GPR126, also known as ADGRG6, is one of the most deeply studied aGPCRs. Initially, GPR126 was thought to be a receptor associated with muscle development and was primarily expressed in the muscular and skeletal systems. With the deepening of research, it was found that GPR126 is expressed in multiple mammalian tissues and organs, and is involved in many biological processes such as embryonic development, nervous system development, and extracellular matrix interactions. Compared with other aGPCRs proteins, GPR126 has a longer N-terminal domain, which can bind to ligands one-to-one and one-to-many. Its N-terminus contains five domains, a CUB (complement C1r/C1s, Uegf, Bmp1) domain, a PTX (Pentraxin) domain, a SEA (Sperm protein, Enterokinase, and Agrin) domain, a hormone binding (HormR) domain, and a conserved GAIN domain. The GAIN domain has a self-shearing function, which is essential for the maturation, stability, transport and function of aGPCRs. Different SEA domains constitute different GPR126 isomers, which can regulate the activation and closure of downstream signaling pathways through conformational changes. GPR126 has a typical aGPCRs seven-transmembrane helical structure, which can be coupled to Gs and Gi, causing cAMP to up- or down-regulation, mediating transmembrane signaling and participating in the regulation of cell proliferation, differentiation and migration. GPR126 is activated in a tethered-stalk peptide agonism or orthosteric agonism, which is mainly manifested by self-proteolysis or conformational changes in the GAIN domain, which mediates the rapid activation or closure of downstream pathways by tethered agonists. In addition to the tethered short stem peptide activation mode, GPR126 also has another allosteric agonism or tunable agonism mode, which is specifically expressed as the GAIN domain does not have self-shearing function in the physiological state, NTF and CTF always maintain the binding state, and the NTF binds to the ligand to cause conformational changes of the receptor, which somehow transmits signals to the GAIN domain in a spatial structure. The GAIN domain can cause the 7TM domain to produce an activated or inhibited signal for signal transduction, For example, type IV collagen interacts with the CUB and PTX domains of GPR126 to activate GPR126 downstream signal transduction. GPR126 has homology of 51.6%-86.9% among different species, with 10 conserved regions between different species, which can be traced back to the oldest metazoans as well as unicellular animals.In terms of diseases, GPR126 dysfunction involves the pathological process of bone, myelin, embryo and other related diseases, and is also closely related to the occurrence and development of malignant tumors such as breast cancer and colon cancer. However, the biological function of GPR126 in various diseases and its potential as a therapeutic target still needs further research. This paper focuses on the structure, interspecies differences and conservatism, signal transduction and biological functions of GPR126, which provides ideas and references for future research on GPR126.

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 analyze the relationship between 24 h movement behaviors and cognitive function in children and adolescents, as well as the isotemporal substitution benefits, in order to provide a basis for developing cognitive development intervention strategies among children and adolescents. Methods: Relevant studies were searched in the Web of Science, PubMed, Embase, EBSCO, and China National Knowledge Infrastructure databases from their inception to November 30, 2024. Systematic evaluation was performed after document screening, data extraction and quality assessment. Results: A total of 24 highquality studies were included, comprising 35 295 children and adolescents aged 3-18 years. Adhering to the 24 h activity guidelines was associated with better cognitive performance (19 studies). Additionally, substituting 5-30 minutes per day of moderate to vigorous physical activity (MVPA) or sleep (SLP) for sedentary behavior (SB) or light physical activity (LPA) were associated with improvements in cognitive function (7 studies). There were inconsistencies in the effects of different types of SB (learning or entertainment) on cognitive function. Conclusions Adherence to the 24 h activity guidelines supports cognitive development in children and adolescents, with MVPA and SLP as key intervention targets. Increasing the proportion of MVPA, ensuring adequate SLP, and limiting recreational SB and screen time might be helpful to enhance the combined benefits of these three behaviors.

