Multiple algorithms highlight key brain genes driven by multiple anesthetics.

Anesthesia serves as a pivotal tool in modern medicine, creating a transient state of sensory deprivation to ensure a pain-free surgical or medical intervention. While proficient in alleviating pain, anesthesia significantly modulates brain dynamics, metabolic processes, and neural signaling, thereby impairing typical cognitive functions. Furthermore, anesthesia can induce notable impacts such as memory impairment, decreased cognitive function, and diminished intelligence, emphasizing the imperative need to explore the concealed repercussions of anesthesia on individuals. In this investigation, we aggregated gene expression profiles (GSE64617, GSE141242, GSE161322, GSE175894, and GSE178995) from public repositories following second-generation sequencing analysis of various anesthetics. Through scrutinizing post-anesthesia brain tissue gene expression utilizing Gene Set Enrichment Analysis (GSEA), Robust Rank Aggregation (RRA), and Weighted Gene Co-expression Network Analysis (WGCNA), this research aims to pinpoint pivotal genes, pathways, and regulatory networks linked to anesthesia. This undertaking not only enhances comprehension of the physiological changes brought about by anesthesia but also lays the groundwork for future investigations, cultivating new insights and innovative perspectives in medical practice.

Identification of hub genes in calcific aortic valve disease.

Calcific aortic valve disease (CAVD) is a heart valve disorder characterized primarily by calcification of the aortic valve, resulting in stiffness and dysfunction of the valve. CAVD is prevalent among aging populations and is linked to factors such as hypertension, dyslipidemia, tobacco use, and genetic predisposition, and can result in becoming a growing economic and health burden. Once aortic valve calcification occurs, it will inevitably progress to aortic stenosis. At present, there are no medications available that have demonstrated effectiveness in managing or delaying the progression of the disease. In this study, we mined four publicly available microarray datasets (GSE12644 GSE51472, GSE77287, GSE233819) associated with CAVD from the GEO database with the aim of identifying hub genes associated with the occurrence of CAVD and searching for possible biological targets for the early prevention and diagnosis of CAVD. This study provides preliminary evidence for therapeutic and preventive targets for CAVD and may provide a solid foundation for subsequent biological studies.

Nutritional indices at admission are associated with mortality rates of patients in the intensive care unit.

BACKGROUND: Malnutrition is a common occurrence in critically ill patients, and has been related to poor prognosis in various diseases. Here, we assess the prognostic value of malnutrition using nutritional indices in intensive care units (ICU) patients. METHODS: We retrieved information on 2060 patients from the Medical Information Mart for Intensive Care III, and randomized the patients into training and validation cohorts, at a ratio of 7:3. We estimated their nutritional indices using prognostic nutritional index (PNI), geriatric nutritional risk index (GNRI), and controlling nutritional status (CONUT) score. Both multivariate regression analysis and the Kaplan-Meier (KM) survival curve were used to examine the prognostic role of nutritional indices in ICU mortality. Then we evaluated the additional predictive significance of each nutritional index beyond the baseline model including conventional risk factors. RESULTS: Multivariate regression analysis revealed that PNI, GNRI, and CONUT were independent predictors of in-hospital and 1-year mortality (all P < 0.001). KM curves showed higher 1-year mortality rates in having nutritional risk patients (PNI ≤ 38 or GNRI ≤ 98 or CONUT ≥ 2). Moreover, subgroup analyses revealed a significant association between each nutritional index and 1-year mortality in patients with different comorbidities. We also observed a pronounced additional impact on the predictive value of 1-year mortality when PNI, GNRI, and CONUT were separately added to the baseline model. The additional role of each nutritional index was further verified in the validation cohort. CONCLUSIONS: Our results revealed that the nutritional indices at admission are significantly correlated with increased mortality rates in ICU adult patients.