International Journal of General Medicine最新文献

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.

Background: Inflammatory bowel disease (IBD) encompasses Crohn's disease (CD) and ulcerative colitis (UC) and represents a heterogeneous spectrum of chronic intestinal inflammation with no exclusive etiology. Emerging evidence underscores that IBD arises from complex interactions between host factors and microbial communities. The disruption of microbial homeostasis facilitates the colonization and invasion of opportunistic pathogens within the gut, precipitating an exaggerated host immune response and driving disease progression. While extensive research has elucidated the role of the gut microbiota in IBD pathogenesis, the contribution of the oral microbiota to this process is garnering increasing attention. Oral microbes can translocate to the intestine via the swallowing of saliva, and harmful oral bacteria and proinflammatory immune cells from the oral mucosa may migrate to the gut, eliciting immune activation. This review explores the emerging role of the oral microbiota in IBD pathogenesis and synthesizes recent advancements in understanding the intricate relationship between oral microbial communities and IBD.

Purpose: Failure to wean from mechanical ventilation can lead to prolonged hospital stays, increased incidence of ventilator-associated pneumonia, and higher mortality rates. This study aimed to explore the effectiveness of the diaphragm contraction pressure index (DCPI) in predicting weaning outcomes in patients undergoing mechanical ventilation, providing a scientific basis for successful weaning in clinical practice.

Patients and methods: This prospective observational study included 286 individuals in the derivation cohort and 104 patients in the validation cohort, all of whom completed the spontaneous breathing trial (SBT). During SBT, ultrasound was used to quantify the right hemidiaphragm excursion (DE), diaphragm thickness (DTF) after inspiration and expiration, and DCPI. MIP values were gathered from the mechanical ventilator when the patients breathed peacefully. The derivation cohort determined the cut-off value of DCPI and compared these ultrasound diaphragm parameters. The validation cohort contributes to verifying the accuracy of DCPI.

Results: The weaning success group's DCPI in the derivation cohort was significantly higher than that of the weaning failure group (36.67% ± 7.02% vs 24.03% ± 5.78%, P < 0.001). While the area under the receiver operating characteristic curve (ROC) (AUC) of DE, DTF, and MIP was 0.698 (95% CI, 0.615-0.771, P < 0.001), 0.770 (95% CI, 0.693-0.837, P < 0.01), and 0.811 (95% CI, 0.737-0.872, P < 0.001), the ROC of DCPI was 0.954 (95% CI, 0.905-0.982, P < 0.001), indicating good predictive performance for weaning success. The DCPI had a sensitivity of 94.1% and a specificity of 90.8%, with the ideal cut-off value set at ≥30.0%. Similarly, in the validation cohort, the AUC of DCPI for the predicted value is 0.952 (95% CI, 0.854-0.992, P < 0.001).

Conclusion: Compared with DTF, DE, and MIP, DCPI dramatically improves the accuracy of predicting successful weaning.

Trial registration: No. ChiCTR2100052470, Registered 28 October 2021.

Background: Residing at high altitude is associated with increased red blood cell volume (RBCV) and reduced plasma volume (PV), changes that complicate interpretation of hemoglobin (Hb) and hematocrit (Hct). Here, we assess blood volume (BV), RBCV, PV, and hemoglobin mass (Hbmass) in individuals living at moderate altitude with the aim of deriving reference range for use in clinical practice.

Methods: One hundred and fifty-eight moderate-altitude residents (ALT; 51 women) from Abha, Saudi Arabia (2250 m above sea level) were recruited and compared to 40 control subjects (CON; 0 women). Blood volumes and Hbmass were determined by carbon monoxide rebreathing. Lean body mass (LBM) was quantified by dual-energy X-ray absorptiometry and adjusted for.

Results: When normalized to LBM, ALT men displayed an 8% increase in BV (109 vs 101 mL, p = 0.003), 14.8% increase in RBCV (54 vs 47 mL, p < 0.001), and 16.2% increase in Hbmass (18 vs 15 g, p < 0.001) compared to CON. ALT women exhibited lower BV (3928 vs 5476 mL), RBCV (1684 vs 2690 mL), PV (2276 vs 2767 mL), and Hbmass (558 vs 886 g), than ALT men, (p < 0.001 for all). When expressed per body weight, ALT men have higher RBCV (33 vs 30 g), lower PV (34 vs 40 mL) and no different BV (68 vs 69 mL, p = 0.443). When expressed per LBM, gender differences in RBCV and Hbmass disappeared (RBCV: 54 vs 51 mL/kg^LBM, p = 0.164; Hbmass: 18 vs 17 g/kg^LBM, p = 0.201). Women have significantly higher BV (120 vs 109 mL/kg^LBM) owing to higher PV (72 vs 55 mL/kg^LBM).

Conclusion: Moderate altitude is associated with a higher BV owing to a higher RBCV, which also explains the elevated Hb. A reference range based on the 5% and 97.5% percentiles of blood volumes and Hbmass adjusted for LBM is suggested for use in clinical practice.

Background: The polymorphisms of the Toll-like receptor 4 (TLR4) gene are associated with lipid levels, such as serum total cholesterol, low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG). The aim of this study was to detect the association of the six polymorphisms in TLR4 gene and serum lipid levels and the risk of ischemic stroke (IS) in a Southern Chinese Han population.

Methods: Genotypes of six polymorphisms in TLR4 gene in 372 subjects (IS, 186 and healthy controls, 186) were determined by the Snapshot Technology. The relationship between TLR4 polymorphisms and serum lipid levels, risk of IS were analyzed.

Results: The levels of fasting blood glucose and triglyceride were higher, and the high-density lipoprotein cholesterol (HDL-C) level was lower in IS cases than those in controls. The allelic frequencies of TLR4 gene rs11536889 SNP (p=0.037) and rs1927914 SNP (p=0.036) were different between the IS and control groups. The rs11536889 C allele carriers had an increased risk of IS (odds ratio (OR)=1.278, 95% confidence interval (CI) =1.013-1.784, p=0.037 for C vs G alleles), and the G allele carriers of rs1927914 had a decreased risk of IS (OR=0.695, 95% CI=0.534-0.949, p=0.036 for G vs A allele) in the southern Chinese Han population.

Conclusion: The TLR4 rs11536889 and rs1927914 SNPs may be associated with decreased risk of IS in the Chinese population.