Stroke is the leading cause of death and disability among adults in China， and its common complications include digestive system abnormalities， cognitive impairment， depression， stroke-associated pneumonia， and hemiplegia. The combination of traditional Chinese and Western medicine has great potential in treating post-stroke complications. Xinglou Chengqitang （XLCQT） is a representative prescription of alleviating the disease in the upper part by treating the lower part. It has definite therapeutic effect and high safety. Clinically， XLCQT is often used to treat stroke and its complications. However， the quantity and quality of clinical trials of XLCQT in treating post-stroke complications need to be improved. Additionally， since the basic research is weak， the material basis and multi-target mechanism for the efficacy of this prescription are unknown. This article reviews XLCQT in terms of the pharmacodynamic basis， medicinal properties， safety evaluation， and progress in clinical research and mechanisms in treating post-stroke complications. This article summarizes 22 key active ingredients of XLCQT in treating acute stroke complicated with syndrome of phlegm heat and fu-organ excess. Among these key active ingredients， resveratrol， kaempferol， luteolin， chrysoeriol， apigenin， （+）-catechin， and adenosine have good pharmacokinetic properties and high bioavailability. The mechanisms of XLCQT in treating post-stroke complications are complex， including inflammatory response， brain-gut axis， hypothalamic-pituitary-adrenal （HPA） axis， intestinal flora， neurotrophic factors， autophagy， oxidative stress， and free radical damage. This review helps to deeply understand the pharmacodynamic basis and mechanisms of XLCQT in treating post-stroke complications and provides a theoretical basis for the clinical application of XLCQT against post-stroke complications and the development of drugs.

Based on previous research on the promoting effect of dendrobium officinale polysaccharides (DOP) on hair growth, this study aimed to regulate the skin keratin penetration and hair follicle targeting ability of DOP through molecular weight and nano-carriers to enhance its therapeutic effect on androgenetic alopecia (AGA). Three molecular weight polysaccharides, namely high (DOP), medium (MDOP), and low (LDOP), were prepared by mannanase hydrolysis, and the corresponding liposomes (DOP-lip/MDOP-lip/LDOP-lip) were constructed. Studies have shown that DOP liposomes can effectively achieve follicular targeted delivery and promote efficient uptake by human dermal papilla cells through caveolin-mediated pathways. In the testosterone-induced AGA mouse model, LDOP-lip demonstrated excellent therapeutic effects, restoring the number and morphology of hair follicles to nearly normal levels. In summary, DOP liposomes show significant potential for promoting hair follicle repair through precise delivery and efficient cellular uptake.

Mesangial cell proliferation is an early pathological indicator of diabetic nephropathy (DN). Growing evidence highlights the pivotal role of paired-related homeobox 1 (Prrx1), a key regulator of cellular proliferation and tissue differentiation, in various disease pathogenesis. Notably, Prrx1 is highly expressed in mesangial cells under DN conditions. Both in vitro and in vivo studies have demonstrated that Prrx1 overexpression promotes mesangial cell proliferation and contributes to renal fibrosis in db/m mice. Conversely, Prrx1 knockdown markedly suppresses hyperglycemia-induced mesangial cell proliferation and mitigates renal fibrosis in db/db mice. Mechanistically, Prrx1 directly interacts with the Yes-associated protein 1 (YAP) promoter, leading to the upregulation of YAP expression. This upregulation promotes mesangial cell proliferation and exacerbates renal fibrosis. These findings emphasize the crucial role of Prrx1 upregulation in high glucose-induced mesangial cell proliferation, ultimately leading to renal fibrosis in DN. Therefore, targeting Prrx1 to downregulate its expression presents a promising therapeutic strategy for treating renal fibrosis associated with DN.

Objective:To construct an evidence-based practice program for dietary management of patients with non-dialysis chronic kidney disease (CKD) based on best evidence and to evaluate the effectiveness of its application.Methods:The best evidence for dietary management of non-dialysis CKD patients was summarized. From September to October 2022, following the evidence clinical transformation model of the Fudan University Centre for Evidence-based Nursing, the best evidence was screened and evidence-based practice program were developed, taking into account patients' wishes, expert opinions, and clinical contexts. From November 2022 through March 2023, baseline reviews, analysis of barriers and facilitators were implemented. Between April 2023 and April 2024, evidence-based practice was carried out in the Department of Nephrology of the Second Affiliated Hospital of Guangzhou Medical University to compare the implementation rate of review indicators at the system, practitioner, and patient levels, and practitioners' knowledge before and after the application of evidence.Results:A total of 14 review indicators were developed. The implementation rate of the 12 review indicators and the practitioners' knowledge of the CKD diet were elevated after the evidence-based practice ( P<0.05) . Conclusions:Evidence-based practice program for dietary management of patients with non-dialysis CKD has a positive effect on improving practitioners' knowledge of non-dialysis CKD diets, implementation rate of dietary management behaviors, and patients' dietary behaviors.

