International Journal of General Medicine最新文献

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.

Background: Traumatic brain injury (TBI) is a leading cause of global disability and mortality. Tetramethylpyrazine (TMP), an active compound from Chuanxiong, holds promise for treating cerebrovascular diseases, but its precise mechanism of action against TBI remains incompletely understood. This study aimed to elucidate the therapeutic effects and underlying mechanisms of TMP in TBI.

Methods: Potential targets of TMP against TBI were identified using Swiss Target Prediction, PharmMapper, and GeneCards databases. Core targets and mechanisms were predicted through network pharmacology, molecular docking, and molecular dynamics (MD) simulations. These computational predictions were then experimentally validated in a rat TBI model, employing behavioral tests, ELISA, RT-qPCR, and Western blot analysis.

Results: Through network pharmacology analysis, 39 potential targets associated with TMP were identified. Molecular docking and MD simulations manifested that key genes like MMP3, MMP2, MMP13, and GSK3B, showed a strong binding affinity to TMP. GO analysis and KEGG analysis corroborated that such targets strongly related to the IL-17 signaling pathway and the relaxin signaling pathway. In vivo tests proved that TMP could improve the modified Neurological Severity Score (mNSS) and foot defect test scores among rats. ELISA confirmed that TMP could decrease the expression of inflammatory factors, encompassing interleukin 1 beta (IL-1β), interleukin 6 (IL-6), interleukin 17A (IL-17A), and tumor necrosis factor-alpha (TNF-α). Furthermore, RT-qPCR analysis exhibited that the levels of MMP3, MMP2, MMP13, and GSK3B were increased within the rat cortex after TBI. Significantly, TMP treatment alleviated such upregulation. Western blot analysis validated that TMP down-regulated the expression of p-GSK3β (Ser9), active MMP13, active MMP3, and P65 NF-κB proteins after TBI, while TMP increased the expression of occludin protein.

Conclusion: This study demonstrates that TMP exerts therapeutic effects on TBI by targeting the IL-17 and relaxin signalling pathways, providing evidence for its potential as a clinical therapy.

Objective: The recurrence rate of post-tuberculosis chronic pulmonary aspergillosis (post-TB CPA) is alarmingly high. This study aims to establish a risk prediction model utilizing machine learning algorithms to forecast the one-year recurrence risk of post-TB CPA.

Methods: This retrospective study included all patients diagnosed with pulmonary tuberculosis complicated by chronic pulmonary aspergillosis at Wuhan Pulmonary Hospital in 2022. Ultimately, 220 patients were included for the significance analysis.The Least Absolute Shrinkage and Selection Operator LASSO regression analysis was utilized to select 8 variables associated with the recurrence of tuberculosis complicated by chronic pulmonary aspergillosis. Four machine learning algorithms were compared to predict the recurrence risk in patients with this complication, with their performance evaluated using the receiver operating characteristic curve, area under the curve (AUC), calibration curve analysis, and decision curve analysis.

Results: LASSO regression analysis identified chronic obstructive pulmonary disease (COPD), chronic fibrotic pulmonary aspergillosis (CFPA), progressive pleural hypertrophy, fungal culture results, age, disease duration, emphysema and treatment duration as factors related to the recurrence risk of tuberculosis complicated by chronic pulmonary aspergillosis. The logistic regression model demonstrated the best performance, it outperformed the other three models by achieving the highest AUC of 0.779 on the internal validation set and 0.819 in the test cohort. The calibration curve indicated a strong correlation between the actual and predicted probabilities, while the decision curve analysis revealed significant clinical benefits.

Discussion: In this study, we developed a disease recurrence prediction model using machine learning techniques. This model aims to assist clinicians in identifying the most relevant risk factors associated with the recurrence of tuberculosis complicated by chronic pulmonary aspergillus. It facilitates the formulation of targeted and effective re-examination plans for discharged patients, ultimately reducing the recurrence rate after discharge and enhancing the quality of life for these patients.

Purpose: To investigate the synergistic effect of Acupuncture combined with Chinese Herbal Medicine (CHM) in treating cerebral infarction (CI) rats, focusing on its impact on gut short-chain fatty acids (SCFAs) and serum interleukin-17 (IL-17) expression.

Methods: 36 male SD rats were divided into 6 groups (n=6): Sham, Model, Acupuncture, CHM, Combined Therapy, and Western Medicine (positive control). The CI model was established by middle cerebral artery occlusion (MCAO). The Combined group received both acupuncture (at bilateral "Hegu" (LI4), "Taichong" (LR3), "Zusanli" (ST36), and "Fenglong" (ST40)) and CHM (oral Banxia Baizhu Tianma Decoction combined with Taoren Honghua Decoction). Treatment lasted 14 days. Neurological deficit scores (Longa and horizontal wooden stick tests) were assessed. SCFA content in colonic contents was analyzed by gas chromatography, and serum IL-17 levels by ELISA. Subsequently, the correlation between SCFAs and IL-17 levels was analyzed.

Results: The combined therapy group showed significantly better improvements in neurological function compared to all single-therapy groups (P < 0.05). Compared to the model group, the total content of SCFAs (including acetic acid, propionic acid, and butyric acid) was significantly lower in the model group, while IL-17 levels were significantly elevated. All treatment groups showed increased SCFA content and decreased IL-17 levels, with the combined group demonstrating superior effects compared to single therapies (P < 0.05). A significant negative correlation was found between total SCFAs, acetic acid, propionic acid, and butyric acid, and serum IL-17 (R² = 0.601-0.711, P < 0.05).

Conclusion: The combination of acupuncture and CHM significantly improved neurological deficits in CI rats. This synergistic effect is likely associated with the regulation of gut microbiota-derived SCFAs and the suppression of IL-17-mediated neuroinflammation.