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Background: Health worker migration from Nigeria poses significant challenges to the Nigerian health care sector and has far-reaching implications for health care systems globally. Understanding the factors driving migration, its effects on health care delivery, and potential policy interventions is critical for addressing this complex issue.

Objective: This study aims to comprehensively examine the factors encouraging the emigration of Nigerian health workers, map out the effects of health worker migration on the Nigerian health system, document the loss of investment in health training and education resulting from migration, identify relevant policy initiatives addressing migration, determine the effects of Nigerian health worker migration on destination countries, and identify the benefits and demerits to Nigeria of health worker migration.

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After more than a decade of practice, registered reports (RRs) are widely adopted in psychology. However, the acceptance of RRs in terms of postpublication academic recognition and public dissemination, compared with nonregistered reports (non-RR), remained largely unexplored. This matched meta-evaluation identified and analyzed 119 pairs of original research articles (RR vs.

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Studies have demonstrated that the quality and transparency of reporting Clinical Practice Guidelines (CPGs) in healthcare are low. This meta-research aimed to evaluate the adherence of nutrition CPGs for critically ill adults to the reporting RIGHT checklist and its association with the methodological quality assessed by AGREE II, along with other potential publication-related factors. A systematic search for CPGs until December 2024 was conducted.

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Background And Purpose:  The amount of information publicly available from arthroplasty registries is large but could be used more effectively. This project aims to improve the knowledge concerning existing registries to facilitate access, transparency, harmonization, and reporting.

Methods: Within the International Society of Arthroplasty Registries (ISAR) we aimed at developing, testing, adopting, and making publicly available a short, standardized registry description with items considered relevant for stakeholders using a cross-sectional study survey.

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AI integration into wavelength-based SPR biosensing: Advancements in spectroscopic analysis and detection.

Anal Chim Acta

March 2025

Artificial Intelligence Research Center, Chang Gung University, Taoyuan, 333323, Taiwan; Department of Artificial Intelligence, College of Intelligent Computing, Chang Gung University, Taoyuan, 333323, Taiwan. Electronic address:

Background: In recent years, employing deep learning methods in the biosensing area has significantly reduced data analysis time and enhanced data interpretation and prediction accuracy. In some SPR fields, research teams have further enhanced detection capabilities using deep learning techniques. However, the application of deep learning to spectroscopic surface plasmon resonance (SPR) biosensors has not been reported.

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