Objective Mendelian randomization(MR)analysis was used to explore the genetic link between immunophenotype and breast cancer(BC)risk and how cerebrospinal fluid(CSF)metabolites play a part in mediating this.Methods We used MR to assess the genetic associations between immune cells and BC risk and their possible mediators.Genetic statistics for immune cells and CSF metabolites were obtained from the Genome-Wide Association Study(GWAS)catalog,whereas those for BC were obtained from the Japan Biobank,the UK Biobank,and FinnGen's cross-ethnic meta-analysis.We performed a two-sample MR analysis using inverse variance weighting(IVW)to investigate the genetic association between immunoepidemiology and BC.We also analyzed CSF metabolites as mediators between them.Heterogeneity was tested using the Cochran's Q statistic,horizontal pleiotropy was tested using the MR Egger intercept,and sensitivity analysis was performed using the"leave-one-out"method.Results MR analysis by the IVW method showed that HLA DR+CD4+T cells were associated with a reduced risk of BC(OR=0.972,95％ CI:0.955-0.990,P=0.003),and there was a negative genetic association between HLA DR+CD4+T cells and methylsuccinimidyl carnitine level(OR=0.922,95％ CI:0.861-0.986,P=0.018),but there was a positive genetic association between the latter and BC risk(OR=1.029,95％ CI:1.012-1.047,P＜0.001).Mediation analysis showed that the direct effect remained significant after correction for CSF methylsuccinylcarnitine level(β=-0.026,SE=0.008,P=0.002).And the indirect effect(β=-0.002,Delta Method SE=0.001)suggested that this CSF metabolite might mediate 8.36％of the association in the protective effect of immune cells against BC risk(95％ CI:-12.4％-29.1％).Conclusion Genetically predicted HLA DR+CD4+T cells may reduce the risk of BC development by modulating the level of methylsuccinylcarnitine,the CSF metabolite.

Objective To observe the value of contrast-enhanced ultrasound(CEUS)for diagnosing malignant adnexal tumors.Methods Totally 112 patients with single adnexal masse were retrospectively enrolled and divided into benign adnexal tumor group(benign group,n=73)and malignant adnexal tumor group(malignant group,n=39).Clinical data,laboratory indicators,ovarian-adnexal ultrasound reporting and data system(O-RADS)classification based on conventional ultrasound(US),CEUS manifestations and CEUS classification of benign and malignant tumors were compared between groups.Multivariable logistic regression analysis of clinical and laboratory indicators being statistically different between groups,as well as US O-RADS classification and CEUS classification was performed to screen the independent predictors of malignant adnexal tumors,and combined models were constructed using forward stepwise regression method.The efficacy of each independent predictor and combined model for diagnosing malignant adnexal tumors was analyzed.Results Statistical differences of carbohydrate antigen 125(CA125),US O-RADS classification,enhancement time and level of CEUS,as well as CEUS classification were found between groups(all P＜0.05).CA125,US O-RADS classification and CEUS classification were all independent predictors of malignant adnexal tumors(all P＜0.05).Combined model Ⅰ,Ⅱ and Ⅲ were constructed based on CA125+CEUS classification,US O-RADS classification+CEUS classification and CA125+US O-RADS classification+CEUS classification,respectively.The area under the curve(AUC)of single CA125 level,US O-RADS classification,CEUS classification and combined model Ⅰ,Ⅱ and Ⅲ for diagnosing malignant adnexal tumor was 0.708,0.809,0.908,0.918,0.945 and 0.954,respectively.AUC of combined model Ⅲ was higher than that of combined model Ⅰ(Z=-2.142,P=0.032),while no significant difference of AUC was found between combined model Ⅱ and Ⅰ nor Ⅱ and Ⅲ(both P＞0.05).Conclusion CEUS could be used to effectively diagnose malignant adnexal tumor.Combining with CA125 level and US O-RADS classification could significantly improve its diagnostic efficacy.

