Jinzhou Central Hospital

This study investigated the association of BMI, weight change patterns, and absolute weight change with mortality in adults (≥ 50 years) with diabetes or prediabetes. A longitudinal cohort analysis of 11,518 NHANES (2005-2018) participants assessed BMI categories, weight change patterns (stable normal weight, persistent overweight, obesity transitions, persistent obesity), and absolute weight change (loss, stable, gain). Cox models and restricted cubic splines evaluated mortality risks, adjusting for covariates. BMI showed a U-shaped mortality relationship, with obesity increasing all-cause (HR: 1.60, 95% CI: 1.20-2.13) and diabetes-related mortality (HR: 2.53, 95% CI: 1.35-4.75). Persistent obesity from young adulthood raised mortality risks (all-cause: +60%; diabetes-related: +153%), while later-life persistent overweight reduced risk. Weight loss (≥ 2.5kg) increased mortality, whereas moderate gain (2.5-20kg) was protective. Results remained robust in sensitivity analyses. For person with diabetes or prediabetes prevention, maintaining normal weight in early adulthood and modest weight gain with aging is beneficial. Older adults may benefit from mild overweight to lower mortality risk. Age-specific weight management strategies should guide clinical and public health recommendations. The online version contains supplementary material available at 10.1007/s40200-026-01896-6.

BackgroundLung cancer (LC) is the most prevalent and aggressive cancer worldwide. Immune checkpoint inhibitors (ICIs) can improve treatment outcomes, but a large number of patients still fail to respond to ICIs, suggesting the presence of additional synergistic inhibitory signaling pathways in the tumor microenvironment. Interleukin-20 receptor subunit beta (IL20RB), as a single transmembrane receptor protein, plays crucial roles in host defence, autoimmune responses, and tissue repair. This study aims to investigate the relationships of IL20RB with the prognosis and immune infiltration in LC patients.MethodsWe obtained clinical data and RNA sequencing data of 1,149 samples from The Cancer Genome Atlas (TCGA). Differential expression analysis identified a total of 2,739 differentially expressed genes (DEGs), including 36 differentially expressed immune-related genes (DEIRGs). Further least absolute shrinkage and selection operator (LASSO) regression analysis identified 16 DEIRGs as significant prognostic factors, with IL20RB emerging as a key target due to its high expression in LC. Functional enrichment analysis, prognosis-related gene feature verification, immune cell infiltration analysis and protein-protein interaction (PPI) network construction were performed for IL20RB. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to detect IL20RB expression levels in cell lines, and potential drugs were screened.ResultsSurvival analysis revealed that a high IL20RB expression level was correlated with a poorer overall survival, suggesting its potential as a prognostic biomarker, with an area under the receiver operating characteristic curve (AUC) of 0.875. Functional enrichment analyses such as Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) indicated that IL20RB was involved in tumorigenesis and immunosuppressive pathways. Furthermore, IL20RB expression was positively correlated with multiple immune cells such as Th2 and natural killer (NK) CD56dim cells, and negatively correlated with eosinophils and Th17 cells. PPI networks constructed by STRING and GeneMANIA revealed the effects of IL20RB in immune receptor-mediated signaling activity and pathways. Pharmacological prediction analysis further identified that quercetin was a potential therapeutic agent targeting IL20RB.ConclusionsIL20RB is highly expressed in LC patients, correlates with tumor immune infiltration, and serves as an independent prognostic factor, suggesting its potential as a biomarker for immunotherapy and prognosis assessment in LC.

BackgroundTranscutaneous transhepatic gallbladder drainage (PTGBD) has shown significant efficacy in the treatment of elderly patients with acute cholecystitis. The goal of this study is to develop a machine learning-based web calculator aimed at predicting the optimal timing for cholecystectomy (LC) after PTGBD in elderly patients with acute cholecystitis (AC) to achieve precise personalized medicine.MethodsA retrospective analysis of 979 elderly patients with acute cholecystitis admitted to Jinzhou Central Hospital and the First Affiliated Hospital of Jinzhou Medical University from 2013 to 2024 was performed, and a total of 680 patients were included in the model development. Patients were divided into delayed (347 cases, surgery > 6 weeks post-PTGBD) and non-delayed (333 cases) groups based on the interval between PTGBD and LC. Minimal Absolute Contraction and Selection Operator (LASSO) and logistic analysis were used to determine the predictors of postponement of LC in elderly patients with AC after PTGBD. Next, we used eight ML algorithms, namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Extreme gradient boosting (XGB), Support Vector Machine (SVM), Multilayer Perceptron (MLP), K-nearest Neighbor (KNN), Gaussian Naive Bayes (GNB), to train and develop ML models using a 10x cross-validation method. The performance of the model was evaluated by a variety of indicators, including the area under the receiver operating characteristic curve (ROC), calibration curve, decision curve, PR curve, and confusion matrix. In addition, model interpretation is performed through Shapley Additive Interpretation (SHAP) analysis to clarify the importance of each feature of the model and its basis for decision-making. Finally, we chose to use the best model to develop a web-based calculator that could be used to predict the likelihood of delaying LC after PTGBD in elderly AC patients.ResultsIn multivariate logistic regression analysis, age, sex, gallbladder wall thickness, time between onset and PTGBD, white blood cell count (WBC), C-reactive protein (CRP), and neutrophil-to-lymphocyte ratio (NLR) were identified as independent predictors of delayed LC in elderly patients with AC after PTGBD. In the training set, the area under the receiver operating characteristic curve (AUC) values for these models ranged from 0.808 to 0.914, with the random forest (RF) model showing the highest AUC value. Through the evaluation of decision curve analysis (DCA), precision-recall (PR) curve and calibration curve, the RF model showed superior clinical decision support and prediction performance compared with the other seven models. Finally, we used the RF model to build an online network calculator, which aims to accurately assist doctors in making more informed and accurate clinical decisions and promote the wide application of the model in clinical practice (https://zw17786325639.shinyapps.io/Postpone/).ConclusionsThis study developed and validated an RF model network calculator based on clinical indicator information to assess the likelihood of delaying LC after PTGBD in elderly patients with acute cholecystitis. This tool is expected to assist physicians in making more appropriate clinical decisions for patients.

