International Journal of General Medicine最新文献

Background: Extubating from mechanical ventilation is crucial in acute respiratory distress syndrome (ARDS). Heparin-binding protein (HBP) has been closely linked to ARDS development. We aimed to evaluate the association between HBP trajectories and extubation outcomes in ARDS patients.

Patients and methods: This was a retrospective study of ARDS patients who were ready for extubation. Group-based trajectory modeling was applied to identify subgroups with similar HBP trajectories in this cohort. Logistic regression was used to elucidate the relationship between different trajectories and extubation success.

Results: Overall, this study enrolled 267 patients from September 2023 to March 2025. Five HBP trajectories were identified including traj1 (HBP stable at extremely low level), traj2 (HBP stable at low level), traj3 (HBP descending from a high level to a low level), traj4 (HBP stable at moderate level), and traj5 (HBP stable at high level). The rates of successful extubation were 86.27%, 61.91%, 71.11%, 50.00%, and 40.98% respectively (P < 0.001). In addition, Logistic regression indicated that patients in traj2, traj3, traj4, and traj5 was associated with significantly decreased extubation success compared to those in traj1 group (odd ratio [OR] = 0.239, 95% confidence interval [CI]: 0.091-0.630; OR = 0.195, 95% CI: 0.054-0.706; OR = 0.143, 95% CI: 0.056-0.368; OR = 0.081, 95% CI: 0.030-0.220, respectively).

Conclusion: In mechanically ventilated ARDS patients, distinct HBP trajectories demonstrate significant associations with extubation outcomes, suggesting their potential utility in refining extubation protocols in critical care settings.

Bee venom (BV) is a promising candidate against breast and lung, including leukemia. Leukemia is a malignancy related to blood cells that causes abnormal production of leukocytes (WBCs) from bone marrow (BM). Leukemia requires a multimodal treatment approach, including stem cell transplantation, immunotherapy, and chemotherapy. Due to several limitations of current therapies such as drug resistance, severe side effects, high costs, limited targeted treatment options, age-related restrictions, after treatment defects, and the genetic heterogeneity of leukemia, there is a need to explore alternatives such as BV, either as whole or its component(s), or its use in conjugation with other treatments. BV's components such as melittin, found as 40-60% of BV's dry mass, exhibit anti-cancer activity such as pro-apoptotic, anti-proliferative, and cell membrane disruption. BV is a potent inducer of apoptosis, while inhibiting cell survival processes such as the Akt/ERK. BV can be considered as the potent anti-leukemia candidate. Various studies have demonstrated BVs effectiveness on leukemia cell lines such as HL-60, K562, Jurkat cell line, U937 cells, CCRF-CEM, K562, THP-1 cell lines, in a dose-time-cell line-dependent manner. This review aims to comprehend the current research assessing the effectiveness of apitherapy in leukemia through in vitro studies. The limitations of present studies and future possibilities exploring the synergistic effect of BV with the conventional treatments and targeted delivery of BV aimed at enhancing the effectiveness of treating leukemia are also highlighted.

Background: There is significant heterogeneity in the proportion of molecular subtypes of endometrial cancer and its relationship with clinicopathological characteristics among different races and regions. It aims to analyze the differences in the clinicopathological characteristics of different molecular subtypes of endometrial cancer in Eastern Guangdong Province, China.

Methods: Five hundred and sixty-three endometrial cancer patients in Meizhou People's Hospital from January 2018 to August 2024 were collected. The relationship of molecular subtypes (DNA polymerase epsilon (POLE) mutant, mismatch repair deficiency (dMMR), p53 abnormal, and non-specific molecular profile (NSMP)) and clinicopathological characteristics (age, reproductive history, menopausal status, and pathological data covered histological type, tumor differentiation, muscular infiltration, lymphovascular invasion, perineural invasion) were analyzed.

Results: The molecular subtypes dMMR, p53 abnormal, POLE mutant, and NSMP were detected in 197 (35.0%), 155 (27.5%), 52 (9.2%), and 159 (28.2%) patients, respectively. There were statistically significant differences in distributions of histological types (p = 0.012, χ²= 14.073), tumor differentiation (p < 0.001, χ²= 16.457), and disease stage (p = 0.019, χ²= 9.796) in NSMP and non-NSMP cases. The proportion of POLE mutant in endometrioid carcinoma was higher than those of other histological types, while the proportion of p53 abnormal was relatively high in high-grade and highly invasive histological types. The proportion of p53 abnormal subtype was relatively high among patients with mixed carcinoma. In addition, the proportions of poor tumor differentiation in the dMMR and p53 abnormal groups were higher than that in the NSMP group.