AIM:To explore the effects of Jianpi Huatan formula on regulating T cells and helper T cells 17(Th17)cells in patients with polycystic ova-ry syndrome(PCOS)due to spleen deficiency and phlegm dampness syndrome,and conduct a model evaluation.METHODS:Ninety-two patients with spleen deficiency phlegm dampness syndrome(PCOS)admitted to our hospital from January 2023 to October 2024 were selected as the research sub-jects.Propensity score matching(PSM)method was used to match them in a 1:1 ratio,with 46 pa-tients in each group.The control group received conventional treatment,while the observation group received treatment with Jianpi Huatan for-mula on the basis of the control group.Compared and analyze the differences in clinical data and lab-oratory indicators between two groups;Compared the changes of sex hormone,glucose metabolism and TCM syndrome score before and after treat-ment in the two groups,and focused on the chang-es of regulatory T cells(Treg)and Th17 cells in the two groups before and after treatment;And used the Generalized Estimation Equation(GEE)model to analyze its improvement.Multiple linear regres-sion analysis was used to examine its correlation with the score of traditional Chinese medicine syn-drome.A time effect model of Jianpi Huatan formu-la for treating PCOS with spleen deficiency and phlegm dampness syndrome was established using a nonlinear mixed effects model.The fitting effect of the final model was evaluated through the good-ness of fit.Bootstrap was used to test and evaluate the stability of model parameters.Visual prediction testing was used to evaluate the predictive perfor-mance of the model.Typical time effect curves of traditional Chinese medicine symptom scores was simulated based on the final model for each base-line.RESULTS:After treatment,the total effective rate of the observation group was significantly high-er than that of the control group(χ2=4.842,P=0.028);Compared with before treatment,after 1months and 3 months of treatment,TC,TG,LDL-C,T,LH,FSH,AMH,FPG,FINS,HOMA-IR,the score of traditional Chinese medicine syndrome were sig-nificantly reduced,while E2 and HDL-C were signifi-cantly increased,and the improvement in the ob-servation group was significantly greater than that in the control group(P<0.05);The results of repeat-ed measures ANOVA showed significant difference-sin the time effects,inter group effects,and interac-tion effects of Treg,Th17,and Treg/Th17 between the two groups of patients(P<0.05).The GEE anal-ysis results showed that the improvement of Treg,Th17,and Treg/Th17 in the observation group were better than that in the control group(P<0.05);The results of multiple linear regression analysis showed that the levels of TC,TG,LDL-C,T,LH,FSH,AMH,FPG,FINS,HOMA-IR,Th17 were significantly positively correlated with TCM syndrome score,while the levels of E2,HDL-C,Treg,and Treg/Th17 were significantly negatively correlated with TCM syndrome score(P<0.05);The decrease in tradition-al Chinese medicine symptom score compared to baseline gradually increases over time,eventually reaching the pharmacological platform,which was consistent with the classic Emax model.After gradu-ally screening covariates,it was found that the baseline value of traditional Chinese medicine symptom score had a significant impact on the effi-cacy parameter Emax.The final model was Emax,i=15.42+1.21×(Baselinei-24.41).The goodness of fit results showed that the final model had a good fit-ting effect on the measured data.The model pa-rameters obtained from Bootstrap testing were very consistent with the original model,indicated that the model parameter estimation was robust.The visual prediction test results showed that the model had good predictive performance.The typi-cal efficacy time curve showed that the higher the baseline value of TCM symptom score,the greater the decrease in score.At 3 months of treatment,the TCM symptom score at each baseline basically decreased to below 10 points.CONCLUSION:The formula for strengthening the spleen and resolving phlegm can effectively improve the levels of Treg and Th17 in PCOS patients with spleen deficiency and phlegm dampness syndrome,and has good therapeutic effects,which is worthy of clinical appli-cation.

