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Animal models are commonly used to investigate developmental processes and disease risk, but humans and model systems (e.g., mice) differ substantially in the pace of development and aging.

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Artificial intelligence (AI) and its subset, machine learning, have tremendous potential to transform health care, medicine, and population health through improved diagnoses, treatments, and patient care. However, the effectiveness of these technologies hinges on the quality and diversity of the data used to train them. Many datasets currently used in machine learning are inherently biased and lack diversity, leading to inaccurate predictions that may perpetuate existing health disparities.

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Epstein-Barr virus (EBV) infection is closely associated with the development of various tumors such as lymphomas and epithelial cancers. EBV has a discrete life cycle with latency and lytic phases. In recent years, significant progress has been made in the understanding of the mechanism underlying the transition of EBV from latency to lytic replication.

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Introduction Launched in 2018, by the Government of India, the Health and Wellness Centers (HWCs) initiative under the Ayushman Bharat program aims to transform India's primary healthcare system. This study evaluates the functioning of Health and Wellness Centres (HWCs) in random blocks of Patna, Bihar, focusing on service availability, infrastructure, manpower challenges, and overall effectiveness in meeting healthcare needs. Methodology The assessment aimed to understand the current state of functioning of 16 HWCs and identify areas for improvement through a Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis.

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