, which is the only selected case in the field of TCM.
Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal ; Humans

OBJECTIVE: To explore the clinical effect of personalized puncture planning before surgery using Picture Archiving and Communication System (PACS) in the treatment of osteoporotic vertebral compression fractures in the elderly. METHODS: A total of 69 elderly patients with osteoporotic vertebral compression fractures treated by percutaneous vertebroplasty from January 2020 20 to December 2021 with more than 1 year of follow-up were analyzed retrospectively. Thirty-four patients were individualized for preoperative planning with PACS software (observation group), including 8 males and 26 females, with a mean age of (73.30±7.96) years old;and 35 patients were treated with conventional treatment (control group), including 7 males and 28 females, with a mean age of (77.30±7.84) years old. The operation time, the amount of cement injection, cement leakage rate, bone watertight diffusion and refracture within 1 year between two groups were observed and compared. The Cobb's angle, low back pain visual analogue scale(VAS) and the modified Oswsetry disability indexes(ODI) before surgery and 1 day, 1 year after surgery were compared between two groups. RESULTS: Both groups successfully completed the operation without serious surgical complications, 2 refractures occurred in the control group. The operation time in the observation group was(41.9±11.9) min, which was less than that in the control group (52.7±13.6) min (P<0.05). There was no significant difference in the cement injection volume between two groups (P>0.05). Two cases of cement leakage in the observation group was less than 8 in the control group (P<0.05). The bone cement distribution index of two groups had significant difference(P<0.05). There were no significant differences between two groups in Cobb's angle of the injured vertebras and ODI before and 1 day after surgery(P>0.05), however, the comparative differences were statistically significant at 1 year after surgery(P<0.05). There was no significant difference in the VAS between two groups at each time period(P>0.05). CONCLUSION Using the PACS software to plan personalized puncture scheme can reduce the operation time, reduce the cement leakage rate, improve the diffusion of bone cement and longer maintain the postoperative form of vertebral body and the functional state of patients' lumbar back.
Humans ; Male ; Female ; Aged ; Vertebroplasty/methods* ; Fractures, Compression/diagnostic imaging* ; Spinal Fractures/diagnostic imaging* ; Osteoporotic Fractures/diagnostic imaging* ; Aged, 80 and over ; Retrospective Studies ; Radiology Information Systems

This study explores whether the current external quality assessment (EQA) level and acceptable bias for basic semen analysis in China are clinically useful. We collected data of semen EQA from Andrology laboratories in the Hunan Province (China) in 2022 and searched for data in the published literature from January 2000 to December 2023 in China. On the basis of these data, we analyzed the coefficients of variation and acceptable biases of different quality control materials for basic semen analysis through robust statistics. We compared these findings with quality specifications based on biological variation from optimal, desirable, and minimum levels of bias to seek a unified and more suitable semen EQA bias evaluation standard for China's national conditions. Different sources of semen quality control material exhibited considerable variation in acceptable biases among laboratories, ranging from 8.2% to 56.9%. A total of 50.0% of the laboratories met the minimum quality specifications for progressive motility (PR), whereas 100.0% and 75.0% of laboratories met only the minimum quality specifications for sperm concentration and total motility (nonprogressive [NP] + PR), respectively. The Z value for sperm concentration and PR+NP was equivalent to the desirable performance specification, whereas the Z value for PR was equivalent only to the minimum performance specification. This study highlights the feasibility of operating external quality assessment schemes for basic semen analysis using quality specifications based on biological variation. These specifications should be unified among external quality control (EQC) centers based on biological variation.
Semen Analysis/standards* ; Humans ; China ; Male ; Quality Control ; Sperm Motility ; Sperm Count/standards*