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Newborn signal functions in Bangladesh: Identification through expert consultation and assessment of readiness among public health facilities.
J Glob Health
September 2022
Maternal and Child Health Division, International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh.
Background: This study aimed to identify a set of newborn signal functions (NSFs) that can categorize health facilities and assist policymakers and health managers in appropriately planning and adequately monitoring the progress and performance of health facilities delivering newborn health care in Bangladesh and similar low-income settings.
Methods: A modified Delphi method was used to identify a set of NSFs and a cross-sectional health facility assessment among the randomly selected facilities was conducted to test them in public health facilities in Bangladesh. In the modified Delphi approach, three main steps of listing, prioritizing, and testing were followed to identify the set of NSFs.
Scoping Review of Current Costing Literature on Interventions to Reach Zero-Dose Children in Low- and Middle-Income Countries.
Vaccines (Basel)
December 2024
JSI Research & Training Institute, Inc., Arlington, VA 22202, USA.
A limited number of studies focus on estimating the costs of interventions to increase childhood immunization coverage in low- and middle-income countries (LMICs). Existing reviews often compare estimated costs but lack information on the methods used. The objective of this review is to summarize the methods used in costing studies that assessed interventions to reach zero-dose (ZD) children.
View Article and Find Full Text PDFApplication of the Sponge Model Implants in the Study of Vaccine Memory in Mice Previously Immunized with LBSap.
Vaccines (Basel)
November 2024
Laboratory of Biology of Cellular Interactions, Department of Morphology, Federal University of Minas Gerais, Belo Horizonte 31270-901, MG, Brazil.
Background/objectives: Considering the large number of candidates in vaccine-testing studies against different pathogens and the amount of time spent in the preclinical and clinical trials, there is a pressing need to develop an improved in vivo system to quickly screen vaccine candidates. The model of a polyester-polyurethane sponge implant provides a rapid analysis of the specific stimulus-response, allowing the study of a compartmentalized microenvironment. The sponge implant's defined measurements were standardized as a compartment to assess the immune response triggered by the vaccinal antigen.
View Article and Find Full Text PDFApplication of Machine Learning to Predict CO Emissions in Light-Duty Vehicles.
Sensors (Basel)
December 2024
Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK.
Climate change caused by greenhouse gas (GHG) emissions is an escalating global issue, with the transportation sector being a significant contributor, accounting for approximately a quarter of all energy-related GHG emissions. In the transportation sector, vehicle emissions testing is a key part of ensuring compliance with environmental regulations. The Vehicle Certification Agency (VCA) of the UK plays a pivotal role in certifying vehicles for compliance with emissions and safety standards.
View Article and Find Full Text PDFMachine Learning Recognizes Stages of Parkinson's Disease Using Magnetic Resonance Imaging.
Sensors (Basel)
December 2024
Faculty of Computer Science, Polish-Japanese Academy of Information Technology, 86 Koszykowa Street, 02-008 Warsaw, Poland.
Neurodegenerative diseases (NDs), such as Alzheimer's disease (AD) and Parkinson's disease (PD), are debilitating conditions that affect millions worldwide, and the number of cases is expected to rise significantly in the coming years. Because early detection is crucial for effective intervention strategies, this study investigates whether the structural analysis of selected brain regions, including volumes and their spatial relationships obtained from regular T1-weighted MRI scans ( = 168, PPMI database), can model stages of PD using standard machine learning (ML) techniques. Thus, diverse ML models, including Logistic Regression, Random Forest, Support Vector Classifier, and Rough Sets, were trained and evaluated.
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