The meaning of 'mapping' in relation to onchocerciasis has changed at least three times over the past 50 years as the programmatic goals and the assessment tools have changed. With the current goal being global elimination of Onchocerca volvulus (OV), all areas where OV might currently be transmitted and where mass drug administration (MDA) with ivermectin treatment has not been delivered previously must now be identified by careful, detailed 'elimination mapping' as either OV endemic or not, so that appropriate programmatic targets can be established. New tools and strategies for such elimination mapping have become available, though ongoing studies must still be completed to define agreed upon optimal diagnostic evaluation units, sampling strategies and serologic tools. With detailed guidance and technical support from the World Health Organization and with implementation and financial support from their global partners, the OV-endemic countries of Africa can soon complete their elimination mapping and then continue with MDA programmes to progressively achieve the same success in OV elimination as that already achieved by the growing list of formerly OV-endemic countries in the Americas.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5881272 | PMC |
http://dx.doi.org/10.1093/inthealth/ihx052 | DOI Listing |
J Mol Neurosci
January 2025
Lanzhou University Second Hospital, The Second Medical College of Lanzhou University, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China.
Ischemic stroke leads to permanent damage to the affected brain tissue, with strict time constraints for effective treatment. Predictive biomarkers demonstrate great potential in the clinical diagnosis of ischemic stroke, significantly enhancing the accuracy of early identification, thereby enabling clinicians to intervene promptly and reduce patient disability and mortality rates. Furthermore, the application of predictive biomarkers facilitates the development of personalized treatment plans tailored to the specific conditions of individual patients, optimizing treatment outcomes and improving prognoses.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221000, China.
In recent years, mobile laser measurement systems have markedly enhanced the capabilities of deformation detection and defect identification within metro tunnels, attributed to their superior efficiency, precision, and versatility. Nevertheless, challenges persist, including substantial equipment costs, inadequate after-sales support, technological barriers, and limitations in customization. This paper develops a mobile laser measurement system that has been specifically developed for the purpose of detecting deformation in metro tunnels.
View Article and Find Full Text PDFMicromachines (Basel)
January 2025
Research Center for Novel Computing Sensing and Intelligent Processing, Zhejiang Lab, Hangzhou 311100, China.
General matrix multiplication (GEMM) in machine learning involves massive computation and data movement, which restricts its deployment on resource-constrained devices. Although data reuse can reduce data movement during GEMM processing, current approaches fail to fully exploit its potential. This work introduces a sparse GEMM accelerator with a weight-and-output stationary (WOS) dataflow and a distributed buffer architecture.
View Article and Find Full Text PDFGenes (Basel)
January 2025
Department of Animal, Veterinary and Food Science, University of Idaho, Moscow, ID 83844, USA.
Background: Lamb health is crucial for producers; however, the percentage of lambs that die before weaning is still 15-20%. One factor that can contribute to lamb deaths is congenital diseases. A novel semi-lethal disease has been identified in newborn Polypay lambs and termed dozer lamb syndrome.
View Article and Find Full Text PDFBioengineering (Basel)
December 2024
Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA.
Non-linear least squares (NLS) methods are commonly used for quantitative magnetic resonance imaging (MRI), especially for multi-exponential T1ρ mapping, which provides precise parameter estimation for different relaxation models in tissues, such as mono-exponential (ME), bi-exponential (BE), and stretched-exponential (SE) models. However, NLS may suffer from problems like sensitivity to initial guesses, slow convergence speed, and high computational cost. While deep learning (DL)-based T1ρ fitting methods offer faster alternatives, they often face challenges such as noise sensitivity and reliance on NLS-generated reference data for training.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!