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This study shows that ACEIs can not significantly improve walk distance or the age-related decline of muscle strength for older participants in clinical trials.

Background: Hepatocellular carcinoma (HCC) is one of the highly heterogeneous cancers that lacks an effective risk model for prognosis prediction. Therefore, we searched for angiogenesis-related immune genes that affected the prognosis of HCC to construct a risk model and studied the role of this model in HCC.Methods: In this study, we collected the transcriptome data of HCC from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) database. Pearson correlation analysis was performed to identify the association between immune genes and angiogenesis-related genes. Consensus clustering was applied to divide patients into clusters A and B. Subsequently, we studied the differentially expressed angiogenesis-related immune genes (DEari-genes) that affected the prognosis of HCC. The most significant features were identified by least absolute shrinkage and selection operator (LASSO) regression, and a risk model was constructed. The reliability of the risk model was evaluated in the TCGA discovery cohort and the ICGC validation cohort. In addition, we compared the novel risk model to the previous models based on ROC analysis. ssGSEA analysis was used for function evaluation, and pRRophetic was utilized to predict the sensitivity of administering chemotherapeutic agents.Results: Cluster A patients had favorable survival rates. A total of 23 DEari-genes were correlated with the prognosis of HCC. A five-gene (including BIRC5, KITLG, PGF, SPP1, and SHC1) signature-based risk model was constructed. After regrouping the HCC patients by the median score, we could effectively discriminate between them based on the adverse survival outcome, the unique tumor immune microenvironment, and low chemosensitivity.Conclusion: The five-gene signature-based risk score established by ari-genes showed a promising clinical prediction value.

Gastric cancer (GC) is one of the most common malignancies worldwide. Despite rapid advances in systemic therapy, GC remains the third leading cause of cancer-related deaths. We aimed to identify a novel prognostic signature associated with FAT2 mutations in GC. We analyzed the expression levels of FAT2-mutant and FAT2-wildtype GC samples obtained from The Cancer Genome Atlas (TCGA). The Kaplan-Meier survival curve showed that patients with FAT2 mutations showed better prognosis than those without the mutation. Sixteen long non-coding RNAs (lncRNAs) and 62 messenger RNAs (mRNAs) associated with FAT2 mutations were correlated with the prognosis of GC. We then constructed a 4-mRNA signature and a 5-lncRNA signature for GC. Finally, we identified the most relevant RP11-21 C4.1/SVEP1 gene pair as a prognostic signature of GC that exhibited superior predictive performance in comparison with the 4-mRNA or 5-lncRNA signature by weighted gene correlation network analysis (WGCNA) and Cox proportional hazards regression analysis. In this study, we constructed a prognostic signature of GC by integrative genomics analysis, which also provided insights into the molecular mechanisms linked to FAT2 mutations in GC.

Background and Aim: Gastric cancer (GC) is the common leading cause of cancer-related death worldwide. Immune-related genes (IRGs) may potentially predict lymph node metastasis (LNM). We aimed to develop a preoperative model to predict LNM based on these IRGs. Methods: In this paper, we compared and evaluated three machine learning models to predict LNM based on publicly available gene expression data from TCGA-STAD. The Pearson correlation coefficient (PCC) method was utilized to feature selection according to its relationships with LN status. The performance of the model was assessed using the area under the curve (AUC) and F1 score. Results: The Naive Bayesian model showed better performance and was constructed based on 26 selected gene features, with AUCs of 0.741 in the training set and 0.688 in the test set. The F1 score in the training set and test set was 0.652 and 0.597, respectively. Furthermore, Naive Bayesian model based on 26 IRGs is the first diagnostic tool for the identification of LNM in advanced GC. Conclusion: These results indicate that our new methods have the value of auxiliary diagnosis with promising clinical potential.

