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.

Tuberculosis (TB) is a chronic infectious disease caused by Mycobacterium tuberculosis, which is primarily transmitted through the respiratory tract, and remains one of the diseases with the highest mortality rate of single-pathogen infections globally. The cell wall polysaccharides of M. tuberculosis are critical for maintaining bacterial structure, mediating pathogenesis, and enabling immune evasion. Lipoarabinomannan (LAM), a key polysaccharide component, has revolutionized non-invasive diagnostic technologies as a TB biomarker, while polysaccharide-based vaccines have emerged as innovative strategies for TB prevention. This review systematically examines the composition, subcellular distribution, and functional roles of M. tuberculosis cell wall polysaccharides in bacterial metabolism, drug resistance, and immune regulation. A particular emphasis is placed on recent advancements in LAM-based diagnostics and vaccine development. Future studies should utilize advanced technologies to precisely characterize the structural features of TB polysaccharides and explore their biological functions, providing a foundation for targeted diagnostic and therapeutic innovations. This article aims to provide reference for advancing both basic research and clinical applications related to M. tuberculosis.

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.

Objective To investigate the effects of plateau hypoxia on the growth and tumor microenvironment of colorectal carcinoma in vivo.Methods A total of 16 male BALB/C mice(6 weeks old,weight 18-20 g)were randomly divided into plateau hypoxic group and plain normoxic group,with 8 mice in each group,while 14 male C57BL/6 mice were grouped in same way,with 7 mice in each group.The mice in the plateau hypoxic group were housed in a low-pressure oxygen(10%)chamber to simulate an altitude of approximately 5 600 m,while the mice of the other group was maintained in SPF-grade normal atmospheric conditions(21%oxygen,at an altitude of about 300 m).Colorectal tumor MC38 cells and colon adenocarcinoma CT26 cells were subcutaneously implanted into C57BL/6 mice and BALB/C mice,respectively to construct subcutaneous tumor-bearing mouse models.Then the tumor size and weight were measured in 4 groups of mice.After the tumor tissues,spleen and blood samples were collected in the C57BL/6 mice.Flow cytometry was used to determine the percentages of macrophages,T lymphocytes,IFN-γ+T lymphocytes,and myeloid-derived suppressor cells(MDSC).The differences in these immune cells were compared between the cells from the plateau hypoxic group and those from the plain normoxic group.Results The weight of subcutaneous tumor mass was significantly inhibited in both C57BL/6 and BALB/C mice from the plateau hypoxic group than those from the 2 plain normoxic groups(0.17 vs 0.09 g,1.38 vs 0.51 g,P<0.01).When compared with the immune cells from the tumor mass of the plain normoxic C57BL/6 mice,the percentage of M2-type macrophages was reduced in the tumor tissue from the plateau hypoxic mice(22.13%vs 15.90%,P<0.05),so was that of MDSC(2.06%vs 1.05%,P<0.01),particularly in the monocytic(M)-MDSC subgroup(60.97%vs 41.13%,P<0.01).While,no significant change was observed in the proportion of the polymorphonuclear(PMN)-MDSC subgroup(10.97%vs 9.70%,P>0.05).Additionally,the percentage of CD4+T cells was significantly reduced(48.70%vs 41.93%,P<0.05),whereas that of CD8+T cells was obviously increased(41.25%vs 51.18%,P<0.05),along with a notable rise in the proportions of IFN-γ+T,IFN-γ+CD4+T and IFN-γ+CD8+T cells(28.58%vs 59.65%,23.33%vs 53.65%,36.9%vs 66.48%,P<0.01).However,between the peripheral blood samples of the 2 groups of C57BL/6 mice,the proportions of M1-type macrophages and CD4+T cells were reduced(84.98%vs 78.43%,5.86%vs 4.01%,P<0.01),and those of MDSC and PMN-MDC were increased(4.47%vs 16.43%,36.56%vs 62.97%,P<0.01).In the spleen tissues,notable decreases were observed in the proportions of CD8+T cells and IFN-γ+CD8+T cells between the 2 groups(33.05%vs 27.68%,5.13%vs 1.58%,P<0.01).Conclusion Plateau hypoxia improves the immune response within the tumor microenvironment,and inhibits subcutaneous tumor growth of colorectal cancer,but suppresses systemic immune response.