Objective:To investigate the prognostic factors for esophageal cancer after radical resection and the application value of machine learning prediction model.Methods:The retrospective cohort study was conducted. The clinicopatholigical data of 406 esophageal cancer patients who were admitted to Qilu Hospital of Shandong University from January 2018 to March 2022 were collected. There were 357 males and 49 females, aged (64±8)years. All patients underwent radical resection of esophageal cancer. The 406 patients were randomly divided into a training set of 285 cases and a validation set of 121 cases at a 7∶3 ratio based on a random number table. The training set was used to construct prediction model, and the validation set was used to validate prediction model. Patients were divided into high-risk group and low-risk group based on risk scores. Observation indicators: (1) follow-up of patients and analysis of influencing factors for prognosis; (2) construction and validation of machine learning prediction models. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Comparison of ordinal data between groups was conducted using the rank sum test. The Kaplan-Meier method was used to calculate survival rate and plot survival curve, and the Log-rank test was used for survival analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses. Independent influencing factors were included, and data processing, machine learning model construction, and visualization were performed using R packages including random survival forest (RSF), gradient boosting machine (GBM), least absolute shrinkage and selection operator Cox regression (LASSO-Cox), Cox proportional hazards model boosting (CoxBoost), survival support vector machine (survivalsvm), extreme gradient boosting (XGBoost), supervised principal component analysis (SuperPC), and Cox partial least squares regression (plsRcox). Receiver operating characteristic (ROC) curves were drawn, and sensitivity, specificity, and area under the curve (AUC) were calculated. The Delong test was used to assess the differences in AUC among different models in the training set, and the time-dependent ROC was used to compare the predictive performance of different models. Calibration curves were used to evaluate model accuracy, and decision curve analysis (DCA) was used to evaluate overall net benefit. Results:(1) Follow-up of patients and analysis of influencing factors for prognosis. All 406 patients were followed up postoperatively for 28(range, 6-36)months, with 1- and 3-year overall survival rate of 86.5% and 40.9%, respectively. The 285 patients in the training set were followed up postoperatively for 30(range, 6-36)months, with 1- and 3-year overall survival rate of 85.1% and 35.5%, respectively. The 121 patients in the validation set were followed up postoperatively for 25(range, 6-36)months, with 1- and 3-year overall survival rate of 87.0% and 43.2%, respectively. There was no significant difference in postoperative overall survival rate between the training set and the validation set ( χ2=3.20, P>0.05). Results of multivariate analysis showed that left thoracic surgical approach, preopera-tive neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia were independent risk factors affecting postoperative survival of 285 patients in the training set ( hazard ratio=1.466, 1.037, 1.482, 1.549, 5.268, 7.727, 22.202, 2.539, 2.686, 1.425, 95% confidence interval as 1.026-2.096, 1.003-1.073, 1.008-2.179, 1.105-2.170, 1.201-23.099, 1.833-32.576, 4.734-104.128, 1.577-4.087, 1.631-4.422, 1.018-1.994, P<0.05). (2) Construction and validation of machine learning prediction models. Independent risk factors affecting postoperative survival were included to construct RSF, GBM, LASSO-Cox, CoxBoost, survivalsvm, XGBoost, SuperPC, and plsRcox machine learning prediction models. Results of Delong test showed that there were significant differences in the AUC of RSF and GBM from the other six models ( P<0.05). Results of time-dependent ROC curve showed that all 8 machine learning predic-tion models had good discriminative ability in the training cohort, among which the RSF machine learning prediction model had the best predictive performance. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postoperative 1-, 2-, and 3-year overall survival in the training cohort, with high consistency with actual results. Results of decision curve analysis showed that within a threshold range of 0-0.80, the RSF machine learning prediction model provided a better overall net benefit. Further analysis showed that in the validation set, the AUC of RSF machine learning prediction model for postoperative 1-, 2-, and 3-year survival prediction were 0.786 (95% confidence interval as 0.609-0.962), 0.774 (95% confidence interval as 0.676-0.873), and 0.750 (95% confidence interval as 0.652-0.848), respectively. Results of calibration curve showed that the RSF machine learning prediction model fitted well for predicting postopera-tive 1-, 2-, and 3-year overall survival in the validation set, with high consistency with actual results. In the training set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score <11.7 as the low-risk group. The median survival times of the two groups were 18.0 months and >36.0 months, respectively, showing a significant difference between them ( χ2=73.30, P<0.05). In the validation set, the optimal cutoff value of the RSF machine learning prediction model risk score was 11.7. Patients with risk score ≥11.7 were classified as the high-risk group, and those with risk score<11.7 as the low-risk group. The median survival times of the two groups were 17.0 months and>36.0 months for the high-risk and low-risk groups, respectively, showing a significant difference between them ( χ2=35.20, P<0.05). Conclusions:Left thoracic surgical approach, preoperative neutrophil count, vascular invasion, perineural invasion, pathological T2-4 stage, pathological N2-3 stage, and postoperative pneumonia are independent risk factors affecting survival of esophageal cancer patients after radical resection. The RSF machine learning prediction model constructed based on these factors can effectively distinguish the survival prognosis of high-risk and low-risk patients.

