Mendelian randomization study reveals causal effects of specific gut microbiota on the risk of interstitial cystitis/bladder pain syndrome (IC/BPS).

Evidence from previous studies have demonstrated that gut microbiota are closely associated with occurrence of interstitial cystitis/bladder pain syndrome (IC/BPS), yet the causal link between the two is not well known. In this study, we performed a two-sample Mendelian randomization (MR) analysis to determine the possible causal association between gut microbiota with IC/BPS. Gut microbiota summary level data were derived from the genome-wide association study (GWAS) conducted by MiBioGen and the IC/BPS GWAS summary level data were obtained from the GWAS Catalog. Next, we performed an MR study to investigate the causal link between gut microbiota and IC/BPS. The primary method for causal analysis was the inverse variance weighted (IVW), and the MR results were validated through multiple sensitivity analyses. A positive association was found between IC/BPS and eight gut microbial taxa, including genus Bacteroides, genus Haemophilus, genus Veillonella, genus Coprococcus1, genus Butyricimonas, family Bacteroidaceae, family Christensenellaceae, and order Lactobacillales. Sensitivity analysis revealed lack of significant pleiotropy or heterogeneity in the obtained results. This MR analysis reveals that a causal association exists between some gut microbiota with IC/BPS. This finding may is expected to guide future research and development of IC/BPS preventions and treatments based on the bladder-gut axis. However, given the clinical complexity and diagnostic challenges of IC/BPS, along with the limitations of using large-scale GWAS summary data for analysis, our MR results require further validation through additional research.

Association of the hemoglobin, albumin, lymphocyte, and platelet score with the risk of Erectile dysfunction: a cross-sectional study.

Erectile dysfunction (ED) is related to nutritional and inflammatory factors. The hemoglobin, albumin, lymphocyte, and platelet score (HALP), a new index reflecting the nutritional and inflammatory status, has been associated with a higher risk of diabetic retinopathy, particularly at lower values (≤ 42.9). However, studies focusing on the relationship between HALP and ED risk are scarce. Hence, this study aimed to investigate the association between HALP and ED. Data were extracted from the National Health and Nutrition Examination Survey (NHANES) conducted between 2001 and 2004. Based on self-reported data, participants were classified into either the ED group or the non-ED group. Next, the HALP score was categorized into four quartiles (Q1-4). Weighted multivariate regression analysis was performed to assess the relationship between categorical HALP and ED risk. Additionally, restricted cubic spline (RCS) analysis was conducted to examine the association between continuous HALP scores and ED risk. Furthermore, subgroup analyses were conducted to examine the association between categorical HALP and the risk of ED based on age, as well as the status of hypertension, diabetes and cardiovascular disease. Finally, a mediation analysis was carried out to investigate the mediating effect of HALP and related parameters on the association between urinary cobalt levels and ED. Initially, the data of 21,161 participants were collected. After implementing the inclusion and exclusion criteria, 3406 participants were included in the final analyses. Weighted multivariate regression analysis demonstrated that the Q4 HALP group was associated with a lower risk of ED (OR 0.96, 95% confidence intervals 0.92-1.00, P = 0.037). Meanwhile, RCS analysis showed that HALP was nonlinearly associated with the risk of ED. In addition, subgroup analyses demonstrated that participants in the Q3/4 HALP group had a significantly lower ED risk than those in the Q1 group among patients aged ≥ 50 years, as well as those with hypertension and diabetes. Lastly, mediation analysis revealed that HALP and its associated parameters had a marginal average causal mediation effect on the relationship between urinary cobalt levels and ED risk (P > 0.05). In US adults, high HALP scores were correlated with a lower risk of ED. The relationship was more pronounced in participants aged ≥ 50 years with hypertension and diabetes. Furthermore, HALP and its parameters may not mediate the association between urinary cobalt levels and ED risk.

Apolipoprotein E is a Potential Biomarker for Predicting Cancer Prognosis and is Correlated with Immune Infiltration.

