International Journal of General Medicine最新文献

Objective: Recovery from acute Bell's palsy (BP) is variable and there are few predictors of response. We evaluated the usefulness of motor unit number index (MUNIX) to predict outcome in BP.

Methods: This prospective study evaluated the prognostic utility of MUNIX in 64 consecutive patients with acute unilateral BP. Within 7 days of symptom onset, participants underwent bilateral MUNIX testing of three facial muscles: orbicularis oculi muscle, zygomatic muscle, and orbicularis oris muscle. Clinical outcomes were assessed using the House-Brackmann Grading System (HBGS) by two blinded neurologists at baseline, 1 month, and 3 months. All patients received prednisolone treatment and regular rehabilitation.

Results: At 1-month follow-up, 26 patients (65%) achieved good recovery (HBGS I-II). The zygomatic muscle demonstrated superior prognostic performance, with absolute value of affected-to-unaffected side MUNIX difference in the zygomatic muscle (ΔMUNIX zygomatic muscle) >14 predicting poor recovery (AUC =0.804, 95% CI 0.667-0.940; p =0.002), showing 85% sensitivity and 79% specificity. Three-month outcomes (n=20) confirmed ΔMUNIX zygomatic muscle >16 as the optimal cutoff (AUC =0.893, 95% CI 0.748-1.000; p =0.006).

Conclusion: These findings establish MUNIX, particularly zygomatic muscle measurements, as an objective, non-invasive prognostic tool for early BP management.

Background: Cardiovascular-kidney-metabolic (CKM) syndrome stage 3 is a high-risk condition for cardiovascular disease (CVD), characterized by intertwined metabolic dysregulation, chronic inflammation, and immune dysfunction. This study aimed to evaluate the association and predictive value of the C-reactive protein-lymphocyte-albumin (CALLY) index for CVD in this population.

Methods: In a retrospective cohort of patients with CKM stage 3, the CALLY index was calculated from baseline laboratory data. Its association with incident CVD was assessed using multivariable Cox proportional hazards models. To test robustness, sensitivity and subgroup analyses were performed. Predictive performance was evaluated by time-dependent receiver operating characteristic (ROC) analysis, integrated discrimination improvement (IDI), and net reclassification improvement (NRI).

Results: Among 826 patients followed for a median of 51 months, a higher CALLY index was independently associated with a lower risk of CVD (adjusted hazard ratio 0.37, 95% CI: 0.25-0.55). The association remained robust in sensitivity and subgroup analyses. The index demonstrated superior discrimination for CVD (area under the curve 0.806, 95% CI: 0.774-0.838). The CALLY index provided significant incremental predictive value compared to using its individual components (CRP, albumin, or lymphocyte count alone).

Conclusion: A lower CALLY index is independently associated with an increased risk of CVD in patients with CKM stage 3 and exhibits robust predictive performance. This readily available composite biomarker may aid in cardiovascular risk stratification for this high-risk group.

Background: Atrial fibrillation (AF) is a common arrhythmia among patients with coronary heart disease (CHD), and inflammatory response plays a key role in its pathogenesis. The pan-immune-inflammation value (PIV), a novel composite marker reflecting systemic inflammation, has not been fully investigated for its predictive value in AF among CHD patients.

Methods: This multicenter retrospective study enrolled patients diagnosed with CHD by coronary angiography from two tertiary hospitals. Participants were categorized into AF and non-AF groups. Clinical characteristics and laboratory data were collected. Feature selection was performed using multivariate logistic regression, and significant predictors were incorporated into two models: extreme gradient boosting (XGBoost) and multilayer perceptron (MLP). Model performance was evaluated by area under the ROC curve (AUC) and calibration analysis. Model interpretability was assessed using SHAP (SHapley Additive exPlanations) values, and partial dependence plots (PDPs) were applied to explore variable interactions.