The valid repair of bone defects requires considering precise elements mirroring native tissue and the efficiency of treatment reality. Magnetic biomaterial‐mediated mechanotransduction emerges as a critical mechanism for enhancing osteogenic differentiation and bone regeneration. Here, enhancement of osteogenic capacity is achieved in a hydroxyapatite whisker (HAw) by integrating with magnetic Fe3O4 nanoparticles (HAw/Fe3O4). The HAw/Fe3O4 exhibited excellent magnetic responsiveness and controllable alignment along magnetic field (MF) vectors enabled precise delivery of programmed mechanical stimuli. In vitro and in vivo experiments showed that Fe3O4 nanoparticles modified HAw has satisfactory biocompatibility and osteoinductivity, while the synergistic efficacy of magnetic biomaterial with external MF stimulation significantly accelerated bone matrix mineralization. The magnetic HAw/Fe3O4 with a MF significantly increased the expression of osteogenic markers and improved bone repair. The mechanosensitive ion channel of Piezo1 enabled efficient transcellular mechanotransduction through the MAPK pathway in response to the magnetic HAw/Fe3O4 with MF stimulation. The current study demonstrates that the HAw/Fe3O4 magnetic framework ‐ developed through Fe3O4 nanoparticle integration to enhance HAw osteoinductivity via coordinated MF application and mechanobiological modulation ‐ served as an optimal therapeutic platform for delivering programmed mechanical cues to activate Piezo1‐mediated mechanotransduction cascades, ultimately accelerating bone tissue regeneration.

This study aimed to investigate the relationship between lung function and metabolic dysfunction-associated steatotic liver disease (MASLD), and the potential mediating role of type 2 diabetes. Data from the 2007 to 2012 National Health and Nutrition Examination Survey were used. Logistic regression analysis was employed to assess the association between lung function parameters [forced vital capacity (FVC), forced expiratory volume in 1 s (FEV 1 ), FEV 1 /FVC] and MASLD prevalence while exploring type 2 diabetes mediation. Further analyses included linkage disequilibrium score regression, Mendelian randomization, and meta-analysis to examine the causal relationship between lung function and MASLD, considering type 2 diabetes mediation. The results showed that higher FVC and FEV 1 levels were associated with decreased MASLD risk, with type 2 diabetes partially mediating this relationship. Genetic analyses supported a causal link between lung function and MASLD, with type 2 diabetes acting as an intermediary. However, no significant association was found between FEV 1 /FVC and MASLD. The study identified a causal relationship between lung function and MASLD, with type 2 diabetes playing a partial mediating role.

Aiming at the problem of fault propagation influence analysis, an improved fault propagation influence analysis method combining ASP and DWNodeRank algorithm is proposed based on the analysis of the influencing factors of fault propagation. In order to reflect the difference of fault propagation probability between units, the weight of directed edge in the network is used to replace the information propagation rate in ASP index; Because DWNodeRank algorithm only considers the influence between adjacent nodes, it is improved by calculating the importance influence of third-order neighborhood nodes according to the third-order influence principle; Aiming at the problem that neither of the two algorithms can fully consider the influencing factors of fault propagation, combined with their advantages, a fusion improved algorithm is proposed. Taking NC machining center as an example, it shows that the fusion improved algorithm is more suitable for fault propagation influence analysis.

Disease-free survival (DFS) is an essential indicator for evaluating the prognosis of liver transplantation (LT) in hepatocellular carcinoma (HCC) patients. Despite progress in the prediction of DFS by radiomics, only preoperative clinical features have been combined in most studies. The aim of this study was to construct a nomogram model (NM) using preoperative clinical features, radiomics, and postoperative pathological indicators for more effective prediction of DFS. This was a retrospective study of a single-center cohort comprising 139 HCC patients. Using the whole cohort, we constructed and assessed a clinical model (CM) based on alpha-fetoprotein (AFP) and alkaline phosphatase (ALP), a pathological model (PM) based on Ki-67 and tumor number, a radiomics model (RM) based on the radiomics score (Rad-score), and an NM based on the above five independent predictors. Significant correlations between the NM and DFS were observed in the training and validation cohorts. Among the four prediction models, the C-index of the NM was the highest [(training/validation cohort) CM: 0.664/0.676, PM: 0.737/0.691, RM: 0.706/0.697, NM: 0.817/0.760], and the areas under the receiver operating characteristic curves (AUCs) of the NM prediction of 1-year, 2-year, and 3-year DFS were also the highest [(training/validation cohort) 1-year, 2-year, and 3-year CM: 0.726/0.726, 0.685/0.744, 0.645/0.686, PM: 0.789/0.780, 0.801/0.748, 0.841/0.735, RM: 0.769/0.752, 0.717/0.805, 0.748/0.765, NM: 0.882/0.854, 0.867/0.849, 0.882/0.801]. The NM also exhibited the highest net clinical benefit. Based on radiomics, clinical features, and pathological indicators, the NM could be used to effectively predict DFS after LT in HCC patients, guiding the follow-up and complementary treatment.