Background: Apolipoprotein E (APOE) is a polymorphic protein that plays a role in lipoprotein transformation and metabolism. It is involved in numerous physiological processes within the body and is closely associated with tumor growth and metastasis. However, the role of APOE in pan-cancer has yet to be evaluated. Therefore, studying the association between APOE and various cancer types is crucial for providing a basis for individualized treatment strategies and clinical prognosis assessment. Methods: We investigated the diagnostic and prognostic significance of APOE across 33 tumor types, as well as its correlation with tumor mutation burden (TMB) and microsatellite instability (MSI). Additionally, we employed the ESTIMATE and CIBERSORT algorithms to analyze the potential impact of APOE on the immune system. Furthermore, gene set enrichment analysis (GSEA) was conducted to explore its underlying physiological function. Results: Based on observations from a pan-cancer dataset, APOE expression was significantly different between cancer and normal tissues, and was simultaneously associated with survival outcomes in terms of cancer type, clinical annotation, TMB, MSI, and TICs abundance. In addition, the results also showed that expression of APOE may respond to a variety of cancer chemotherapy. Conclusion: The findings from this study strongly indicate a close association between APOE and tumor development. Moreover, APOE shows promise as a potential biomarker for predicting prognosis and response to immunotherapy in patients with pan-cancer.

Deciphering the molecular classification of pediatric sepsis: integrating WGCNA and machine learning-based classification with immune signatures for the development of an advanced diagnostic model.

Introduction: Pediatric sepsis (PS) is a life-threatening infection associated with high mortality rates, necessitating a deeper understanding of its underlying pathological mechanisms. Recently discovered programmed cell death induced by copper has been implicated in various medical conditions, but its potential involvement in PS remains largely unexplored. Methods: We first analyzed the expression patterns of cuproptosis-related genes (CRGs) and assessed the immune landscape of PS using the GSE66099 dataset. Subsequently, PS samples were isolated from the same dataset, and consensus clustering was performed based on differentially expressed CRGs. We applied weighted gene co-expression network analysis to identify hub genes associated with PS and cuproptosis. Results: We observed aberrant expression of 27 CRGs and a specific immune landscape in PS samples. Our findings revealed that patients in the GSE66099 dataset could be categorized into two cuproptosis clusters, each characterized by unique immune landscapes and varying functional classifications or enriched pathways. Among the machine learning approaches, Extreme Gradient Boosting demonstrated optimal performance as a diagnostic model for PS. Discussion: Our study provides valuable insights into the molecular mechanisms underlying PS, highlighting the involvement of cuproptosis-related genes and immune cell infiltration.

Machine learning-based overall and cancer-specific survival prediction of M0 penile squamous cell carcinoma：A population-based retrospective study.

Background: Penile cancer is a rare tumor and few studies have focused on the prognosis of M0 penile squamous cell carcinoma (PSCC). This retrospective study aimed to identify independent prognostic factors and construct predictive models for the overall survival (OS) and cancer-specific survival (CSS) of patients with M0 PSCC. Methods: Data was extracted from the Surveillance, Epidemiology, and End Results database for patients diagnosed with malignant penile cancer. Eligible patients with M0 PSCC were selected according to predetermined inclusion and exclusion criteria. These patients were then divided into a training set, a validation set, and a test set. Univariate and multivariate COX regression analyses were initially performed to identify independent prognostic factors for OS and CSS in M0 PSCC patients. Subsequently, traditional and machine learning prognostic models, including random survival forest (RSF), COX, gradient boosting, and component-wise gradient boosting modelling, were constructed using the scikit-survival framework. The performance of each model was assessed by calculating time-dependent area under curve (AUC), C-index, and integrated Brier score (IBS), ultimately identifying the model with the highest performance. Finally, the Shapley additive explanation (SHAP) value, feature importance, and cumulative rates analyses were used to further estimate the selected model. Results: A total of 2, 446 patients were included in our study. Cox regression analyses demonstrated that age, N stage, and tumor size were predictors of OS, while the N stage, tumor size, surgery, and residential area were predictors of CSS. The RSF and COX models had a higher time-independent AUC and C-index, and lower IBS value than other models in OS and CSS prediction. Feature importance analysis revealed the N stage as a common significant feature for predicting M0 PSCC patients' survival. The SHAP and cumulative rate analyses demonstrated that the selected models can effectively evaluate the prognosis of M0 PSCC patients. Conclusion: In M0 PSCC patients, age, N stage, and tumor size were predictors of OS. In addition, the N stage, tumor size, surgery, and residential area were predictors of CSS. The machine learning-based RSF and COX models effectively predicted the prognosis of M0 PSCC patients.