Results: Compared with the non-AF group, the AF group had significantly higher levels of PIV, age, AST, WBC, and TBIL. Logistic regression identified PIV, age, and diabetes as independent predictors of AF, while sex, left main coronary artery disease (LM), and AST showed borderline significance. The XGBoost model achieved superior performance (AUC = 0.79 in training and 0.73 in testing) compared to the MLP model (AUC = 0.75 and 0.69, respectively), with better calibration consistency. SHAP analysis indicated that PIV was the most influential feature, with higher values associated with increased AF risk. PDPs further demonstrated synergistic effects between PIV and other key variables.

Conclusion: PIV is a valuable predictor of AF in CHD patients. The XGBoost model outperformed the deep learning model in this context and may serve as a robust tool for individualized AF risk assessment.

Objective: This study aimed to develop and validate a prognostic nomogram integrating clinical and laboratory variables to predict prolonged hospital stay in patients undergoing surgery for gastrointestinal (GI) perforation, facilitating early risk stratification and informed clinical decision-making.

Patients and methods: A retrospective retrospective single-center study included 164 surgical patients with GI perforation from 2022-2024. Variables encompassed demographics, perforation characteristics, and preoperative/postoperative laboratory markers. The least absolute shrinkage and selection operator (LASSO) regression identified key predictors, followed by multivariate logistic regression to construct a nomogram. Model performance was evaluated using the receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).

Results: Upper GI perforation (OR=2.93, 95% CI:1.23-6.98), smaller perforation diameter (OR=0.48, 95% CI:0.28-0.82), and lower preoperative albumin (OR=1.10 per unit increase, 95% CI:1.03-1.17) independently predicted prolonged hospitalization. The nomogram demonstrated good discrimination (training AUC=0.75; validation AUC=0.79) and calibration. DCA confirmed clinical utility, with net benefit surpassing "treat all" or "treat none" strategies across risk thresholds.

Conclusion: In summary, we developed and validated a nomogram that effectively identifies patients at high risk for prolonged hospitalization after GI perforation surgery by integrating three routinely available clinical parameters. This tool aids in optimizing resource allocation and personalized perioperative management. Further multicenter validation is warranted to enhance generalizability and incorporate dynamic biomarkers.

Purpose: Venous thromboembolism (VTE) poses a significant health risk for the elderly. This study aims to evaluate the knowledge, attitudes, and practices (KAP) concerning VTE among elderly individuals.

Patients and methods: This cross-sectional study was conducted between September and October 2024 among elderly inpatients and outpatients at the Department of Gerontology, Shanghai Sixth People's Hospital in China, involving 540 participants. Demographic characteristics and KAP scores were collected using a self-designed questionnaire. Confirmatory factor analysis demonstrated acceptable construct validity, and a cutoff of 70% of the maximum score was applied to define adequate knowledge, positive attitudes, and proactive practices. Univariate and multivariate regression analyses were used to identify factors associated with KAP scores, and structural equation modeling (SEM) was performed to examine the direct and indirect relationships among knowledge, attitudes, and practices.

Results: Among respondents, 272 (50.4%) were male, and 67 (12.4%) reported a history of VTE. Mean scores were 8.04 ± 5.22 (knowledge), 40.99 ± 4.10 (attitude), and 28.77 ± 5.12 (practice), indicating inadequate knowledge, generally positive attitudes, and moderately proactive practices. SEM revealed that knowledge significantly influenced both attitude (β = 0.365, P < 0.001) and practice (β = 0.306, P < 0.001), while attitude also affected practice (β = 0.219, P < 0.001). Knowledge further had an indirect effect on practice via attitude (β = 0.080, P < 0.001).

Conclusion: These findings highlight critical knowledge gaps among elderly individuals, particularly in mechanical prophylaxis and symptom recognition, underscoring the urgent need for targeted educational interventions to improve VTE prevention strategies.

Purpose: To investigate the prognostic value of cell cyclin A2 (CCNA2) in lung adenocarcinoma (LUAD) and to explore its mechanisms in promoting cancer progression.

