Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

OBJECTIVE: To identify prognostic genes associated with lysosome-dependent cell death (LDCD) in patients with gastric cancer (GC). METHODS: Differentially expressed genes (DEGs) were identified using The Cancer Genome Atlas - Stomach Adenocarcinoma. Weighted gene co-expression network analysis was performed to identify the key module genes associated with LDCD score. Candidate genes were identified by DEGs and key module genes. Univariate Cox regression analysis, and least absolute shrinkage and selection operator regression and multivariate Cox regression analyses were performed for the selection of prognostic genes, and risk module was established. Subsequently, key cells were identified in the single-cell dataset (GSE183904), and prognostic gene expression was analyzed. Cell proliferation and migration were assessed using the Cell Counting Kit-8 assay and the wound healing assay. RESULTS: A total of 4,465 DEGs, 95 candidate genes, and 4 prognostic genes, including C19orf59, BATF2, TNFAIP2, and TNFSF18, were identified in the analysis. Receiver operating characteristic curves indicated the excellent predictive power of the risk model. Three key cell types (B cells, chief cells, and endothelial/pericyte cells) were identified in the GSE183904 dataset. C19orf59 and TNFAIP2 exhibited predominant expression in macrophage species, whereas TNFAIP2 evolved over time in endothelial/pericyte cells and chief cells. Functional experiments confirmed that interfering with C19orf59 inhibited proliferation and migration in GC cells. CONCLUSION C19orf59, BATF2, TNFAIP2, and TNFSF18 are prognostic genes associated with LDCD in GC. Furthermore, the risk model established in this study showed robust predictive power.
Stomach Neoplasms/pathology* ; Humans ; Prognosis ; Lysosomes/physiology* ; RNA-Seq ; Cell Death ; Single-Cell Analysis ; Gene Expression Regulation, Neoplastic ; Cell Proliferation ; Single-Cell Gene Expression Analysis

OBJECTIVE: To investigate the association between ABO blood types and gestational diabetes mellitus (GDM) risk. METHODS: A prospective birth cohort study was conducted. ABO blood types were determined using the slide method. GDM diagnosis was based on a 75-g, 2-h oral glucose tolerance test (OGTT) according to the criteria of the International Association of Diabetes and Pregnancy Study Groups. Logistic regression was applied to calculate the odds ratios ( ORs) and 95% confidence intervals ( CIs) between ABO blood types and GDM risk. RESULTS: A total of 30,740 pregnant women with a mean age of 31.81 years were enrolled in this study. The ABO blood types distribution was: type O (30.99%), type A (26.58%), type B (32.20%), and type AB (10.23%). GDM was identified in 14.44% of participants. Using blood type O as a reference, GDM risk was not significantly higher for types A ( OR = 1.05) or B ( OR = 1.04). However, women with type AB had a 19% increased risk of GDM ( OR = 1.19, 95% CI = 1.05-1.34; P < 0.05), even after adjusting for various factors. This increased risk for type AB was consistent across subgroup and sensitivity analyses. CONCLUSION The ABO blood types may influence GDM risk, with type AB associated with a higher risk. Incorporating it-either as a single risk factor or in combination with other known factors-could help identify individuals at risk for GDM before or during early pregnancy.
Humans ; Female ; Pregnancy ; Diabetes, Gestational/etiology* ; ABO Blood-Group System ; Adult ; Prospective Studies ; Risk Factors ; Young Adult

