ObjectiveHepatocellular carcinoma (HCC) represents 90% of all primary liver cancers. The main risk factors associated with HCC include viral hepatitis (B and/or C), alcohol abuse, and metabolic dysfunction-associated steatotic liver disease (MASLD), which progressively advance to liver fibrosis, cirrhosis, and ultimately evolve into HCC. Surgical resection represents the most effective treatment for HCC, while recent advances in immunotherapy, including immune checkpoint inhibitors and adoptive cell therapies, have provided improved treatment prospects for patients with unresectable HCC. However, the complex metabolic heterogeneity of HCC limits the therapeutic efficacy. Metabolic intermediates acyl-CoA not only provide energy and substrates for numerous biochemical reactions but also serve as donors for protein lysine acylation, a major class of post-translational modification (PTM). Therefore, a deeper understanding of the molecular mechanisms underlying protein lysine acylation and hepatocarcinogenesis is urgently needed. MethodsThe levels of protein lysine acylation and silence information regulator 5 (SIRT5) expression levels in clinical HCC samples were analyzed by Western blot. Quantitative malonylome and succinylome of HCC samples were analyzed by antibody-based affinity enrichment coupled with tandem mass spectrometry. The proliferation of HCC cells was analyzed with Cell Counting Kit-8 (CCK-8) assays, the apoptosis was quantified by Annexin V-FITC/propidium iodide (PI) staining coupled with flow cytometry, and the ability of cells to migrate was assayed by Transwell assays. The enzymatic activity of glutathione S-transferase Mu 1 (GSTM1) was quantified. Transgenic mice with hepatic overexpression of SIRT5 were constructed using CRISPR-Cas9, and primary hepatocarcinogenesis was induced by administration of diethylnitrosamine. ResultsWestern blot analysis indicated that the expression level of SIRT5 was elevated in clinical samples from HCC patients, and the levels of lysine malonylation, glutarylation, and succinylation were significantly reduced in HCC tissues. Knockout of SIRT5 in MHCC-97H and MHCC-97L hepatoma cells suppressed cell proliferation, and increased the percentage of apoptotic cells significantly. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the differentially malonylome and succinylome of HCC samples revealed significant enrichment in two major classes of biological processes: core energy metabolism (e.g., glycolysis/gluconeogenesis, tricarboxylic acid metabolic process, fatty acid beta oxidation) and detoxification and oxidative stress response (e.g., response to toxic substance, chemical carcinogenesis, reactive oxygen species (ROS)). SIRT5 removes malonylation from lysine residues in GSTM1 and restores its detoxification activity, which is crucial for the survival of hepatocytes under stressed conditions. More importantly, in vivo experiment indicated that hepatic-specific overexpression of SIRT5 in mice accelerated diethylnitrosamine-induced liver fibrosis and hepatocarcinogenesis, indicating the critical role of SIRT5 in HCC progression. ConclusionThis study highlights the previously unrecognized SIRT5-GSTM1 axis as a key regulator in hepatocarcinogenesis, and suggests a potential target for the treatment of patients with HCC.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Objective: To analyze the school tuberculosis (TB) outbreaks and preventive treatment in Hefei from 2022 to 2024, so as to provide reference for TB prevention and control in schools. Methods: Data were collected on all school based TB outbreaks occurring during 2022-2024 in Hefei, defined as ≥2 epidemiologically linked TB cases within the same school during a single semester. Statistical analyses were performed using the Chi square test. Results: Close contacts exhibited significantly higher TB incidence (2.88%) and latent mycobacterium tuberculosis infection (LTBI) rates (13.80%) in the school TB outbreaks, compared to non close contacts (0.12% and 2.63%, respectively). Among close contacts, secondary school students showed lower TB incidence (0.48%) and LTBI prevalence (3.42%) than both primary school or younger children (0.68%, 6.95%) and college students ( 0.78% , 6.50%), with statistically significant differences ( χ 2=360.91, 6.37; 791.71, 102.03, all P <0.05). The proportion of LTBI individuals recommended for preventive therapy was higher in primary school or younger groups (98.59%) than in secondary (95.25%) or college students (86.34%) ( χ 2=25.86, P <0.01). However, among those recommended, close contacts had higher uptake (85.82%) and completion rates (87.25%) of preventive therapy than non close contacts (69.63% and 70.57%); similarly, secondary school students demonstrated higher uptake (91.21%) and completion rates (86.45%) compared to primary school or younger (88.57%, 83.87%) and college students (57.28%, 64.08%) ( χ 2=30.52, 26.72; 125.17, 38.84, all P <0.01). Subsequent TB incidence among LTBI close contacts (13.30%) and among those who did not complete preventive therapy (22.73%) were significantly higher than among non close contacts (2.80%, 2.41%), respectively ( χ 2=32.19, 13.87, both P <0.05). Conclusions In school TB outbreaks, close contacts face higher LTBI prevalence and subsequent TB risk than non close contacts. College students show notably low adherence to preventive therapy. It is necessary to take targeted measures to improve the compliance of preventive measures among students.