Patients and methods: In this study, we employed an integrated strategy combining bioinformatics, clinical analysis and molecular biology to elucidate the role of CCNA2 in LUAD. First, comprehensive bioinformatics analyses were performed using public datasets. This included detecting the differential expression of CCNA2 in LUAD versus normal tissues, analyzing its correlation with patient survival and clinical characteristics, and employing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) analysis to predict the functions of CCNA2-associated genes. The relationship between CCNA2 expression and immune infiltration was further examined via the tumor immune estimation resource (TIMER) platform. The expression level of CCNA2 was also confirmed through reverse transcription-quantitative PCR and Western blotting. Additionally, the biological function of CCNA2 was evaluated by constructing an in vitro transfection model.

Results: The results of the present study indicated that CCNA2 was upregulated in LUAD tissues. Cox regression analysis revealed that CCNA2 upregulation is a independent prognostic biomarker for LUAD. Additionally, CCNA2 was markedly associated with immune cell infiltration and immune checkpoint molecules. The results of in vitro experiments confirmed that knockdown of CCNA2 significantly inhibited the proliferation, invasion and migration of H1975 and H1299 cells. Furthermore, CCNA2 was found to promote the invasion and migration of lung cancer cells through the PI3K/AKT signaling pathway.

Conclusion: The present research identified the prognostic signature and biological function of CCNA2 in LUAD, which suggested that CCNA2 may be a potential prognostic biomarker and a pivotal oncogenic driver for this disease.

Background: The therapeutic effects of compound herbs (Radix Paeoniae Rubra, Radix Cirsii Japonici, Gentianae Radix Et Rhizoma, and Cardeniae Fructus) on stomach adenocarcinoma (STAD) remain unclear.

Methods: Active ingredients and their targets from the herbal combination were obtained using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. STAD-related targets were collected from the GeneCards database, and the TCGA-STAD dataset was downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) between STAD and normal tissues were screened. The intersection of DEGs, drug targets, and STAD-related targets was taken to determine key targets. A protein-protein interaction (PPI) network was built and hub genes were identified. Functional enrichment analysis and molecular docking of the hub genes were performed. The Cell Counting Kit-8 (CCK8) assay was used to evaluate the effects of the active ingredients on the proliferation of Stomach Gastric Carcinoma cell line 7901 (SGC-7901) and MaKuNo cell line 45 (MKN-45) cells. The Transwell assay was used to evaluate the effect of quercetin on the migration ability of SGC-7901 and MKN-45 cells.

Results: A total of 67 key targets were obtained, among which five hub genes-ESR1 (Estrogen receptor 1), FOS (FBJ murine osteosarcoma viral oncogene homolog), HSP90AA199 (Heat shock protein 90 alpha family class A member 1), JUN (Avian sarcoma virus 17 oncogene homolog), and MMP9 (Matrix Metallopeptidase 9)-were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that these hub genes were significantly associated with the T Helper 17 (Th17) Cell differentiation pathway. Molecular docking predictions suggested that active ingredients such as quercetin could bind effectively to the hub genes, with quercetin showing the strongest binding affinity to FOS. Cell experiments further confirmed that quercetin exhibited the most potent inhibitory effect on the proliferation of STAD cells, with a half-maximal inhibitory concentration (IC50) value of 4 μM. Furthermore, quercetin can significantly inhibit the migration of SGC-7901 and MKN-45 cells.

Conclusion: This study identified five key targets and active compounds in herbal compounds for STAD treatment. These results suggest that quercetin may inhibit STAD progression by targeting FOS, and may have therapeutic potential.

Purpose: Timely reperfusion is critical for improving outcomes in patients with acute myocardial infarction (AMI), as every delay raises the incidence of complications and mortality. Therefore, we aimed to develop a machine-learning model that quantifies the risk of pre-hospital decision-making delay and visualizes how individual determinants modulate this risk.

