Role of Artificial Intelligence in Identifying Vital Biomarkers with Greater Precision in Emergency Departments During Emerging Pandemics.

The COVID-19 pandemic has accelerated advances in molecular biology and virology, enabling the identification of key biomarkers to differentiate between severe and mild cases. Furthermore, the use of artificial intelligence (AI) and machine learning (ML) to analyze large datasets has been crucial for rapidly identifying relevant biomarkers for disease prognosis, including COVID-19. This approach enhances diagnostics in emergency settings, allowing for more accurate and efficient patient management.

Machine Learning-Based Assessment of Survival and Risk Factors in Non-Alcoholic Fatty Liver Disease-Related Hepatocellular Carcinoma for Optimized Patient Management.

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, with an incidence that is exponentially increasing. Hepatocellular carcinoma (HCC) is the most frequent primary tumor. There is an increasing relationship between these entities due to the potential risk of developing NAFLD-related HCC and the prevalence of NAFLD.

Critical appraisal of pharmacoeconomic studies comparing TNF-alpha antagonists for rheumatoid arthritis treatment.

Clinical trials and economic models have showed that anti-TNF agents are effective and cost effective compared with standard therapies (mainly disease-modifying antirheumatic drugs) in patients with rheumatoid arthritis. However, no head-to-head clinical trials between these agents are available and the relative effectiveness and cost-effectiveness is uncertain. We have conducted a literature review in order to identify full economic evaluations that compared two or more TNF antagonists.