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Equid alphaherpesvirus 4 (EqAHV4; , ; equine rhinopneumonitis virus) has seldom been associated with complications such as abortion and myeloencephalopathy, given the low tendency of this virus to induce viremia. We investigated the frequency of EqAHV4 viremia in horses with fever and respiratory signs. Case selection included all equids with EqAHV4 quantitative real-time PCR (qPCR)-positive nasal secretions (defined as EqAHV4 qPCR-positive cases) submitted to a diagnostic laboratory.

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Introduction: Human mobility is associated with an increased risk of HIV acquisition and disengagement from HIV care, leading to poorer health outcomes among highly mobile individuals compared to less mobile individuals. Mobile individuals, broadly defined as those who temporally, seasonally, or permanently move from one place to another for voluntary or involuntary reasons, face many challenges in accessing HIV care services. These challenges include logistical difficulties, interruptions in HIV care continuity, and limited access to services across different locations, which together hinder timely testing, treatment initiation, and viral suppression.

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Background: A significant gap exists in understanding the effectiveness of intra-class (same-class) level peer mentorship programmes designed to enhance academic performance, well-being, and student involvement among underperforming medical students. This study assessed the effectiveness of intra-class (same-class) peer mentorship programme on the academic performances, subjective well-being and school engagement of academically underperforming medical students in Nigeria.

Methods: This was a quasi-experimental research consisting of the pretest-posttest control design at Nnamdi Azikiwe University, Awka, Nigeria.

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Background: Assessing the difficulty of impacted lower third molar (ILTM) surgical extraction is crucial for predicting postoperative complications and estimating procedure duration. The aim of this study was to evaluate the effectiveness of a convolutional neural network (CNN) in determining the angulation, position, classification and difficulty index (DI) of ILTM. Additionally, we compared these parameters and the time required for interpretation among deep learning (DL) models, sixth-year dental students (DSs), and general dental practitioners (GPs) with and without CNN assistance.

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Background: Drivers of COVID-19 severity are multifactorial and include multidimensional and potentially interacting factors encompassing viral determinants and host-related factors (i.e., demographics, pre-existing conditions and/or genetics), thus complicating the prediction of clinical outcomes for different severe acute respiratory syndrome coronavirus (SARS-CoV-2) variants.

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