In this review, we examine the current landscape of point-of-care testing (POCT) diagnostic tools designed for poverty-related infectious diseases (PRIDs) in sub-Saharan Africa (sSA) while delineating key avenues for future advancements. Our analysis encompasses both established and emerging diagnostic methods for PRIDs, addressing the persistent challenges in POCT tool development and deployment, such as cost, accessibility, and reliability. We emphasize recent advancements in POCT diagnostic tools as well as platforms poised to enhance diagnostic testing in sSA. Recognizing the urgency for affordable and widely accessible POCT diagnostic tools to detect PRIDs in sSA, we advocate for a multidisciplinary approach. This approach integrates current and emerging diagnostic methods, explicitly addressing challenges hindering point-of-care (POC) tool development. Furthermore, it recognizes the profound impact of misdiagnosis on public and global health, emphasizing the need for effective tools. To facilitate the successful development and implementation of POCT diagnostic tools in sSA, we propose strategies including the creation of multi-analyte detection POCT tools, the implementation of education and training programs, community engagement initiatives, fostering public-private collaborations, and the establishment of reliable supply chains. Through these concerted efforts, we aim to accelerate the development of POCT in the sSA region, ensuring its effectiveness and accessibility in addressing the diagnostic challenges associated with PRIDs.
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http://dx.doi.org/10.7717/peerj.17198 | DOI Listing |
Interact J Med Res
January 2025
Medical Directorate, Lausanne University Hospital, Lausanne, Switzerland.
Large language models (LLMs) are artificial intelligence tools that have the prospect of profoundly changing how we practice all aspects of medicine. Considering the incredible potential of LLMs in medicine and the interest of many health care stakeholders for implementation into routine practice, it is therefore essential that clinicians be aware of the basic risks associated with the use of these models. Namely, a significant risk associated with the use of LLMs is their potential to create hallucinations.
View Article and Find Full Text PDFAnn Rheum Dis
January 2025
Department of Surgery, University of Cambridge, Cambridge, UK.
Objectives: To facilitate the stratification of patients with osteoarthritis (OA) for new treatment development and clinical trial recruitment, we created an automated machine learning (autoML) tool predicting the rapid progression of knee OA over a 2-year period.
Methods: We developed autoML models integrating clinical, biochemical, X-ray and MRI data. Using two data sets within the OA Initiative-the Foundation for the National Institutes of Health OA Biomarker Consortium for training and hold-out validation, and the Pivotal Osteoarthritis Initiative MRI Analyses study for external validation-we employed two distinct definitions of clinical outcomes: Multiclass (categorising OA progression into pain and/or radiographic) and binary.
ACS Biomater Sci Eng
January 2025
Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, P. R. China.
Bacterial biofilms, complex microbial communities encased in a protective extracellular matrix, pose a significant threat to public health due to their inherent antibiotic resistance. This review explores the potential of peptides, particularly antimicrobial peptides (AMPs), as innovative tools to combat biofilm-related infections. AMPs, characterized by their potent antimicrobial activity and tissue permeability, offer a promising approach to overcome the challenges posed by biofilms.
View Article and Find Full Text PDFPlant Dis
January 2025
Universidad de Chile, Departamento de Sanidad Vegetal, Facultad de Ciencias Agronomicas, Casilla 1004, Santiago, Chile, 8820000;
Walnut (Juglans regia L.) is the primary nut tree cultivated in Chile, covering 44.626 ha.
View Article and Find Full Text PDFPlast Reconstr Surg
February 2025
From the Departments of Plastic and Reconstructive Surgery.
Background: Spring-assisted surgery (SAS) and cranial vault remodeling (CVR) are widely used surgical techniques to correct sagittal craniosynostosis (SC). The authors evaluated changes in regional morphology of patients with SC who had undergone SAS or CVR, using the frontal bossing index (FBI), occipital bulleting index, vertex narrowing index (VNI), and scaphocephalic severity index (SCI) to capture differences in anterior protrusion, posterior protrusion, width restriction, and global dysmorphology, respectively.
Methods: Indices were measured on computed tomography and 3-dimensional photographs (n = 788) of 257 patients with SC from 2001 through 2022 who underwent SAS (n = 177) or CVR (n = 80).
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