Objective:To clarify the genetic associations between obesity, diabetes, smoking, non-alcoholic fatty liver disease, acute and chronic pancreatitis, and pancreatic cancer risk.Methods:Summary data from genome-wide association studies (GWAS) of individuals of European descent were used. Obesity, alcohol consumption, diabetes, and acute and chronic pancreatitis data for the UK population were obtained from the GWAS catalog, while alcohol consumption, non-alcoholic fatty liver disease, occasional smoking, and regular smoking data were obtained from the UK biobank. Pancreatic cancer-related data for the Finnish population were sourced from the latest R11 version of the Finnish database. Two-sample Mendelian randomization (MR) analysis was conducted on the associations between the aforementioned risk factors and pancreatic cancer using five MR methods, primarily inverse variance weighting. The robustness of the results was assessed through Q heterogeneity tests, pleiotropy tests, MR-PRESSO analysis, and reverse MR analysis.Results:Obesity showed a significant positive association with pancreatic cancer risk ( OR=1.407, 95% CI: 1.100-1.714, P=0.030), and the results were robust based on Q heterogeneity tests, pleiotropy tests, MR-PRESSO, and reverse MR analysis (all P>0.05). However, no significant associations were found between pancreatic cancer risk and alcohol consumption ( P=0.330), heavy drinking ( P=0.382), type 1 diabetes ( P=0.674), type 2 diabetes ( P=0.825), occasional smoking ( P=0.607), regular smoking ( P=0.758), non-alcoholic fatty liver disease ( P=0.287), acute pancreatitis ( P=0.336), or chronic pancreatitis ( P=0.545). Conclusion:This study further confirms the strong genetic association between obesity and increased pancreatic cancer risk.

Objective:To explore the clinical efficacy of super-selective ophthalmic artery thrombolysis in the treatment of central retinal artery occlusion (CRAO).Methods:This is a retrospective case series study,based on the analysis of clinical data of 50 non-arteritic CRAO patients. The patients were advised to be treated with super-selective intra-ocular arterial thrombolysis at the Neurosurgery Department, Shenyang No. 4 People′s Hospital from May to December 2024, and treated with intra-arterial thrombolysis and postoperative management guidance by the Department of Neurosurgery, General Hospital of the Northern Theater Command. There were 36 males and 14 females, aged (59.5±10.2)years (range: 41 to 75 years). There were 5 cases of complete obstruction of the central retinal artery and 45 cases of subtotal obstruction.Before the operation, all patients underwent optical coherence tomography angiography (OCTA)+ocular vascular ultrasonography, and their visual acuity was measured using a standard visual acuity logarithmic scale, visual field was measured using the contrast visual field examination method;One week after the operation, all patients were rechecked for OCTA, visual acuity and visual field. The patients′ preoperative and postoperative visual field recovery status were compared. Significant effect was defined as an improvement of more than 3 lines of visual acuity or a complete improvement of visual field defects after treatment compared with pretreatment visual acuity; effectiveness was defined as an improvement of 1 to 2 lines of visual acuity or an improvement of visual field defects after treatment compared with pretreatment visual acuity.Results:The overall effective rate of 50 patients with CRAO treated with super-selective ophthalmic artery urokinase thrombolysis was 94.0% (47/50), with 29 very effective, 18 effective and 3 ineffective. The time from onset to surgery was 0 to 6 hours in 5 patients, with an effective rate of 5/5; >6 to 24 hours in 11 patients, with an effective rate of 10/11; >1 to 7 days in 21 patients, with an effective rate of 90.5%(19/21); >7 to 14 days in 9 patients, with an effective rate of 9/9; and >14 to 21 days in 4 patients, with an effective rate of 4/4, and the difference in effective rate between the different time windows of thrombolytic therapy was not statistically significant ( P=0.961). There were 3 cases of intraoperative and postoperative complications, including 1 case of ophthalmic artery entrapment, 1 case of femoral artery pseudoaneurysm and 1 case of fundus hemorrhage, but all of them were cured after symptomatic treatment. Conclusions:Intra-arterial thrombolysis for CRAO patients has a high effective rate and a low complication rate. The surgical time window can be extended to 21 days after the onset, which is of positive significance for the recovery and improvement of the patient′s final visual acuity.

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.