Childhood obesity has become a global public health issue. Current research indicates that early life obesogen exposure has emerged as a significant risk factor for childhood obesity. While obesogens have been confirmed to influence the development and progression of childhood obesity through mechanisms such as endocrine disruption and epigenetic programming, controversies remain regarding the establishment of causal relationships, assessment of combined exposures, and validation of transgenerational effects in humans. In recent years, novel approaches including multi-omics technologies, exposome-based analysis, and multigenerational cohort studies have integrated dynamic biomarker monitoring with analyses of social-environmental interactions, offering new perspectives and methodologies for constructing a systematic "exposure-mechanism-outcome" research framework. This article reviews literature from PubMed and Web of Science up to August 2025 on the association between early life obesogen exposure and childhood obesity, summarizing evidence on the health effects of early life obesogen exposure, major exposure pathways and internal exposure assessment, interactions and amplifying effects of social and environmental factors, as well as the biological mechanisms underlying obesogen action. It further examines current research frontiers and challenges, aiming to provide a theoretical foundation for early prevention and precision intervention of childhood obesity.

ObjectiveTo investigate the mechanisms by which Renshentang treats atherosclerosis （AS） in mice， focusing on the regulation of endothelial inflammatory responses mediated by transient receptor potential vanilloid subtype 1 （TRPV1）. MethodsAn AS model was established in apolipoprotein E knockout （ApoE^-/-） mice fed a high-fat diet. The mice were randomly divided into a simvastatin group （0.02 g·kg^-1·d^-1） and low-， medium-， and high-dose Renshentang groups （1.77， 3.54， 7.08 g·kg^-1·d^-1）， with 12 mice in each group. ApoE^-/- mice were fed a high-fat diet and treated simultaneously. C57BL/6J mice fed a normal diet served as the normal group （n=9）. After continuous administration for 12 weeks， mice were anesthetized and the aortas were collected. Oil Red O staining was used to observe lipid plaque formation in the aorta. Hematoxylin-eosin （HE） staining was performed to examine pathological changes in the aortic root. Immunohistochemistry was used to analyze the levels of pro-inflammatory factors tumor necrosis factor-α （TNF-α） and interleukin-1β （IL-1β）， as well as the expression of TRPV1， phosphorylated phosphoinositide 3-kinase （p-PI3K）， and phosphorylated protein kinase B （p-Akt） in the aortic root. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to detect endothelial nitric oxide synthase （eNOS） mRNA expression in the aorta， and Western blot was used to detect TRPV1 protein expression. ResultsCompared with the normal group， the model group showed a significant increase in aortic plaque formation （P<0.01） and significantly elevated levels of TNF-α and IL-1β in the aortic root （P<0.01）. The expression levels of TRPV1， p-PI3K， and p-Akt were decreased （P<0.05， P<0.01）， and eNOS mRNA expression was reduced （P<0.05， P<0.01）. Compared with the model