Objective To comprehensively analyze the mechanism of Astragalus and Magnolia officinalis in treating prostate cancer based on the principles of network pharmacology.Methods Active molecular targets of Astragalus and Magnolia officinalis were predicted using the TCMSP and SwissTargetPrediction databases.Prostate cancer-related targets were screened via Genecards,DisGeNET,and OMIM databases.A"disease-active ingredient-target"network was constructed using Venny software,identifying 69 candidate key target genes.A protein-protein interaction(PPI)network was built using the STRING database,followed by GO and KEGG enrichment pathway analyses performed with R language.Constructing a subcutaneous tumor model in nude mice through in vivo experiments and intervening with active ingredients from Astragalus membranaceus and Magnolia offi-cinalis.Results Molecular docking analysis revealed that active components such as astragaloside IV(MOL000438)and honokiol(MOL000398)exhibited significant binding activity with the key target proteins of prostate cancer,including AKT1,ESR1,PPARG,PTGS2,and SRC.Notably,Honokiol demonstrated a binding energy of-8.7 kcal/mol with estrogen receptor α(ESR1,PDB:1a52),forming stable hydrogen bond interaction with the LEU-391 residue.The in vivo experiments further confirmed that the Astragalus-Magnolia active component group showed smaller subcutaneous xenograft tumor volumes in nude mice as compared to the model group(P<0.05).Immunohistochemical analysis revealed significant downregulation of PPARG and PTGS2 protein expression in tumor tissues(P<0.05).QPCR results indicated that the formula bidirectionally regulated gene expression:pro-apoptotic factor AKT1 was upregulated(P<0.05),while cancer-associated genes PTGS2,PPARG,SRC,and ESR1 were downregulated(P<0.05).Conclusion The combination of Astragalus and Magnolia may exert anti-prostate cancer effects through multi-target and multi-pathway synergistic mechanisms,demonstrating favorable binding activity and therapeutic potential.

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.

The sepsis and sepsis-induce lung inju-ry threats seriously human health.Dexmedetomi-dine(DEX),a sedative drug,plays an active role in preventing sepsis-induced lung injury during the ba-sic and clinical practice.The current article reviews the role and mechanism of DEX dexmedetomidine alleviating sepsis-induced lung injury from the as-pects of inflammation,oxidative stress,apoptosis,mitochondrial dynamics,autophagy,vascular per-meability,neuro-regulation,targeting miR-128-3p/MAPK14 and DNA methylation,etc.This review looks forward to deepen the understanding the ap-plication of DEX in the field of critical care medi-cine,expand the pharmacological effect of DEX and provide a new idea for the prevention and treat-ment of sepsis from the sedation approach.

BACKGROUND:Exercise is an important strategy for the prevention and management of sarcopenic obesity in older adults,but there is a lack of exploration and research on accurate and personalized exercise prescription for sarcopenic obesity in older adults.OBJECTIVE:To review the tandem mechanism of sarcopenic obesity in older adults and the effects of different exercise interventions in older patients with sarcopenic obesity,in order to provide theoretical and practical guidance for the formulation of exercise prescriptions for sarcopenic obesity in older adults.METHODS:PubMed,Web of Science,CNKI,VIP,and WanFang databases were retrieved for relevant literature using the keywords of"sarcopenic obesity,sarcopenic adiposity,aging,sport,exercise,exercise intervention,exercise prescription,resistant training,aerobic training,combination training,muscle strength,muscle mass,physical activity"in Chinese and English,respectively.A total of 85 articles were included for review according to the inclusive and exclusive criteria.RESULTS AND CONCLUSION:(1)Resistance exercise is still an effective exercise method to prevent and alleviate sarcopenic obesity in older adults.Resistance exercise is more significant in improving and improving skeletal muscle mass,muscle strength and endurance,and physical function ability.However,in practice,it should be applied in a gradual manner,with a gradual increase in intensity to a medium-to-high level.(2)Aerobic exercise is also an important intervention to control and slow the progress of sarcopenic obesity,and it is more effective in improving cardiorespiratory endurance,decreasing percentage of body fat,and decreasing the area of visceral fat in older patients with sarcopenic obesity.(3)Combining the advantages of aerobic exercise and resistance exercise can better improve body composition and reduce cardiovascular disease risk factors.To some extent,combined exercise is better than single exercise.

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: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.