Publications by authors named "P P Huynh"

The convergence of artificial intelligence (AI) and genomics is redefining cancer drug discovery by facilitating the development of personalized and effective therapies. This review examines the transformative role of AI technologies, including deep learning and advanced data analytics, in accelerating key stages of the drug discovery process: target identification, drug design, clinical trial optimization, and drug response prediction. Cutting-edge tools such as DrugnomeAI and PandaOmics have made substantial contributions to therapeutic target identification, while AI's predictive capabilities are driving personalized treatment strategies.

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Introduction: Metabolic disorders (type 2 diabetes, insulin resistance, hyperglycaemia, obesity, hyperlipidaemia, hypertension, non-alcoholic fatty liver disease and metabolic syndrome) are leading causes of mortality and disability worldwide. These disorders disproportionately affect older adults relative to those younger. Digital health technologies (DHTs), such as patient monitoring, digital diagnostics and digital therapeutics, emerge as promising tools for health promotion in day-to-day life.

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: Canada's long-term care (LTC) sector is struggling with a significant staffing crisis related to shortages, high-turnover rate, and challenging working conditions. The COVID-19 pandemic exacerbated these issues and emphasized the need for improved mental health support for LTC staff. Understanding and addressing the wellbeing of staff is important for ensuring quality of care and promoting a positive work environment for a healthy workforce.

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PEG10 is a retroelement-derived Mart-family gene that is necessary for placentation and has been implicated in neurological disease. PEG10 resembles both retrotransposon and retroviral proteins and forms virus-like particles (VLPs) that can be purified using iodixanol ultracentrifugation. It is hypothesized that formation of VLPs is crucial to the biological roles of PEG10 in reproduction and neurological health.

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Objective: To quantify the otolaryngologic diagnoses encountered in outpatient clinics by otolaryngology-head and neck surgery (OHNS) residents during their 5 years of postgraduate training.

Methods: This is a retrospective review at a single institution following 5 consecutive graduating resident cohorts throughout their training. The electronic health record was queried for OHNS clinic encounters from 2013 through 2022 during which the resident physicians were included as the visit provider or assistant to an attending physician.

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