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 assess the safety and topical efficacy of prim-O-glucosylcimifugin (POG) and investigate the molecular mechanisms of its therapeutic effects in atopic dermatitis (AD). METHODS: The effects of POG on human keratinocyte cell viability and its anti-inflammatory properties were evaluated using cell counting kit-8 assay and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Subsequently, the impact of POG on the differentiation of cluster of differentiation (CD) 4⁺ T cell subsets, including T-helper type (Th) 1, Th2, Th17, and regulatory T (Treg), was examined through in vitro experiments. Network pharmacology analysis was used to elucidate POG's therapeutic mechanisms. Furthermore, the therapeutic potential of topically applied POG was further evaluated in a calcipotriol-induced mouse model of AD. The protein and transcript levels of inflammatory markers, including cytokines, lymphocyte-specific protein tyrosine kinase (Lck) mRNA, and LCK phosphorylation (p-LCK), were quantified using immunohistochemistry, RT-qPCR, and Western blot analysis. RESULTS: POG was able to suppress cell proliferation and downregulate the transcription of interleukin 4 (Il4) and Il13 mRNA. In vitro experiments indicated that POG significantly inhibited the differentiation of Th2 cells, whereas it exerted negligible influence on the differentiation of Th1, Th17 and Treg cells. Network pharmacology identified LCK as a key therapeutic target of POG. Moreover, the topical application of POG effectively alleviated skin lesions in the calcipotriol-induced AD mouse models without causing pathological changes in the liver, kidney or spleen tissues. POG significantly reduced the levels of Il4, Il5, Il13, and thymic stromal lymphopoietin (Tslp) mRNA in the AD mice. Concurrently, POG enhanced the expression of p-LCK protein and Lck mRNA. CONCLUSION Our research revealed that POG inhibits Th2 cell differentiation by promoting p-LCK protein expression and hence effectively alleviates AD-related skin inflammation. Please cite this article as: Zhao H, Ma X, Wang H, Ding XJ, Kuai L, Song JK, Zhang Z, Yang D, Gao CJ, Li B, Zhou M. Prim-O-glucosylcimifugin mitigates atopic dermatitis by inhibiting Th2 differentiation through LCK phosphorylation modulation. J Integr Med. 2025; 23(3): 309-319.
Dermatitis, Atopic/drug therapy* ; Animals ; Humans ; Cell Differentiation/drug effects* ; Phosphorylation/drug effects* ; Mice ; Th2 Cells/drug effects* ; Keratinocytes/drug effects* ; Disease Models, Animal ; Mice, Inbred BALB C ; Calcitriol/analogs & derivatives*

Saponins associated with Panax notoginseng (P. notoginseng) demonstrate significant therapeutic efficacy across multiple diseases. However, certain high-yield saponins face limited clinical applications due to their reduced pharmacological efficacy. This study synthesized and evaluated 36 saponin-1,2,3-triazole derivatives of ginsenosides Rg1/Rb1 and notoginsenoside R1 for anti-adipogenesis activity in vitro. The research revealed that the ginsenosides Rg1-1,2,3-triazole derivative a17 demonstrates superior adipogenesis inhibitory effects. Structure-activity relationships (SARs) analysis indicates that incorporating an amidyl-substituted 1,2,3-triazole into the saponin side chain via Click reaction enhances anti-adipogenesis activity. Additionally, several other derivatives exhibit general adipogenesis inhibition. Compound a17 demonstrated enhanced potency compared to the parent ginsenoside Rg1. Mechanistic investigations revealed that a17 exhibits dose-dependent inhibition of adipogenesis in vitro, accompanied by decreased expression of preadipocytes. Peroxisome proliferator-activated receptor γ (PPARγ), fatty acid synthase (FAS), and fatty acid binding protein 4 (FABP4) adipogenesis regulators. These findings establish the ginsenoside Rg1-1,2,3-triazole derivative a17 as a promising adipocyte differentiation inhibitor and potential therapeutic agent for obesity and associated metabolic disorders. This research provides a foundation for developing effective therapeutic approaches for various metabolic syndromes.
Adipogenesis/drug effects* ; Triazoles/chemical synthesis* ; Ginsenosides/chemical synthesis* ; Saponins/chemical synthesis* ; Animals ; Mice ; Structure-Activity Relationship ; PPAR gamma/genetics* ; 3T3-L1 Cells ; Adipocytes/metabolism* ; Panax notoginseng/chemistry* ; Drug Design ; Molecular Structure ; Humans ; Cell Differentiation/drug effects* ; Fatty Acid-Binding Proteins/genetics*

