Epidemiology, biosafety, and biosecurity of Avian Influenza: Insights from the East Mediterranean region.

The World Organization for Animal Health defines Avian Influenza Virus as a highly infectious disease caused by diverse subtypes that continue to evolve rapidly, impacting poultry species, pet birds, wild birds, non-human mammals, and occasionally humans. The effects of Avian influenza viruses have been recognised as a precursor for serious health concerns among affected birds, poultry, and human populations in the Middle East. Furthermore, low and high pathogenic avian influenza viruses lead to respiratory illness with varying severity, depending on the virus subtype (e.g., H5, H7, H9, etc.). Possible future outbreaks and endemics of newly emerging subtypes are expected to occur, as many studies have reported the emergence of novel mutations and viral subtypes. However, proper surveillance programs and biosecurity applications should be developed, and countries with incapacitated defences against such outbreaks should be encouraged to undergo complete reinstation and reinforcement in their health and research sectors. Public education regarding biosafety and virus prevention is necessary to ensure minimal spread of avian influenza endemic.

Emergence of Artificial Intelligence and Machine Learning Models in Sudden Cardiac Arrest: A Comprehensive Review of Predictive Performance and Clinical Decision Support.

Sudden cardiac death/sudden cardiac arrest (SCD/SCA) is an increasingly prevalent cause of mortality globally, particularly in individuals with preexisting cardiac conditions. The ambiguous premortem warnings and the restricted interventional window related to SCD account for the complexity of the condition. Current reports suggest SCD to be accountable for 20% of all deaths hence accurately predicting SCD risk is an imminent concern. Traditional approaches for predicting SCA, particularly "track-and-trigger" warning systems have demonstrated considerable inadequacies, including low sensitivity, false alarms, decreased diagnostic liability, reliance on clinician involvement, and human errors. Artificial intelligence (AI) and machine learning (ML) models have demonstrated near-perfect accuracy in predicting SCA risk, allowing clinicians to intervene timely. Given the constraints of current diagnostics, exploring the benefits of AI and ML models in enhancing outcomes for SCA/SCD is imperative. This review article aims to investigate the efficacy of AI and ML models in predicting and managing SCD, particularly targeting accuracy in prediction.

Exploring Transthyretin Amyloid Cardiomyopathy: A Comprehensive Review of the Disease and Upcoming Treatments.

Transthyretin amyloid cardiomyopathy (ATTR-CM) is a mutation-based genetic disorder due to the accumulation of unstable transthyretin protein and presents with symptoms of congestive heart failure (CHF) and numerous extracardiac symptoms like carpal tunnel syndrome and neuropathy. Two subtypes of ATTR-CM are hereditary and wild-type, both of which have different risk factors, gender prevalence and major clinical symptoms. Timely usage of imaging modalities like echocardiography, cardiac magnetic imaging resonance, and cardiac scintigraphy has made it possible to suspect ATTR-CM in patients presenting with CHF. Management of ATTR-CM includes appropriate treatment for heart failure for symptomatic relief, prevention of arrhythmias and heart transplantation for nonresponders. With the recent approval of tafamidis in the successful management of ATTR-CM, numerous potential therapeutic points have been identified to stop or delay the progression of ATTR-CM. This article aims to provide a comprehensive review of ATTR-CM and insights into its novel therapeutics and upcoming treatments.