In Pseudomonas donghuensis SVBP6, isolated from an agricultural field, the well-conserved Gac-Rsm pathway upregulates biosynthesis of the antifungal compound 7-hydroxytropolone (7-HT). However, 7-HT does not fully explain the strain's Gac-Rsm-dependent antimicrobial activity. Here, we combined comparative transcriptomic, proteomic, and metabolomic approaches to identify novel GacS-dependent biosynthetic gene clusters (BGC) and/or extracellular specialized metabolites.
View Article and Find Full Text PDFBioimpedance analysis (BIA) is a validated non-invasive technique already proven to be useful for the diagnosis, prognosis, and management of body fluids in subjects with heart failure (HF) and chronic kidney disease (CKD). Although BIA has been widely employed for research purposes, its clinical application is still not fully widespread. The aim of this review is to provide a comprehensive overview of the state of the art of BIA utilization by analyzing the clinical benefits, limitations, and potential future developments in this clinically unexplored field.
View Article and Find Full Text PDFClin Res Cardiol
November 2024
Background: Hospital re-admissions in heart failure (HF) patients are mostly caused by an acute exacerbation of their chronic congestion. Bioimpedance analysis (BIA) has emerged as a promising non-invasive method to assess the volume status in HF. However, its correlation with clinically assessed volume status and its prognostic value in the acute intra-hospital setting remains uncertain.
View Article and Find Full Text PDFPatient adherence to drug treatment is crucial to the success of any prescribed therapy, especially in chronic conditions. The present phenomenological qualitative study aims to explore the elderly experience in managing their medication therapy and their perception of medication adherence. Based on Husserl's perspective, a qualitative descriptive study was conducted utilizing the phenomenological approach, specifically Interpretative Phenomenological Analysis (IPA).
View Article and Find Full Text PDFHeart failure (HF) is defined as the inability of the heart to meet body oxygen demand requiring an elevation in left ventricular filling pressures (LVP) to compensate. LVP increase can be assessed in the cardiac catheterization laboratory, but this procedure is invasive and time-consuming to the extent that physicians rather rely on non-invasive diagnostic tools. In this work, we assess the feasibility to develop a novel machine-learning (ML) approach to predict clinically relevant LVP indices.
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