RESUMO
Large-vessel occlusion (LVO) stroke is a promising field for the use of AI, especially machine learning (ML) because optimal results are highly dependent on timely diagnosis, communication, and treatment. In order to better understand the current state of artificial intelligence (AI) in relation to LVO strokes, its efficacy, and potential future applications, we searched relevant literature to perform a comprehensive evaluation of the topic. The databases PubMed, Embase, and Scopus were extensively searched for this review. Studies were then screened using title and abstract criteria and duplicate studies were excluded. By using pre-established inclusion and exclusion criteria, it was decided whether or not to include full-text papers in the final analysis. The studies were analyzed, and the relevant information was retrieved. In recognizing LVO on computed tomography, ML approaches were very accurate. There is a shortage of AI applications for thrombectomy patient selection, despite the fact that certain research accurately evaluates individual patient eligibility for endovascular therapy. Machine learning algorithms may reasonably predict clinical and angiographic outcomes as well as associated factors. AI has shown promise in the diagnosis and treatment of people who have just suffered a stroke. However, the usefulness of AI in management and forecasting remains restricted, necessitating more studies into machine learning applications that can guide decision making in the future.
RESUMO
This study reports on the possible effects of OH radical impact on the transmembrane domain 6 of P-glycoprotein, TM6, which plays a crucial role in drug binding in human cells. For the first time, we employ molecular dynamics (MD) simulations based on the self-consistent charge density functional tight binding (SCC-DFTB) method to elucidate the potential sites of fragmentation and mutation in this domain upon impact of OH radicals, and to obtain fundamental information about the underlying reaction mechanisms. Furthermore, we apply non-reactive MD simulations to investigate the long-term effect of this mutation, with possible implications for drug binding. Our simulations indicate that the interaction of OH radicals with TM6 might lead to the breaking of C-C and C-N peptide bonds, which eventually cause fragmentation of TM6. Moreover, according to our simulations, the OH radicals can yield mutation in the aromatic ring of phenylalanine in TM6, which in turn affects its structure. As TM6 plays an important role in the binding of a range of cytotoxic drugs with P-glycoprotein, any changes in its structure are likely to affect the response of the tumor cell in chemotherapy. This is crucial for cancer therapies based on reactive oxygen species, such as plasma treatment.