Objective:To evaluate the long-term protective efficacy of the recombinant Chinese hamster ovary cell-derived hepatitis B vaccine（CHO-HepB）26 years post-vaccination in the rural China.Methods:Zhengding county，Hebei province was designated as a rural monitoring site for CHO-HepB efficacy. Study participants included individuals born between 1997 and 1999 who had completed the three-dose CHO-HepB primary series without booster doses. A cross-sectional survey was conducted in late 2024 using random sampling. Demographic and vaccination history data were collected via questionnaires，and hepatitis B virus（HBV）serological markers were detected using chemiluminescence. Historical surveillance data were integrated to infer infection statuses of HBsAg-positive individuals and evaluate longitudinal trends in anti-HBs seropositivity and antibody titers.Results:Among 178 participants（mean time since vaccination：26.2 years），the seroprevalence rates were 0.6% for HBsAg（95% CI：0.0%-1.6%），64.6% for anti-HBs（95% CI：57.6%-71.6%），and 1.1% for anti-HBc（95% CI：0.0%-2.7%）. Compared to the pre-vaccination baseline HBsAg positivity of 11.3% in children under 10 years of age，the estimated vaccine protection rate was 95%. Two notable cases were identified：one with concurrent HBsAg and anti-HBc positivity and one with anti-HBs and anti-HBc positivity，suggestive of transient HBV exposure（1999—2009）without chronicity. Natural immune boosting was inferred for the latter case based on anti-HBs titer dynamics. Longitudinal analysis of four prior cross-sectional surveys（2005，2009，2013，and 2017）revealed no significant upward trends in HBsAg and anti-HBc positivity（both P>0.05）over 26 years，while anti-HBs seropositivity declined significantly（ P<0.05）from 6 to 26 years post-vaccination. Conclusion:The CHO-HepB vaccine demonstrates sustained immunological persistence and robust long-term protection up to 26 years post-immunization. Continued emphasis on rigorous implementation of mother-to-child transmission prevention strategies is critical for future hepatitis B control.

Objective:To compare the effect of empirical cefazolin and vancomycin on the adverse outcomes in peritoneal dialysis (PD)-associated peritonitis patients.Methods:This was a retrospective analysis of a single-centre prospective cohort. Clinical data of consecutive PD-related peritonitis episodes occurring for the first time in patients (≥18 years) between January 1, 2008 and December 31, 2021 were reviewed. Patients were classified into a cefazolin group or a vancomycin group according to the empirical antibiotic regimen. The primary endpoint was peritonitis-related death within 1 month of onset and the secondary endpoint was transfer to haemodialysis for peritonitis-related reasons within the same period. Differences in both endpoints between the two regimens were analysed in the overall population and in the Gram-positive peritonitis subgroup. Univariable and multivariable logistic regression models were used to estimate the risk of adverse outcomes associated with antibiotic choice. Propensity-score matching was performed to control for confounding bias.Results:A total of 516 eligible PD patients developed peritonitis during the study period were included, among whom 138 received empirical cefazolin and 322 received vancomycin. Baseline characteristics were significantly different between the cefazolin and vancomycin groups, including annual income >50 000 yuan ( χ2=17.854, P<0.001), cardiovascular disease history ( χ2=3.909, P=0.048), prior peritonitis history ( χ2=18.327, P<0.001), serum albumin ( t=2.430, P=0.013), triglycerides ( Z=-3.108, P=0.002), total cholesterol ( t=3.752, P<0.001), phosphate ( t=3.362, P=0.002) and sodium ( t=3.021, P=0.004). Neither peritonitis-related mortality nor transfer to haemodialysis differed between the cefazolin and vancomycin groups in the overall cohort or in the Gram-positive peritonitis subgroup (all P>0.05). Logistic regression analysis (univariable and multivariable) showed that empirical vancomycin was not associated with a higher risk of adverse outcome when compared with cefazolin in the overall cohort or in the Gram-positive peritonitis subgroup (all P>0.05). After propensity-score matching, results remained consistent (all P>0.05). Conclusion:Empirical cefazolin and vancomycin yield similar rates of short-term adverse outcomes in patients with PD-associated peritonitis, including those caused by Gram-positive organisms.