OBJECTIVE: This study aimed to investigate possible serum 25-hydroxyvitamin D [25(OH)D] cutoffs for the associations between 25(OH)D and Bone turnover markers (BTMs), and how GC gene variation influences such cutoffs in Chinese women of childbearing age. METHODS: In total, 1,505 non-pregnant or non-lactating women (18-45 years) were recruited from the 2015 Chinese Adult Chronic Disease and Nutrition Surveillance. Serum 25(OH)D, osteocalcin (OC), procollagen type 1 N-terminal propeptide (P1NP), β-CrossLaps of type 1 collagen containing cross-linked C-telopeptide (β-CTX), and single nucleotide polymorphisms were determined. Locally weighted regression and smoothing scatterplot and segmented regression were performed to estimate the 25(OH)D thresholds. RESULTS: The median serum 25(OH)D was 16.63 (11.96-22.55) ng/mL and the prevalence of low serum 25(OH)D (< 12 ng/mL) was 25.2%. Women with the lowest 25(OH)D had the highest β-CTX. After adjustment for the confounders, 25(OH)D cutoffs for OC [14.04 (12.84-15.23) ng/mL], β-CTX [13.94 (12.49-15.39) ng/mL], and P1NP [13.87 (12.37-15.37) ng/mL] in the whole population, cutoffs for OC [12.30 (10.68-13.91) ng/mL], β-CTX [12.23 (10.22-14.23) ng/mL], and P1NP [11.85 (10.40-13.31) ng/mL] in women with the GC rs2282679 G allele, and cutoffs for OC [12.75 (11.81-13.68) ng/mL], β-CTX [13.05 (11.78-14.32) ng/mL], and P1NP [12.81 (11.57-14.06) ng/mL] in women with the GC rs2282679 T allele, were observed. Below these cutoffs, BTMs were negatively associated with 25(OH)D, while above these cutoffs, BTMs plateaued. CONCLUSION In Chinese women of childbearing age, there were thresholds effect of serum 25(OH)D concentrations on BTMs. The results indicated that serum 25(OH)D concentrations < 13.87 ng/mL in this population had adverse influences on maintaining bone remodeling. BTMs were suppressed at a relatively lower serum 25(OH)D in women with the GC rs2282679 G allele compared with those with the T allele.
Humans ; Female ; Vitamin D/blood* ; Adult ; Middle Aged ; Polymorphism, Single Nucleotide ; Adolescent ; Young Adult ; China ; Biomarkers/blood* ; Bone Remodeling/genetics* ; Vitamin D-Binding Protein/genetics* ; Procollagen/blood* ; Osteocalcin/blood* ; Peptide Fragments/blood* ; East Asian People

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.

Objective To understand the changing composition and antibiotic resistance of bacterial species in the clinical isolates from outpatient and emergency department(hereinafter referred to as outpatients)and inpatient children over time in various hospitals,and to provide laboratory evidence for rational antibiotic use.Methods The data on clinically isolated pathogenic bacteria and antimicrobial susceptibility of isolates from outpatients and inpatient children in the CHINET program from 2015 to 2021 were collected and analyzed.Results A total of 278 471 isolates were isolated from pediatric patients in the CHINET program from 2015 to 2021.About 17.1％of the strains were isolated from outpatients,primarily group A β-hemolytic Streptococcus,Escherichia coli,and Staphylococcus aureus.Most of the strains(82.9％)were isolated from inpatients,mainly SS.aureus,E.coli,and H.influenzae.The prevalence of methicillin-resistant S.aureus(MRSA)in outpatients(24.5％)was lower than that in inpatient children(31.5％).The MRSA isolates from outpatients showed lower resistance rates to the antibiotics tested than the strains isolated from inpatient children.The prevalence of vancomycin-resistant Enterococcus faecalis or E.faecium and penicillin-resistant S.pneumoniae was low in either outpatients or inpatient children.S.pneumoniae,β-hemolytic Streptococcus and S.viridans showed high resistance rates to erythromycin.The prevalence of erythromycin-resistant group A β-hemolytic Streptococcus was higher in outpatients than that in inpatient children.The prevalence of β-lactamase-producing H.influenzae showed an overall upward trend in children,but lower in outpatients(45.1％)than in inpatient children(59.4％).The prevalence of carbapenem-resistant Klebsiella pneumoniae(CRKpn),carbapenem-resistant Pseudomonas aeruginosa(CRPae)and carbapenem-resistant Acinetobacter baumannii(CRAba)was 14％,11.7％,47.8％in outpatients,but 24.2％,20.6％,and 52.8％in inpatient children,respectively.The prevalence of multidrug-resistant E.coli,K.pneumoniae,Proteus mirabilis,P.aeruginosa and A.baumannii strains was lower in outpatients than in inpatient children.The prevalence of fluoroquinolone-resistant E.coli,ESBLs-producing K.pneumoniae,ESBLs-producing P.mirabilis,carbapenem-resistant E.coli(CREco),CRKpn,and CRPae was lower in children in outpatients than in inpatient children,but the prevalence of CRAba in 2021 was higher than in inpatient children.Conclusions The distribution of clinical isolates from children is different between outpatients and inpatients.The prevalence of MRSA,ESBL,and CRO was higher in inpatient children than in outpatients.Antibiotics should be used rationally in clinical practice based on etiological diagnosis and antimicrobial susceptibility test results.Ongoing antimicrobial resistance surveillance and prevention and control of hospital infections are crucial to curbing bacterial resistance.