ObjectiveTo evaluate the clinical efficacy and safety of Qidan Tangshen Granules （芪丹糖肾颗粒， QTG） in the treatment of early diabetic kidney disease （DKD） with qi deficiency， blood stasis， and kidney deficiency syndrome， and to explore its mechanism. MethodsA double-blind， double-simulated method was used to enroll 200 patients with early DKD and qi deficiency， blood stasis， and kidney deficiency syndrome. Patients were randomly assigned in a 1∶1 ratio to the treatment group （100 cases） and the control group （100 cases）. The treatment group received QTG plus a valsartan capsule simulant， while the control group received valsartan capsules plus a QTG simulant， both for 12 weeks. The primary outcome was the urinary albumin-to-creatinine ratio （UACR）. Secondary outcomes included estimated glomerular filtration rate （eGFR）， fasting blood glucose （FBG）， 2-hour postprandial blood glucose （PBG）， glycated hemoglobin （HbA1c）， and traditional Chinese medicine （TCM） syndrome scores （including individual symptom scores for fatigue， dull complexion， soreness and weakness of the waist and knees， headache and chest pain， irritability， spontaneous sweating， thirst and polydipsia， polyphagia， polyuria， numbness of the limbs， and the total TCM syndrome score）. Oxidative stress markers including serum 8-hydroxy-2'-deoxyguanosine （8-OHDG）， 3-nitrotyrosine （3-NT）， and superoxide dismutase （SOD） were also assessed. Clinical efficacy and TCM syndrome efficacy were evaluated after treatment， and routine blood tests， urinalysis， and liver function tests were conducted and adverse reaction during the tria was recorded to assess safety. ResultsA total of 191 patients completed the study （95 in the treatment group and 96 in the control group）. The treatment group showed significant reductions in UACR， FBG， PBG， and HbA1c levels after treatment （P＜0.05 or P＜0.01）. The single TCM symptom scores except for polyphagia and total TCM syndrome scores significantly decreased （P＜0.05 or P＜0.01）. Compared to the control group， the treatment group had signi-ficantly lower UACR， FBG， PBG levels， and total TCM syndrome scores， sinlge symptoms scores except for polyphagia and limb numbness （P＜0.05 or P＜0.01）. Among 40 randomly selected patients （21 cases in the treatment group and 19 cases in the control group） for oxidative stress analysis， there were no significant differences in SOD， 3-NT， and 8-OHDG levels before and after treatment within or between groups （P＞0.05）. The overall effective rate in the treatment group was 64.2% （61/95） and 39.6% （38/96） in the control group， while the TCM syndrome efficacy rates were 80.0% （76/95） and 24.0% （23/96）， respectively， with the treatment group showing superior efficacy （P＜0.01）. No significant differences were observed in routine blood tests， urinalysis， or liver function indices before and after treatment in either group （P＞0.05）. The incidence of adverse reactions was 8.4% （8/95） in the treatment group and 9.4% （9/96） in the control group， with no statistically significant difference （P＞0.05）. ConclusionQTG can effectively reduce UACR and blood glucose levels， alleviate clinical symptoms， and improve clinical efficacy in patients with early DKD with qi deficiency， blood stasis， and kidney deficiency syndrome. The treatment is well-tolerated and safe， with no significant impact on oxidative stress markers.

Objective: To assess the awareness and associated factors of wild mushroom poisoning among students in Guizhou Province, so as to provide a scientific foundation for wild mushroom poisoning prevention and control among students. Methods: By a multi stage stratified cluster random sampling method, 1 162 students from Guizhou Province were selected in May 2024. The questionnaire survey was administered to evaluate knowledge regarding wild mushroom poisoning. Data were analyzed employing the χ 2 test and Logistic regression model. Results: Among the nine questions assessing awareness of wild mushroom poisoning, only three had the awareness rate exceeding 70%. Binary Logistic regression analysis revealed that students who "actively learn about the prevention of wild mushroom poisoning" ( OR=0.48, 95%CI =0.26-0.92) and "spread knowledge about wild mushroom poisoning to others" ( OR=0.47, 95%CI =0.33-0.69) scored higher on the wild mushroom poisoning knowledge questions ( P <0.05). Conversely, students with a habit of consuming wild mushrooms ( OR=1.52, 95%CI =1.15-2.02) scored lower ( P < 0.05 ). 42.3% of the students suggested that scientific dissemination and publicity about wild mushrooms should be intensified. Conclusions The awareness rate of wild mushroom poisoning knowledge among students in Guizhou Province requires further attention. Comprehensive knowledge should be disseminated systematically through various channels to further improve students awareness of the prevention and control of wild mushroom poisoning.

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.