Publications by authors named "E C Sabino"
Background: Chagas disease (CD) is neglected that affects vulnerable individuals, whose majority has low ability to understand health information.
Objectives: To assess health literacy and its association with sociodemographic, clinical, and quality of life (QoL) characteristics.
Design And Setting: A cross-sectional study the participants with Chagas disease (ChD) were identified through serological diagnosis during blood donation, while those without the disease were seronegative blood donors.
Impact of Exogenous on the Gut Microbiome of Hematopoietic Stem Cell Transplantation Patients Colonized by Multidrug-Resistant Bacteria: An Observational Study.
Antibiotics (Basel)
October 2024
can inhibit the growth of multidrug-resistant organisms (MDROs) and modulate the gut microbiome. However, data on hematopoietic stem cell transplantation (HSCT) are scarce. In an observational study, we assessed the impact of on the modulation of the gut microbiome in HSCT patients colonized by MDROs.
View Article and Find Full Text PDFBackground: HIV/AIDS remains a highly stigmatizing disease worldwide, preventing people with risk or infection from testing to learn their HIV status, accessing supportive services, or taking antiretroviral therapy. Despite many studies of HIV in blood donors, no studies have evaluated the factors that contribute to stigma surrounding this illness following notification process and counseling provided by blood centers.
Methods: A cross-sectional questionnaire-based survey was conducted between 2016 and 2017.
Prognostic Significance and Associations of Neural Network-Derived Electrocardiographic Features.
Circ Cardiovasc Qual Outcomes
December 2024
Article Synopsis
- The study explores the use of advanced neural network-derived ECG features to predict cardiovascular disease and mortality, aiming to uncover subtle, important indicators that traditional methods might miss.
- Using data from over 1.8 million patients and various international cohorts, researchers identified three distinct phenogroups, with one, phenogroup B, showing a significantly higher mortality risk—20% more than phenogroup A.
- The findings suggest that neural network ECG features not only indicate future health risks like atrial fibrillation and ischemic heart disease but also highlight specific genetic loci that may contribute to these risks.
Article Synopsis
- - The AI-ECG risk estimator (AIRE) platform was developed to improve predictions of future disease and mortality risks from electrocardiograms (ECGs), addressing limitations in existing models related to individual actionability and biological plausibility.
- - AIRE utilizes deep learning and survival analysis on a massive dataset of over 1.16 million ECGs to predict patient-specific mortality risks and timelines, validated across diverse international cohorts.
- - The platform demonstrated high accuracy for predicting various health risks, such as all-cause mortality and heart failure, and identified biological pathways linked to cardiac health, making it a promising tool for clinical use globally.