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.

G protein-coupled receptor (GPR) 40, as one of GPRs family, plays a potential role in regulating glucose and lipid metabolism. To study the effect of GPR40 novel agonist SZZ15-11 on hyperglycemia and hyperlipidemia and its potential mechanism, spontaneous type 2 diabetic KKA^y mice, human hepatocellular carcinoma HepG2 cells and murine mature adipocyte 3T3-L1 cells were used. KKA^y mice were divided into four groups, vehicle group, TAK group, SZZ (50 mg·kg^-1) group and SZZ (100 mg·kg^-1) group, with oral gavage of 0.5% sodium carboxymethylcellulose (CMC), 50 mg·kg^-1 TAK875, 50 and 100 mg·kg^-1 SZZ15-11 respectively for 45 days. Fasting blood glucose, blood triglyceride (TG) and total cholesterol (TC), non-fasting blood glucose were tested. Oral glucose tolerance test and insulin tolerance test were executed. Blood insulin and glucagon were measured via enzyme-linked immunosorbent assay (ELISA). After mice′s execution, liver tissue was harvested to test TG and TC content. Then pathological morphology of liver was observed through hematoxylin-eosin (HE) staining, and the lipid metabolism relative signal pathway was analyzed by Western blot and RT-PCR. The experiments were approved by the Institutional Animal Care and Use Committee of the Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College. At the same time, Akt phosphorylation level in HepG2 cells and adiponectin in 3T3-L1 cells treated with TNFα were measured with Western blot. The results show that SZZ15-11 not only decreased blood glucose and lipid, improved insulin sensitivity, but also increased fasting blood glucagon and promoted insulin secretion after glucose loading in KKA^y mice. Additionally, SZZ15-11 alleviated hepatic steatosis and liver dysfunction in KKA^y mice. In liver tissue, SZZ15-11 increased AMPKα phosphorylation level and cholesterol metabolism relative gene Abcg8 transcription. In HepG2 cells, SZZ15-11 increased Akt phosphorylation level. In adipocyte 3T3-L1, SZZ15-11 recovered the decreased adiponectin expression by TNFα. This study proved that GPR40 agonist SZZ15-11 could be a candidate compound for regulating glucolipid metabolic disorder.

Cryoablation therapy with explicit anti-tumor mechanisms and histopathological manifestations has a long history.A large number of clinical practice has shown that cryoablation therapy is safe and effective,making it an ideal tumor treatment method in theory.Previously,its efficacy and clinical application were constrained by the limitations of refrigerants and refrigeration equipment.With the development of the new generation of cryoablation equipment represented by argon helium knives,significant progress has been made in refrigeration efficien-cy,ablation range,and precise temperature measurement,greatly promoting the progression of tumor cryoablation technology.This consensus systematically summarizes the mechanism of cryoablation technology,indications for oral mucosal melanoma(OMM)cryotherapy,clinical treatment process,adverse reactions and management,cryotherapy combination therapy,etc.,aiming to provide reference for carrying out the standardized cryoablation therapy of OMM.

Lung cancer is the leading type of cancer death, and most patients with lung cancer are diagnosed at an advanced stage and have a very poor prognosis. Although low-dose computed tomography (LDCT) has entered the clinic as a screening tool for lung cancer, its false-positive rate is more than 90%. As one of the epigenetic modifications of research hotspots, DNA methylation plays a key role in a variety of diseases, including cancer.Hypermethylation of tumor suppressor genes and hypomethylation of proto-oncogenes are important events in tumorigenesis and development. Therefore, DNA methylation analysis can provide some useful information for the early screening, diagnosis, treatment and prognosis of lung cancer. Although invasive methods such as tissue biopsy remain the gold standard for tumor diagnosis and monitoring, they also have limitations such as inconvenience in sampling. In recent years, there has been a rapid development of liquid biopsy, which can detect primary or metastatic malignancies and reflect the heterogeneity of tumors. In addition, the blood sample can be collected in a minimally invasive or non-invasive format and is well tolerated in older and frail patients. This article explores some of the emerging technologies for DNA methylation analysis and provides an overview of the application of DNA methylation in the diagnosis and treatment of lung cancer.

