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*

Objective To investigate the high-risk factors associated with acute postoperative hypertension (APH) following laparoscopic sleeve gastrectomy(LSG) in obese patients and to establish a predictive model. Methods A retrospective analysis was conducted on clinical data and laboratory parameters of obese patients who underwent LSG at Department of Metabolic Surgery in our hospital from August 2021 to December 2023. Logistic-LASSO regression analysis was used to identify independent risk factors for APH. A nomogram predictive model was developed based on these factors. The predictive performance and clinical utility of the model were assessed using the receiver operating characteristic (ROC) curve, Bootstrap resampling, calibration curve, Hosmer-Lemeshow (H-L) test, decision curve analysis (DCA), and clinical impact curve (CIC). Results The incidence of APH was 55.90%. Body mass index (BMI), platelet count, globulin, uric acid, sodium, fibrinogen, fasting blood glucose, and preoperative diastolic pressure had potential predictive value. Among them, BMI (OR=1.066, 95% CI: 1.003-1.137, P=0.046), platelet count (OR=0.994, 95% CI: 0.998-0.999, P=0.027), fibrinogen (OR=1.943, 95% CI: 1.128-3.479, P=0.02), and preoperative diastolic blood pressure (OR=0.953, 95% CI: 0.918-0.985, P = 0.006) were identified as independent high-risk factors. The area under the curve (AUC) of the nomogram was 0.783 (95% CI: 0.711-0.855), with a sensitivity of 0.817 and a specificity of 0.689. The AUC based on Bootstrap resampling was 0.776 (95% CI: 0.702-0.849). The H-L test yielded P>0.05, and the calibration curve showed good model fit. Both DCA and CIC demonstrated favorable screening efficiency. Conclusions BMI, platelet count, fibrinogen, and preoperative diastolic blood pressure are independent high-risk factors for APH following LSG. The developed nomogram model exhibits good predictive performance and clinical applicability, providing a valuable tool for early screening and prevention of APH in LSG patients.

Salmonella enterica serovar Goldcoast isolates from Yangzhou were analyzed for assessment of their pathogenic and ge-nomic characteristics.From 2017 to 2023,eight strains were detected through foodborne disease surveillance and health check-ups.These strains exhibited resistance to two to seven antibiotic classes,and all strains bore a T57S mutation in the parC gene.Notably,50％(4/8)of the strains carried sulfonamide resistance genes(sul1 or sul3),and 37.5％(3/8)were positive for extended-spectrum beta-lactamase(ESBL)genes,including blaCTX-M-55 and blaTEM-1.Multilocus sequence typing(MLST)revealed that ST358(five strains)and ST2529(three strains)were the predominant sequence types,corresponding to CRISPR Ⅰ and Ⅱ groups,respectively.Single nucleotide polymorphism(SNP)phylogenetic analysis indicated a close genetic relationship between the case isolate Sal-2214 and strains from multiple regions,with a SNP difference≤20,thus suggesting potential inter-regional transmission.For example,one isolate from the stool of a 4-year-old child with diarrhea(Sal-2214)carried the beta-lactam resistance gene(blaCTX-M-55)and exhib-ited high resistance to cefotaxime(CTX)and ceftazidime(CAZ).These findings indicated that S.enterica Goldcoast emerged in Yang-zhou from 2022,and was characterized by multidrug resistance and a genetic relationship with strains from coastal regions.These find-ings highlight the need for enhanced resistance monitoring and trace-back studies to better understand the epidemiology and control the potential spread of this pathogen.

Objective:To explore the clinical and biochemical characteristics of patients with hyperuricemia according to different pathophysiological subtypes. This may facilitate rapid identification of each subtype in clinical settings and provide evidence for personalized urate-lowering treatment.Methods:Patients diagnosed with hyperuricemia at the Department of Endocrinology and Metabolism, Tenth People′s Hospital of Tongji University between October 2015 and January 2024 were included. Based on 24-h urinary uric acid excretion(UUE) and the fractional excretion of uric acid(FEUA), patients were classified into four subtypes: renal uric acid underexcretion type(RUE), renal uric acid overload type(ROL), combined type and renal normal type. Clinical and biochemical variables-including sex, age, BMI, smoking history, comorbidities, blood glucose, and serum uric acid-were analyzed. Binary logistic regression was used to identify factors associated with each subtype.Results:Among 2 073 patients with hyperuricemia, 55.8% were RUE type, 6.9% were ROL type, 31.3% were combined type and 6.0% were renal normal type. RUE type had lower blood glucose levels and fewer cases of diabetes [ OR=0.685(95% CI 0.478-0.980), P<0.05]. ROL type showed a higher incidence of tophi, positively correlated with smoking history [ OR=1.672(95% CI 1.009-2.771), P<0.05], and negatively correlated with serum uric acid levels [ OR=0.994(95% CI 0.990-0.998), P=0.001]. Combined type had the youngest onset age, shortest disease duration, and the fewest comorbidities, and was associated with higher BMI [ OR=1.035(95% CI 1.001-1.070), P<0.05]. Renal normal type had the oldest age of onset, the highest proportion of female patients and comorbidities, and was associated with lower serum uric acid levels[ OR=0.994(95% CI 0.989-0.998), P=0.007], higher BMI[ OR=1.064(95% CI 1.003-1.129), P<0.05], and increased tophi incidence[ OR=2.261(95% CI 1.206-4.237), P=0.011]. Conclusion:Each pathophysiological subtype of hyperuricemia exhibits distinct clinical and biochemical characteristics, which may serve as useful references for subtype identification and personalized management in clinical practice.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

