RESUMO
The aim of this study was to compare the outcomes between dual antiplatelet therapy (DAPT) versus intravenous tissue plasminogen activator (IV t-PA) in patients with minor stroke. This meta-analysis follows the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines. Two authors independently conducted online database searches using PubMed, Web of Science, and EMBASE to identify articles published in English language from inception to September 5, 2023. Outcomes assessed in this meta-analysis included all-cause mortality, stroke incidence, and functional outcomes (measured by modified ranking scale (mRS) scores of 0 to 1). A total of three studies fulfilled the eligibility criteria and included in the final analysis. Pooled analysis showed that the risk of all-cause mortality was not significantly different between the t-PA group and DAPT group (relative risk (RR): 1.14, 95% confidence interval (CI): 0.32-4.06). Compared with those treated with DAPT, there was no significant difference in t-PA in terms of the number of patients with a favorable functional outcome (defined as an mRS score of 0-1). The risk of stroke was not significantly different between the t-PA group and DAPT group (RR: 1.11, 95% CI: 0.68 to 1.82). The analysis, based on three studies, revealed no significant differences between t-PA and DAPT regarding all-cause mortality, stroke incidence, and functional outcomes.
RESUMO
Rare genetic disorders (RDs), characterized by their low prevalence and diagnostic complexities, present significant challenges to healthcare systems. This article explores the transformative impact of artificial intelligence (AI) and machine learning (ML) in addressing these challenges. It emphasizes the need for accurate and early diagnosis of RDs, often hindered by genetic and clinical heterogeneity. This article discusses how AI and ML are reshaping healthcare, providing examples of their effectiveness in disease diagnosis, prognosis, image analysis, and drug repurposing. It highlights AI's ability to efficiently analyze extensive datasets and expedite diagnosis, showcasing case studies like Face2Gene. Furthermore, the article explores how AI tailors treatment plans for RDs, leveraging ML and deep learning (DL) to create personalized therapeutic regimens. It emphasizes AI's role in drug discovery, including the identification of potential candidates for rare disease treatments. Challenges and limitations related to AI in healthcare, including ethical, legal, technical, and human aspects, are addressed. This article underscores the importance of data ethics, privacy, and algorithmic fairness, as well as the need for standardized evaluation techniques and transparency in AI research. It highlights second-generation AI systems that prioritize patient-centric care, efficient patient recruitment for clinical trials, and the significance of high-quality data. The integration of AI with telemedicine, the growth of health databases, and the potential for personalized therapeutic recommendations are identified as promising directions for the field. In summary, this article provides a comprehensive exploration of how AI and ML are revolutionizing the diagnosis and treatment of RDs, addressing challenges while considering ethical implications in this rapidly evolving healthcare landscape.