R-spondin2 (Rspo2) is a member of protein family RSPOs, which can be coupled to receptor 4/5 (leucine-rich repeat-containing g protein-coupled receptor 4/5, LGR4/5), cell surface transmembrane E3 ubiquitin ligase ZNRF3/RNF43 (zinc and ring finger 3/ring finger protein 43), heparan sulfate proteoglycan (heparan sulfate proteoglycans, HSPGs) and the IQ motif (IQ gap 1) containing GTP enzyme activating protein 1, regulating the Wnt/β-catenin signaling pathway, which is the most widely studied signaling pathway and directly related to basic bone biology. Any problem in this pathway may have an impact on bone regulation. In recent years, it has been found that Rspo2 can act on osteoblast, osteoclast and chondrocytes through Wnt/β-catenin, and take part in occureace and development of some bone diseases such as ossification of the posterior longitudinal ligament (OPLL), osteoarthritis (OA) and rheumatoid arthritis (RA), so the study of Rspo2 may become a new therapeutic direction for bone-related diseases. Based on the latest research progress, this paper reviews the structure and main functions of Rspo2, the mechanism of Rspo2 regulating Wnt/β-catenin signaling pathway and its influence on skeletal system, in order to provide new ideas and ways for the prevention and treatment of bone-related diseases.

BACKGROUND:Many clinical research observations have indicated a close association between rheumatoid arthritis and osteoporosis as well as bone mineral density(BMD).However,it remains unclear whether there is a causal genetic relationship between rheumatoid arthritis and the development of osteoporosis and alterations of BMD. OBJECTIVE:To assess the potential causal relationship between rheumatoid arthritis and osteoporosis as well as BMD using a two-sample Mendelian randomization approach,provide meaningful insights from a genetic perspective into the underlying mechanisms and offer a reference for early prevention of osteoporosis and improvement in the progression of the disease. METHODS:We conducted a study using data from publicly available genome-wide association studies databases to identify single nucleotide polymorphisms associated with rheumatoid arthritis as instrumental variables(P＜5×10-8).The main outcomes of the study included osteoporosis and BMD at five different sites,including total body BMD,lumbar spine BMD,femoral neck BMD,heel BMD,and forearm BMD.The inverse variance-weighted method was used as the primary analysis method to evaluate causal effects.Weighted median,simple median,weighted mode and MR-Egger regression were used as supplementary analyses.Causal relationships between rheumatoid arthritis and the risk of osteoporosis and BMD were assessed using odds ratios(OR)and 95%confidence intervals(CI).Heterogeneity was assessed using Cochran's Q test and horizontal pleiotropy was evaluated using MR-Egger intercept tests. RESULTS AND CONCLUSION:The inverse variance-weighted analysis demonstrated a positive association between genetically predicted rheumatoid arthritis and osteoporosis(OR=1.123,95%CI:1.077-1.171;P=4.02×10-8).Heterogeneity test(P=0.388)indicated no significant heterogeneity among the single nucleotide polymorphisms.MR-Egger intercept(P=0.571)tests did not detect horizontal pleiotropy,and sensitivity analysis showed no evidence of bias in the study results.There was no causal relationship between rheumatoid arthritis and BMD at the five different sites.The total body BMD(OR=1.000,95%CI:0.988-1.012;P=0.925),lumbar spine BMD(OR=0.999,95%CI:0.982-1.016;P=0.937),femoral neck BMD(OR=1.001,95%CI:0.986-1.016;P=0.866),heel BMD(OR=0.996,95%CI:0.989-1.004;P=0.419),and forearm BMD(OR=1.063,95%CI:0.970-1.031;P=0.996)indicated no significant association.MR-Egger intercept analysis did not detect potential horizontal pleiotropy(total body BMD:P=0.253;lumbar spine BMD:P=0.638;femoral neck BMD:P=0.553;heel BMD:P=0.444;forearm BMD:P=0.079).Rheumatoid arthritis may contribute to the development of osteoporosis through the interaction between chronic inflammation and bone formation,resorption,and absorption.Additionally,the use of glucocorticoids and the presence of autoantibodies(such as anti-citrullinated protein antibody)in patients with rheumatoid arthritis showed associations with osteoporosis.Future research should focus on monitoring systemic inflammatory markers,standardized use of glucocorticoids,and regular screening for osteoporosis risk in patients with rheumatoid arthritis.