Optimized Hybrid Deep Learning Framework for Early Detection of Alzheimer's Disease Using Adaptive Weight Selection.

Background: Alzheimer's disease (AD) is a progressive neurological disorder that significantly affects middle-aged and elderly adults, leading to cognitive deterioration and hindering daily activities. Notwithstanding progress, conventional diagnostic techniques continue to be susceptible to inaccuracies and inefficiencies. Timely and precise diagnosis is essential for early intervention.

Enhanced brain tumor diagnosis using combined deep learning models and weight selection technique.

Brain tumor classification is a critical task in medical imaging, as accurate diagnosis directly influences treatment planning and patient outcomes. Traditional methods often fall short in achieving the required precision due to the complex and heterogeneous nature of brain tumors. In this study, we propose an innovative approach to brain tumor multi-classification by leveraging an ensemble learning method that combines advanced deep learning models with an optimal weighting strategy.

Exploring the use of COVID-19 antigen rapid diagnostic tests among displaced populations in Iraq: findings from a pilot project in four IDP camps.

Background: IOM piloted the use of Ag RDTs for COVID-19 in Iraq, in collaboration with FIND, the Global Alliance for Diagnostics, to facilitate access to testing and understand barriers and opportunities for testing in a displacement context. The purpose was to (i) evaluate the performance metrics of the Sure Status COVID-19 Antigen Card Test in this population; (ii) compare Ag RDT results across high- and low-probability cases, vaccination status, and symptom severity; and (iii) report participant perspectives on Ag RDT use for COVID-19 and other diseases.

Methods: Secondary analysis was conducted using de-identified cross-sectional data collected from November 2022-March 2023 in four IDP camps in the Kurdistan region of Iraq.

Development and Validation of a Diagnostic Algorithm for Down Syndrome Using Birth Certificate and International Classification of Diseases Codes.

Objective: We aimed to develop an algorithm that accurately identifies children with Down syndrome (DS) using administrative data.

Methods: We identified a cohort of children born between 2000 and 2017, enrolled in the Tennessee Medicaid Program (TennCare), who either had DS coded on their birth certificate or had a diagnosis listed using an International Classification of Diseases (ICD) code (suspected DS), and who received care at Vanderbilt University Medical Center, a comprehensive academic medical center, in the United States. Children with suspected DS were defined as having DS if they had (a) karyotype-confirmed DS indicated on their birth certificate; (b) karyotype-pending DS indicated on their birth certificate (or just DS if test type was not specified) and at least two healthcare encounters for DS during the first 6 years of life; or (c) at least three healthcare encounters for DS, with the first and last encounter separated by at least 30 days, during the first six years of life.

BNP and NT-proBNP as prognostic biomarkers for the prediction of adverse outcomes in HFpEF patients: A systematic review and meta-analysis.

Heart failure with preserved ejection fraction (HFpEF) presents a challenge in clinical practice due to its complexity and impact on morbidity and mortality. The aim of this systematic review and meta-analysis (SR/MA) was to evaluate the value of B-Type Natriuretic Peptide (BNP) and NT-proBNP in predicting overall adverse outcomes, cardiovascular events, and mortality, in patients with HFpEF. This SR/MA included observational studies and randomized controlled trials (RCTs) that reported the use of BNP and NT-proBNP as prognostic biomarkers for adverse outcomes in HFpEF patients.