Background Malnutrition is an under recognized, but common issue in elderly patients. This study aimed to investigate the prevalence of poor nutritional status and identify comprehensive geriatric assessment‐based clinical factors associated with increased malnutrition risk to assessing malnutrition risk in hospitalized elderly patients in China. Methods A total of 365 elderly hospitalized patients (178 women, 76.37 ± 7.74 years) undertook a comprehensive geriatric assessment (CGA), and have their nutritional status assessed using the short‐form mini‐nutritional assessment. Results Among 365 patients, 32 (8.77%) were malnourished and 112 (30.68%) were at risk of malnutrition. A logistic regression analysis showed that age (odds ratio [OR], 1.59; 95% confidence interval [CI], 1.13‐2.23), alcohol consumption (OR, 2.04; 95% CI, 1.19‐3.48), presence or history of cancer or heart failure (OR, 3.48 and 2.86; 95% CI, 1.49‐8.13 and 1.12‐7.27), depression (OR, 2.86; 95% CI, 1.97‐4.17), body mass index (OR, 5.62; 95% CI, 3.62‐8.71), being dependent in activity of daily living (OR, 3.81; 95% CI, 2.61‐5.57), a lower score in instrumental activities of daily living (OR, 3.01; 95% CI, 2.09‐4.33), recent fall(s) (OR, 2.22; 95% CI, 1.37‐2.91), cognitive impairment (OR, 1.81; 95% CI, 1.30‐2.53), insomnia (OR, 1.49; 95% CI, 1.07‐2.06), hemoglobin and albumin level (OR, 1.72 and 2.86; 95% CI, 1.17‐2.50 and 1.53‐5.36) were independent correlates of malnutrition in older patients. Conclusion Our study demonstrated that age, alcohol consumption, chronic diseases (cancer and heart failure), depression, body mass index, function status, recent fall(s), cognitive impairment, insomnia, and low hemoglobin and albumin levels were independently associated with malnutrition in these patients. Comprehensive geriatric assessment can provide detailed information of older patients and can be a useful tool for assessing malnutrition risk‐associated factors.

Objective Insomnia is a common problem in older persons and is associated with poor prognosis from a functional or clinical perspective. The purpose of this study was to investigate the prevalence of insomnia and identify comprehensive geriatric assessment (CGA) based clinical factors associated with insomnia in elderly hospitalized patients. Methods Standardized face‐to‐face interviews were conducted and CGA data were collected from 356 Chinese hospitalized patients aged 60 years or older. Insomnia was defined as self‐reported sleep poor quality according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM‐Ⅴ). Multivariate logistic regression analysis was applied to assess the association between patient clinical factors together with domains within the CGA and insomnia. Results Among the 365 patients, insomnia was found in 48.31% of the participants. Difficulty in initiating sleep (DIS), early morning awakening (EMA), difficulty in maintaining sleep (DMS), and snoring were found in 33.99%, 9.55%, 13.48%, and 1.69% of patients, respectively. Significant associations were found between insomnia and several covariates: female gender (P = 0.034), depression (P = 0.001), activities of daily living (ADL) (P = 0.034), instrumental activities of daily living (IADL; P = 0.009), falling (P = 0.003), chronic pain (P = 0.001), and poor nutritional status (P = 0.038). According to the results of the adjustment multivariate logistic regression analysis, female sex (odds ratio [OR] = 2.057, confidence interval [CI] = 1.179‐3.588, P = 0.011), depression (OR = 1.889, CI = 1.080‐3.304, P = 0.026), and chronic pain (OR = 1.779, CI = 1.103‐2.868, P = 0.018) were significant independently predictors associated with insomnia. Conclusions Our study revealed that female sex, depression, and chronic pain were independently predictors of insomnia in hospitalized patients. Early identification of elderly patients with these risk factors using the CGA may improve the quality of life and treatment outcomes.

Introduction Early diagnosis and potential therapeutic targets of sepsis-induced cardiomyopathy (SIC) remain challenges clinically. Circulating extracellular vesicles from immune cells carrying crucial injurious mediators, including miRNAs in sepsis. However, the impacts of neutrophil-derived extracellular vesicles and their miRNAs in the SIC development are unknown. Objectives The present study focused on the in-depth miRNA expression profiles of neutrophil-derived extracellular vesicles and explored the potential molecular biomarkers during the process of SIC. Methods Neutrophil-derived extracellular vesicles were isolated from the blood samples in three sepsis patients with or without cardiomyopathy on day 1 and day 3 after ICU admission in comparison with three healthy controls. miRNAs were determined by RNA sequencing. The closely related differentially expressed miRNAs with SIC were further validated through qRT-PCR in the other cohorts of sepsis patients with (30 patients) or without cardiomyopathy (20 patients) and the association between miRNAs and the occurrence or disease severity of septic cardiomyopathy were stratified with logistic regression analysis. Results Sixty-eight miRNAs from neutrophil-derived extracellular vesicles were changed significantly between healthy controls and without septic cardiomyopathy patients (61 miRNAs upregulated and seven downregulated). Thirty-eight miRNAs were differentially expressed in the septic cardiomyopathy patients. 27 common differentially expressed miRNAs were found in both groups with similar kinetics (23 miRNAs upregulated and four downregulated). The enriched cellular signaling pathway mediated by miRNAs from sepsis to septic cardiomyopathy was the HIF-1 signaling system modulated septic inflammation. Using multivariate logistic regression analysis, miR-150-5p coupled with NT-pro BNP, LVEF, and SOFA score (AUC = 0.941) were found to be the independent predictors of septic cardiomyopathy. Conclusion miRNAs derived from neutrophil-derived extracellular vesicles play an important role in septic disease severity development towards cardiomyopathy. miR-150-5p may be a predictor of sepsis severity development but warrants further study.