Conclusion: The distribution of molecular subtypes among patients with different histopathological types shows significant differences. The proportion of POLE mutant type in endometrioid carcinoma is higher than that of other histological types, while the proportion of p53 abnormal type is relatively high in high-grade and highly invasive histological types such as serous carcinoma and clear cell carcinoma. It provides valuable reference for guiding the diagnosis and treatment of endometrial cancer by integrating molecular subtypes with clinicopathological characteristics.

Background: Chronic periodontitis (CP) and non-alcoholic fatty liver disease (NAFLD) are increasingly prevalent worldwide. Although mechanisms remain incompletely defined, recent studies suggest a close association between these two diseases. This review systematically outlines potential links between periodontitis and NAFLD, emphasizing their pathological mechanisms and interactions within an oral-gut-liver framework.

Methods: We reviewed observational, interventional, and mechanistic studies evaluating associations between periodontal status/treatment and NAFLD-related outcomes, integrating evidence on dysbiosis, inflammatory mediators, microbial metabolites, oxidative stress, microRNA regulation, and gut barrier function.

Results: Across epidemiological studies, periodontitis is associated with higher risk and greater severity of NAFLD. Mechanistically, oral dysbiosis, especially enrichment of oral pathobionts, is linked to hepatic steatosis and fibrosis. Translocation of microbial products and the resulting cytokine release drive systemic inflammation, impair gut barrier integrity, and induce hepatocellular injury. Microbial metabolites (such as short-chain fatty acids (SCFAs) and trimethylamine N-oxide (TMAO)) and oxidative stress contribute to metabolic dysregulation. Emerging evidence suggests that microRNAs (miRNAs) function as epigenetic regulators linking periodontal inflammation and bone remodeling to immune-metabolic pathways relevant to non-alcoholic fatty liver disease (NAFLD). However, direct evidence on whether treating periodontitis can improve NAFLD outcomes remains limited. Despite heterogeneity in study designs and diagnostic criteria, cumulative evidence supports periodontitis as a modifiable risk factor for the progression of NAFLD.

Conclusion: CP and NAFLD appear to be linked through systemic inflammation, dysbiosis, and metabolic disturbances. Future research should prioritize microbiome modulation, advance interdisciplinary care models, and develop personalized prevention and treatment strategies. Integrating oral and liver health within comprehensive management may provide new options for preventing and treating these frequently coexisting diseases.

Purpose: This study aimed to identify key risk factors and develop an explainable machine learning (ML) model for predicting early dysphagia in patients with acute ischemic stroke (AIS).

Patients and methods: In this cross-sectional study, 1041 patients with AIS were recruited from two tertiary hospitals. Participants were classified into a non-dysphagia group (n = 736) and a dysphagia group (n = 305). Feature selection was carried out using the Boruta algorithm and logistic regression. The dataset was randomly partitioned into a training set (n = 728) and a test set (n = 313) in a 7:3 ratio. Six ML models were trained with 10-fold cross-validation. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, accuracy,positive predictive value (PPV), negative predictive value (NPV), F1-score and Youden's index. Key predictors were interpreted using SHapley Additive exPlanations (SHAP) analysis.

Results: The incidence of early dysphagia with AIS was 29.3%. The Random Forest (RF) model demonstrated the best overall performance, with an AUC-ROC of 0.952 (95% CI: 0.927-0.976). The significant risk factors identified were Activities of Daily Living (ADL) grade, National Institutes of Health Stroke Scale (NIHSS) score, multifocal lesions, hypoalbuminemia, coronary heart disease, and lesion hemisphere.

Conclusion: ML models may serve as reliable assessment tools for predicting dysphagia in patients with AIS. The RF model demonstrated the best predictive performance. This predictive model could assist clinical healthcare providers in delivering early warnings and developing individualized treatment plans for high-risk patients.

Purpose: Coronary heart disease (CHD) has a significant co-morbid association with chronic kidney disease (CKD), but identification tools for the risk of concomitant CKD in patients with CHD are still lacking. The purpose of this research was to construct machine learning (ML) models for identifying undetected CKD in CHD patients.

Methods: 1786 CHD patients undergoing coronary intervention were retrospectively included. Lasso regression and multifactor logistic regression were used to screen feature variables. Five ML models, ie, logistic regression (LR), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost), were constructed. Participants were divided into the training set and validation set in a 7:3 ratio. The evaluation metrics included the area under the curve, calibration curve, and decision curve.