group， all Renshentang groups significantly reduced aortic plaque formation （P<0.01）， significantly decreased TNF-α and IL-1β levels （P<0.01）， and markedly increased the expression levels of TRPV1， p-PI3K， p-Akt， and eNOS mRNA （P<0.05， P<0.01）. ConclusionRenshentang may inhibit endothelial inflammation and suppress the formation of AS by increasing TRPV1 protein expression and up-regulating the PI3K/Akt/eNOS signaling pathway， which may be one of the molecular mechanisms underlying its therapeutic effect against AS.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Objectives:Coronary slow flow(CSF)has long been regarded as a marker of coronary microvascular dysfunction(CMD).This study aims to evaluate the diagnostic value of CSF for CMD in patients with angina and nonobstructive coronary arteries(ANOCA).Methods:The study data were derived from the ANOCA-CMD prospective cohort study.All enrolled patients underwent coronary angiography and concurrent coronary physiological assessments in the left anterior descending artery using pressure-wire and thermodilution techniques to obtain coronary flow reserve(CFR)and the index of microcirculatory resistance(IMR).Based on the results,CMD was classified into four subtypes:CMD with elevated IMR(IMR≥25),CMD with reduced CFR(CFR<2.5),CMD with either reduced CFR or elevated IMR(CFR<2.5 or IMR≥25),and CMD with both reduced CFR and elevated IMR(CFR<2.5 and IMR≥25).The corrected thrombolysis in myocardial infarction(TIMI)frame count(CTFC)in the left anterior descending artery was calculated from coronary angiography images,with CSF defined as CTFC>27.This study evaluated the correlation between CTFC,CFR,and IMR,and investigated the diagnostic value of CSF for CMD in ANOCA patients.Results:A total of 103 ANOCA patients were enrolled in this study,with a mean age of(64.2±10.6)years,and 53.4％were female.Among them,57 patients(55.3％)were diagnosed with coronary slow flow.Patients with slow flow had higher IMR(P<0.001)and CFR(P=0.041).Similarly,the proportion of CMD with elevated IMR was higher in the slow flow group(P<0.001),while the proportion of CMD with reduced CFR was lower(P=0.044).There was no significant difference between the groups in the proportions of CMD with either reduced CFR or elevated IMR or CMD with both reduced CFR and elevated IMR(all P>0.05).CTFC was positively correlated with hyperemic mean transit time(r=0.424,P<0.001),IMR(r=0.430,P<0.001),and CFR(r=0.211,P=0.032).The area under the curve(AUC)of CTFC for diagnosing CMD with elevated IMR was 0.721(95％CI:0.623-0.819)with an accuracy of 67％(57％,76％),for diagnosing CMD with reduced CFR was 0.610(95％CI:0.499-0.720)with an accuracy of 60％(50％,70％),for diagnosing CMD with either reduced CFR or elevated IMR was 0.549(95％CI:0.425-0.673)with an accuracy of 47％(37％,57％),and for diagnosing CMD with both reduced CFR and elevated IMR was 0.582(95％CI:0.471-0.693)with an accuracy of 47％(37％,57％).Thus,CSF demonstrated limited diagnostic values across all subtypes of CMD.Conclusions:In ANOCA patients,CSF cannot serve as an effective diagnostic marker for CMD.Therefore,in clinical practice,the slow flow phenomenon should not be directly equated with the presence of coronary microvascular dysfunction in ANOCA patients.