Patients and methods: This retrospective study included 594 AMI patients admitted to hospitals in Hainan from January to August 2023. Data were collected via medical systems and surveys. We used the Elastic Net and Boruta algorithms for feature selection and hyperparameter optimization with grid search and 10-fold cross-validation. Six machine learning models were developed: logistic regression, random forest, support vector machine, XGBoost, decision tree, and naive Bayes. The primary metric was the Area Under the Curve (AUC), and SHapley Additive exPlanations (SHAP) were used to assess feature importance.

Results: The medical decision-making delay rate was 61.78%, with a median decision time of 3.98 hours. All models showed good predictive performance, with the random forest model excelling, achieving an AUC of 0.91, accuracy of 0.92, recall of 0.98, F1 score of 0.93, and specificity of 0.81. SHAP analysis revealed that pain severity, disease type, and history of myocardial infarction were the most significant predictors of delay. Pain severity had a nonlinear relationship with delay risk, while disease type and prior infarction history showed complex interactions.

Conclusion: Machine learning models, especially random forest, accurately predict the risk of delayed medical decision-making in AMI patients and reliably delineate the key drivers of such delay, thereby informing targeted clinical interventions.

Background: The occurrence of gastric cancer is a complex pathological process leading to multiple abnormalities in clinical laboratory indicators. Machine learning techniques can make it easy to handle millions of variables to make more accurate predictions and diagnoses of diseases.

Methods: Clinical data from gastric cancer patients in a single-center who underwent surgery between 2016 and 2023 were collected. Five machine learning algorithms (extreme gradient boosting, XGBoost; random forest, RF; support vector machine-recursive feature elimination, SVM-RFE; light gradient boosting machine, LGBM; and recursive partitioning, rpart) were utilized to develop diagnostic models. Among the date, 60% were randomly selected to train the models, while the remaining 40% were used for testing. We used the area under the receiver operating characteristic curve (AUROC), F1-score value, sensitivity, and specificity to evaluate the performance of models.

Results: The XGBoost algorithm showed the best performance in gastric cancer diagnosis, with significantly higher area under curve (AUC) (combining blood indicators and pathological parameters, AUC=0.9909) value than other models. Glutathione reductase (GR), carbohydrate antigen 724 (CA724), erythrocytes (RBC), carbohydrate antigen 242 (CA242), and albumin (ALB) contributed the most to the diagnosis. The tumor size were independent risk factors for early gastric cancer.

Conclusion: Machine learning combined blood indicators and pathological parameters could predict gastric cancer risk more accurately. The XGBoost model had the best diagnostic performance. The study provides confirmatory data support for the preclinical implementation of the model.

Objective: This study explored the level of soluble CD13 (sCD13) and its correlation with angiogenic factors, evaluating the predictive efficacy of sCD13 in wet age-related macular degeneration (wAMD).

Methods: 200 patients were included (58 in Non AMD group, 42 in Early AMD group, and 100 in wAMD group). Detailed routine and ophthalmologic examinations were performed on all subjects, and the central retinal thickness (CRT) and ganglion cell-inner plexiform layer (GCIPL) were determined. The concentration of sCD13 was compared. The correlation of sCD13 with PDGF, hsCRP and IL-8 was analyzed. ROC curves were plotted and the diagnostic value of sCD13 was assessed by area under the curve (AUC).

Results: The sCD13 concentration of patients in the wAMD group (20.41 ± 5.86 U/mL) was higher. Age, history of smoking, CRT, hsCRP and IL-8 were higher in the wAMD group, while mean GCIPL, BCVA, and PDGF were lower. sCD13 was positively correlated with hsCRP (r = 0.505) and IL-8 (r = 0.193) and negatively correlated with PDGF (r = -0.241). sCD13 had predictive efficacy in distinguishing wAMD from non AMD and early AMD, with AUC values of 0.74 and 0.61, respectively (P < 0.05).

Conclusion: sCD13 concentration in the affected eyes of wAMD patients is abnormally elevated and associated with elevated serum hsCRP and IL-8 levels and decreased PDGF. These results suggest that elevated sCD13 may promote the development of wAMD, emphasizing the importance of early control of sCD13 levels.