Results: Totally, 1786 CHD patients were enrolled and split into training (70%) and validation (30%) sets. The prevalence of CKD was 21.8% (390/1786). Multivariate logistic regression analysis showed that men, advanced age, hypertension, diabetes mellitus, history of atrial fibrillation (AF), high Gensini, low hemoglobin, low plateletcrit (PCT), high triglycerides (TG), high lipoprotein(a) (Lp(a)), hyperkalemia, high uric acid to albumin ratio (UAR), high systemic inflammation response index (SIRI), low lymphocyte to monocyte ratio (LMR), and high apolipoprotein B to apolipoprotein A1 (ApoB/ApoA1) ratio were the key clinical and laboratory test indicators of CKD. The XGBoost model performed optimally in the validation set (AUC=0.909, 95% CI 0.881 -0.937). SHapley Additive explanation analysis identified UAR, hypertension, Gensini score, age, and SIRI as the top 5 key features.

Conclusion: The XGBoost model constructed on routine clinical data was effective in identifying CKD risk in CHD patients, with UAR as a novel strong predictor. Decision curve analysis confirmed the clinical utility of the model, indicating that it may be used to guide decisions for enhanced monitoring and early intervention over a wide range of risk thresholds.

Rotator cuff injuries are frequently associated with lesions of the long head of the biceps tendon (LHBT), and the management strategies for LHBT significantly influence shoulder function recovery and pain relief in patients. This review provides a comprehensive overview of the anatomical features of the LHBT and its relationship with rotator cuff pathologies. It critically compares the clinical efficacy and complications of various treatment strategies for LHBT, including preservation, partial resection, complete tenotomy, and tendon transfer repair. By integrating recent advancements in imaging and anatomical studies, the review explores how LHBT lesions affect shoulder joint stability and function, as well as the mechanisms through which different surgical strategies impact the prognosis of rotator cuff repairs. Through a systematic analysis of the current literature, this review aims to provide a theoretical basis and practical guidance for clinicians in developing individualized treatment plans for patients with rotator cuff injuries involving the LHBT.

Purpose: Glaucoma is the leading cause of irreversible vision loss worldwide. We aimed to uncover the molecular mechanisms and regulatory networks of hub genes in human glaucoma to identify promising targets for detection and treatment.

Methods: We obtained GSE758, GSE2378, and GSE9944 datasets from the Gene Expression Omnibus database. The list of genes linked to regulated cell death (RCD) was obtained from a previous study. RCD-related differentially expressed genes (DEGs) were identified in patients with glaucoma and controls. Weighted Gene Co-Expression Network Analysis (WGCNA) and machine learning algorithms were used to identify hub genes. Gene set enrichment analysis (GSEA) was used to explore signaling pathways enriched by hub genes, and molecular docking analysis was performed to identify the gene-drug network of hub genes for potential treatment. Immunofluorescence was used to reveal the expression levels of hub genes in glaucomatous mice and controls.

Results: This study identified 358 RCD-related DEGs that distinguished healthy individuals from glaucoma patients and underscored the pivotal involvement of the immune response in human glaucoma pathogenesis. We systematically identified 33 hub genes, including PLEC, DLGAP4, Glycosylphosphatidylinositol (GPI), etc. that demonstrated significant diagnostic or treatment potential for glaucoma. The cytoskeletal regulator PLEC has emerged as a promising candidate gene associated with glaucomatous neurodegeneration with possible acting drugs.

Conclusion: We constructed a machine-learning-driven analytical framework based on diverse RCD patterns to refine molecular subtypes and druggable genes. These findings may provide novel targets for glaucoma detection and treatment.

Objective: Deep vein thrombosis (DVT) frequently occurs in the lower extremities of elderly hip - fracture patients. This study aims to develop an interpretable machine - learning model for predicting preoperative DVT risk in these patients and use the SHapley Additive exPlanations (SHAP) method to explain the model and identify significant factors.

Methods: A total of 976 patients (38 variables) were included. The dataset was randomly split into a training set (N = 683) and a validation set (N = 293). The Synthetic Minority Over - sampling Technique (SMOTE) was used to balance the training set. Logistic Regression (LR), Random Forest (RF), and Adaptive Boosting (AdaBoost) were applied to select influential factors, and Venn analysis was used to identify key variables. Five machine - learning techniques, including Extreme Gradient Boosting (XGBoost), were used to develop a predictive model. The performance of various models was evaluated to find the optimal algorithm, and the SHAP method was used for interpretation.

Results: A total of eight variables were selected as inputs for the predictive model. The XGBoost model achieved the highest performance on the training set data, with an Area Under the Curve (AUC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score of 0.975, 0.923, 0.936, 0.910, 0.909, 0.939, and 0.922, respectively. Furthermore, the calibration curve demonstrated a high level of agreement between the predicted probabilities and the observed risks, while the decision curve revealed that the XGBoost model had a higher net benefit compared to other machine learning models. Additionally, the use of the SHAP tool facilitated the interpretation of both the features and individual predictions.

Conclusion: Interpretable predictive models can help implement timely interventions and assist physicians in accurately predicting preoperative DVT risk in elderly hip - fracture patients.