Endovascular Therapy in the Extended Time Window for Large Vessel Occlusion in Patients With Pre-Stroke Disability / 대한뇌졸중학회지

Background@#and Purpose We compared the outcomes of endovascular therapy (EVT) in an extended time window in patients with large-vessel occlusion (LVO) between patients with and without pre-stroke disability. @*Methods@#In this prespecified analysis of the multinational CT for Late Endovascular Reperfusion study (66 participating sites, 10 countries between 2014 and 2022), we analyzed data from patients with acute ischemic stroke with a pre-stroke modified Rankin Scale (mRS) score of 0–4 and LVO who underwent EVT 6–24 hours from the time last seen well. The primary outcome was the composite of functional independence (FI; mRS score 0–2) or return to the pre-stroke mRS score (return of Rankin, RoR) at 90 days. Outcomes were compared between patients with pre-stroke disability (pre-stroke mRS score 2–4) and those without (mRS score 0–1). @*Results@#A total of 2,231 patients (median age, 72 years; median National Institutes of Health Stroke Scale score, 16) were included in the present analysis. Of these, 564 (25%) had pre-stroke disability. The primary outcome (FI or RoR) was observed in 30.7% of patients with pre-stroke disability (FI, 16.5%; RoR, 30.7%) compared to 44.1% of patients without (FI, 44.1%; RoR, 13.0%) (P<0.001). In multivariable logistic regression analysis with inverse probability of treatment weighting, pre-stroke disability was not associated with significantly lower odds of achieving FI or RoR (adjusted odds ratio 0.73, 95% confidence interval 0.43–1.25). Symptomatic intracranial hemorrhage occurred in 6.3% of both groups (P=0.995). @*Conclusion@#A considerable proportion of patients with late-presenting LVO and pre-stroke disability regained pre-stroke mRS scores after EVT. EVT may be appropriate for patients with pre-stroke disability presenting in the extended time window.

Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments

A growing body of evidence now proposes that machine learning and deep learning techniques can serve as a vital foundation for the pharmacogenomics of antidepressant treatments in patients with major depressive disorder (MDD). In this review, we focus on the latest developments for pharmacogenomics research using machine learning and deep learning approaches together with neuroimaging and multi-omics data. First, we review relevant pharmacogenomics studies that leverage numerous machine learning and deep learning techniques to determine treatment prediction and potential biomarkers for antidepressant treatments in MDD. In addition, we depict some neuroimaging pharmacogenomics studies that utilize various machine learning approaches to predict antidepressant treatment outcomes in MDD based on the integration of research on pharmacogenomics and neuroimaging. Moreover, we summarize the limitations in regard to the past pharmacogenomics studies of antidepressant treatments in MDD. Finally, we outline a discussion of challenges and directions for future research. In light of latest advancements in neuroimaging and multi-omics, various genomic variants and biomarkers associated with antidepressant treatments in MDD are being identified in pharmacogenomics research by employing machine learning and deep learning algorithms.

Epigenetics and Depression: An Update

OBJECTIVE: Depression is associated with various environmental risk factors such as stress, childhood maltreatment experiences, and stressful life events. Current approaches to assess the pathophysiology of depression, such as epigenetics and gene-environment (GxE) interactions, have been widely leveraged to determine plausible markers, genes, and variants for the risk of developing depression. METHODS: We focus on the most recent developments for genomic research in epigenetics and GxE interactions. RESULTS: In this review, we first survey a variety of association studies regarding depression with consideration of GxE interactions. We then illustrate evidence of epigenetic mechanisms such as DNA methylation, microRNAs, and histone modifications to influence depression in terms of animal models and human studies. Finally, we highlight their limitations and future directions. CONCLUSION: In light of emerging technologies in artificial intelligence and machine learning, future research in epigenetics and GxE interactions promises to achieve novel innovations that may lead to disease prevention and future potential therapeutic treatments for depression.