Objectives:To assess the impact of intravascular ultrasound(IVUS)guidance for drug-eluting stent(DES)implantation on the long-term outcomes in coronary artery disease(CAD)patients with chronic kidney disease(CKD).Methods:A retrospective study was conducted on 1 800 CAD and CKD patients who underwent coronary DES implantation at Nanjing First Hospital from January 2018 to December 2022.Patients were divided into IVUS-guided group(IVUS group,n=333)and angiography-guided group(angiography group,n=1 467).Propensity score matching(PSM)was performed at a 1:2 ratio to adjust for differences in baseline clinical and angiographic characteristics between the two groups.Major adverse cardiovascular events(MACE),including cardiac death,target vessel myocardial infarction,and clinically driven target vessel revascularization,were evaluated over a 2-year follow-up.Cox proportional hazards regression was performed to identify independent predictors of MACE.Results:After propensity score matching,333 patients in the IVUS group were matched with 666 patients in the angiography group.Following matching,there were no significant differences in clinical characteristics and angiographic data between the two groups(all P>0.05).Regarding procedural data,IVUS group had a higher number of stents implanted,longer total stent length,larger average stent diameter,more frequent use of post-dilation balloons,larger post-dilation balloon diameter,and greater contrast agent usage(all P<0.05).MACE rate was significantly higher in the angiography group than that in the IVUS group(23.9%vs.18.0%,P=0.037).The difference was mainly attributed to a higher rate of cardiac death in the angiography group(16.1%vs.10.8%,P=0.013).Additionally,patients in angiography group had a significantly higher all-cause mortality rate compared to IVUS group(19.7%vs.13.8%,P=0.022).Multivariate cox regression analysis showed that IVUS guidance(HR=0.73,95%CI:0.55-0.99,P=0.042)was an independent protective factor,while hypertension(HR=1.60,95%CI:1.13-2.26,P=0.008),type 2 diabetes(HR=1.56,95%CI:1.19-2.05,P=0.001),acute coronary syndrome(HR=1.92,95%CI:1.12-3.30,P=0.018),and moderate to severe calcification(HR=1.55,95%CI:1.18-2.04,P=0.002)were independent risk factors for MACE at 2 years after DES implantation in CKD patients.Conclusions:Patients with CAD and CKD,IVUS-guided DES implantation is associated with a reduced risk of MACE and all-cause mortality at 2 years post-procedure compared to coronary angiography-guided implantation.

Objective To investigate the effect of methane-rich saline on rat acne and its mechanism.Methods Totally 60 SD rats were randomly assigned to 4 groups(n=15):normal group,acne model group,tretinoin treatment group,or methane-rich saline treatment group.Acne models were established by applying oleic acid once a day and injecting Propionibacterium acnes every other day.No treatment was applied to the normal and acne model groups.The tretinoin treatment group received topical application of tretinoin cream on the affected areas,while the methane-rich saline treatment group was administered with methane-rich saline via oral gavage.Wet/dry weight ratio and histological staining were used to detect the rat ear edema and morphological changes.Terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling(TUNEL)was used to detect apoptosis,enzyme-linked immunosorbent assay(ELISA)was used to detect the expression of inflammatory factors(interleukin[IL]-6,IL-8,tumor necrosis factor α[TNF-α],and IL-37),and Western blotting was used to detect the protein expression of superoxide dismutase(SOD),nuclear factor κB(NF-κB),and cysteine aspartic acid specific protease 3(caspase 3).Results After the treatment with methane-rich saline,the edema and acne were alleviated in rat ears.There were significant improvements in glandular hyperplasia,inflammatory cell infiltration,vascular dilation,hair follicle enlargement,and keratinization for the rats in the methane-rich treatment group.The effect of methane-rich saline was similar to that of tretinoin.The treatment of methane-rich saline could significantly reduce the expression of inflammatory factors such as IL-6,IL-8,and TNF-α,while could significantly increase the expression of anti-inflammatory factor IL-37 and the rate of apoptosis(all P＜0.01).In addition,methane-rich saline treatment could upregulate the expression of SOD,caspase 3,and cleaved caspase 3,while could reduce the expression of NF-κB(all P＜0.01).Conclusion The administration of methane-rich saline has a good therapeutic effect on rat acne,and it may be related to its anti-inflammatory,antioxidant,and pro-apoptotic activities.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.