Salmonella enterica serovar Goldcoast isolates from Yangzhou were analyzed for assessment of their pathogenic and ge-nomic characteristics.From 2017 to 2023,eight strains were detected through foodborne disease surveillance and health check-ups.These strains exhibited resistance to two to seven antibiotic classes,and all strains bore a T57S mutation in the parC gene.Notably,50％(4/8)of the strains carried sulfonamide resistance genes(sul1 or sul3),and 37.5％(3/8)were positive for extended-spectrum beta-lactamase(ESBL)genes,including blaCTX-M-55 and blaTEM-1.Multilocus sequence typing(MLST)revealed that ST358(five strains)and ST2529(three strains)were the predominant sequence types,corresponding to CRISPR Ⅰ and Ⅱ groups,respectively.Single nucleotide polymorphism(SNP)phylogenetic analysis indicated a close genetic relationship between the case isolate Sal-2214 and strains from multiple regions,with a SNP difference≤20,thus suggesting potential inter-regional transmission.For example,one isolate from the stool of a 4-year-old child with diarrhea(Sal-2214)carried the beta-lactam resistance gene(blaCTX-M-55)and exhib-ited high resistance to cefotaxime(CTX)and ceftazidime(CAZ).These findings indicated that S.enterica Goldcoast emerged in Yang-zhou from 2022,and was characterized by multidrug resistance and a genetic relationship with strains from coastal regions.These find-ings highlight the need for enhanced resistance monitoring and trace-back studies to better understand the epidemiology and control the potential spread of this pathogen.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

Objective:To explore the clinical and biochemical characteristics of patients with hyperuricemia according to different pathophysiological subtypes. This may facilitate rapid identification of each subtype in clinical settings and provide evidence for personalized urate-lowering treatment.Methods:Patients diagnosed with hyperuricemia at the Department of Endocrinology and Metabolism, Tenth People′s Hospital of Tongji University between October 2015 and January 2024 were included. Based on 24-h urinary uric acid excretion(UUE) and the fractional excretion of uric acid(FEUA), patients were classified into four subtypes: renal uric acid underexcretion type(RUE), renal uric acid overload type(ROL), combined type and renal normal type. Clinical and biochemical variables-including sex, age, BMI, smoking history, comorbidities, blood glucose, and serum uric acid-were analyzed. Binary logistic regression was used to identify factors associated with each subtype.Results:Among 2 073 patients with hyperuricemia, 55.8% were RUE type, 6.9% were ROL type, 31.3% were combined type and 6.0% were renal normal type. RUE type had lower blood glucose levels and fewer cases of diabetes [ OR=0.685(95% CI 0.478-0.980), P<0.05]. ROL type showed a higher incidence of tophi, positively correlated with smoking history [ OR=1.672(95% CI 1.009-2.771), P<0.05], and negatively correlated with serum uric acid levels [ OR=0.994(95% CI 0.990-0.998), P=0.001]. Combined type had the youngest onset age, shortest disease duration, and the fewest comorbidities, and was associated with higher BMI [ OR=1.035(95% CI 1.001-1.070), P<0.05]. Renal normal type had the oldest age of onset, the highest proportion of female patients and comorbidities, and was associated with lower serum uric acid levels[ OR=0.994(95% CI 0.989-0.998), P=0.007], higher BMI[ OR=1.064(95% CI 1.003-1.129), P<0.05], and increased tophi incidence[ OR=2.261(95% CI 1.206-4.237), P=0.011]. Conclusion:Each pathophysiological subtype of hyperuricemia exhibits distinct clinical and biochemical characteristics, which may serve as useful references for subtype identification and personalized management in clinical practice.

Background: Depression, anxiety, and stress have become major public health concerns among adolescents, particularly in the aftermath of the COVID-19 pandemic. Understanding their prevalence and determinants is essential to inform prevention and intervention strategies. Objectives: To describe the prevalence and associated factors of depression, anxiety, and stress among adolescents from 2022 to 2023. Methods: This is a scoping review. We synthesize scientific studies on depression, anxiety, stress in adolescents aged 10-24 years published in journals from three data bases: PubMed, Cochrane, Embase. Results: A total of 34 studies on depression, anxiety, and stress in adolescents were included in the review. This scoping review indicates that prevalence of depression ranged from 12.5% to 90.9%, that of anxiety was between 15.2% and 74.7%, and that of stress ranged from 3.2% to 75.3%. The mean age of participants across studies ranged from 14.5 to 21.9 years. Several factors have been identified in different studies as exacerbating these symptoms among adolescents, including age, gender, education related factors, family background, and risk behaviors. Conclusion: The findings of this review provide valuable insights for parents and health professionals to design effective strategies that mitigate mental health symptoms and foster adolescents’ healthy development.