BACKGROUND: Renal osteodystrophy (ROD) is a skeletal pathology associated with chronic kidney disease-mineral and bone disorder (CKD-MBD) that is characterized by aberrant bone mineralization and remodeling. ROD increases the risk of fracture and mortality in CKD patients. The underlying mechanisms of ROD remain elusive, partially due to the absence of an appropriate animal model. To address this gap, we established a stable mouse model of ROD using an optimized adenine-enriched diet and conducted exploratory analyses through ribonucleic acid sequencing (RNA-seq). METHODS: Eight-week-old male C57BL/6J mice were randomly allocated into three groups: control group ( n = 5), adenine and high-phosphate (HP) diet group ( n = 20), and the optimized adenine-containing diet group ( n = 20) for 12 weeks. We assessed the skeletal characteristics of model mice through blood biochemistry, microcomputed tomography (micro-CT), and bone histomorphometry. RNA-seq was utilized to profile gene expression changes of ROD. We elucidated the functions of differentially expressed genes (DEGs) using gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and gene set enrichment analysis (GSEA). DEGs were validated via quantitative real-time polymerase chain reaction (qRT-PCR). RESULTS: By the fifth week, adenine followed by an HP diet induced rapid weight loss and high mortality rates in the mouse group, precluding further model development. Mice with optimized adenine diet-induced ROD displayed significant abnormalities in serum creatinine and blood urea nitrogen levels, accompanied by pronounced hyperparathyroidism and hyperphosphatemia. The femur bone mineral density (BMD) of the model mice was lower than that of control mice, with substantial bone loss and cortical porosity. ROD mice exhibited substantial bone turnover with an increase in osteoblast and osteoclast markers. Transcriptomic profiling revealed 1907 genes with upregulated expression and 723 genes with downregulated expression in the femurs of ROD mice relative to those of control mice. Pathway analyses indicated significant enrichment of upregulated genes in the sphingolipid metabolism pathway. The significant upregulation of alkaline ceramidase 1 ( Acer1 ), alkaline ceramidase 2 ( Acer2 ), prosaposin-like 1 ( Psapl1 ), adenosine A1 receptor ( Adora1 ), and sphingosine-1-phosphate receptor 5 ( S1pr5 ) were successfully validated in mouse femurs by qRT-PCR. CONCLUSIONS Optimized adenine diet mouse model may be a valuable proxy for studying ROD. RNA-seq analysis revealed that the sphingolipid metabolism pathway is likely a key player in ROD pathogenesis, thereby providing new avenues for therapeutic intervention.
Animals ; Mice ; Chronic Kidney Disease-Mineral and Bone Disorder/genetics* ; Male ; Disease Models, Animal ; Mice, Inbred C57BL ; Sphingolipids/metabolism* ; Transcriptome/genetics* ; Signal Transduction/genetics* ; X-Ray Microtomography ; Adenine

Objective To investigate the causal relationship between circulating white blood cells(WBC)and juvenile idiopathic arthritis(JIA)using a two-sample Mendelian randomization(MR)analysis,and to provide a reference for the treatment strategy of JIA.Methods Relevant data of WBC and JIA were extracted from the public data of genome-wide association studies.Then,bidirectional MR analysis was conducted using the inverse variance weighted method(IVW),MR-Egger regression method,mixed contamination method,and Bayesian weighted Mendelian randomization.A series of sensitivity analyses were used to verify the robustness of the results.Results After MR analysis,false discovery rate(FDR)correction and sensitivity verification,calculations using IVW as the main method showed that neutrophils could reduce the risk of JIA(OR=0.752,95%CI:0.622,0.908,P=0.003,PFDR=0.003),and that JIA could lead to increased monocyte counts(bete=0.015,95%CI:0.007,0.022,P=1.90E-04,PFDR=1.14E-03).Conclusion A bidirectional causal association is identified between WBC and the risk of JIA occurrence.