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.

Objectives: This study aimed to present the Asia-Pacific consensus on long-term and sequential therapy for osteoporosis, offering evidence-based recommendations for the effective management of this chronic condition.The primary focus is on achieving optimal fracture prevention through a comprehensive, individualized approach. Methods: A panel of experts convened to develop consensus statements by synthesizing the current literature and leveraging clinical expertise. The review encompassed long-term anti-osteoporosis medication goals, first-line treatments for individuals at very high fracture risk, and the strategic integration of anabolic and anti resorptive agents in sequential therapy approaches. Results: The panelists reached a consensus on 12 statements. Key recommendations included advocating for anabolic agents as the first-line treatment for individuals at very high fracture risk and transitioning to anti resorptive agents following the completion of anabolic therapy. Anabolic therapy remains an option for in dividuals experiencing new fractures or persistent high fracture risk despite antiresorptive treatment. In cases of inadequate response, the consensus recommended considering a switch to more potent medications. The consensus also addressed the management of medication-related complications, proposing alternatives instead of discontinuation of treatment. Conclusions This consensus provides a comprehensive, cost-effective strategy for fracture prevention with an emphasis on shared decision-making and the incorporation of country-specific case management systems, such as fracture liaison services. It serves as a valuable guide for healthcare professionals in the Asia-Pacific region, contributing to the ongoing evolution of osteoporosis management.

Background: To compare risk of diabetic retinopathy (DR) between patients taking sodium-glucose cotransporter-2 inhibitors (SGLT2is) and those taking glucagon-like peptide-1 receptor agonists (GLP1-RAs) in routine care. Methods: This retrospective cohort study emulating a target trial included patient data from the multi-institutional Chang Gung Research Database in Taiwan. Totally, 33,021 patients with type 2 diabetes mellitus using SGLT2is and GLP1-RAs between 2016 and 2019 were identified. 3,249 patients were excluded due to missing demographics, age <40 years, prior use of any study drug, a diagnosis of retinal disorders, a history of receiving vitreoretinal procedure, no baseline glycosylated hemoglobin, or no follow-up data. Baseline characteristics were balanced using inverse probability of treatment weighting with propensity scores. DR diagnoses and vitreoretinal interventions served as the primary outcomes. Occurrence of proliferative DR and DR receiving vitreoretinal interventions were regarded as vision-threatening DR. Results: There were 21,491 SGLT2i and 1,887 GLP1-RA users included for the analysis. Patients receiving SGLT2is and GLP-1 RAs exhibited comparable rate of any DR (subdistribution hazard ratio [SHR], 0.90; 95% confidence interval [CI], 0.79 to 1.03), whereas the rate of proliferative DR (SHR, 0.53; 95% CI, 0.42 to 0.68) was significantly lower in the SGLT2i group. Also, SGLT2i users showed significantly reduced risk of composite surgical outcome (SHR, 0.58; 95% CI, 0.48 to 0.70). Conclusion Compared to those taking GLP1-RAs, patients receiving SGLT2is had a lower risk of proliferative DR and vitreoretinal interventions, although the rate of any DR was comparable between the SGLT2i and GLP1-RA groups. Thus, SGLT2is may be associated with a lower risk of vision-threatening DR but not DR development.

Cytomegalovirus Infections/diagnosis* ; Humans ; Infant, Newborn ; Mass Screening ; Research ; Singapore

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.

The novel coronavirus infection, COVID-19, is a pandemic that currently affects the whole world. During this period, Malaysians displayed a variety of behaviour changes as a response to COVID-19, including panic buying, mass travelling during movement restriction and even absconding from treatment facilities. This article attempts to explore some of these behaviour changes from a behaviourist perspective in order to get a better understanding of the rationale behind the changes.

OBJECTIVE: Considering the different prevalence rates of diseases such as asthma and chronic obstructive pulmonary disease in Asians relative to other races, Koreans may have unique airway structure and lung function. This study aimed to investigate unique features of airway structure and lung function based on quantitative computed tomography (QCT)-imaging metrics in the Korean Asian population (Koreans) as compared with the White American population (Whites). MATERIALS AND METHODS: QCT data of healthy non-smokers (223 Koreans vs. 70 Whites) were collected, including QCT structural variables of wall thickness (WT) and hydraulic diameter (Dh) and functional variables of air volume, total air volume change in the lung (ΔVair), percent emphysema-like lung (Emph%), and percent functional small airway disease-like lung (fSAD%). Mann-Whitney U tests were performed to compare the two groups. RESULTS: As compared with Whites, Koreans had smaller volume at inspiration, ΔVair between inspiration and expiration (p < 0.001), and Emph% at inspiration (p < 0.001). Especially, Korean females had a decrease of ΔVair in the lower lobes (p < 0.001), associated with fSAD% at the lower lobes (p < 0.05). In addition, Koreans had smaller Dh and WT of the trachea (both, p < 0.05), correlated with the forced expiratory volume in 1 second (R = 0.49, 0.39; all p < 0.001) and forced vital capacity (R = 0.55, 0.45; all p < 0.001). CONCLUSION: Koreans had unique features of airway structure and lung function as compared with Whites, and the difference was clearer in female individuals. Discriminating structural and functional features between Koreans and Whites enables exploration of inter-racial differences of pulmonary disease in terms of severity, distribution, and phenotype.
Asian Continental Ancestry Group ; Asthma ; Continental Population Groups ; Female ; Forced Expiratory Volume ; Humans ; Lung ; Lung Diseases ; Phenotype ; Prevalence ; Pulmonary Disease, Chronic Obstructive ; Thorax ; Trachea ; Vital Capacity

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.
Artificial Intelligence ; Biomarkers ; Depression ; DNA Methylation ; Epigenomics ; Gene-Environment Interaction ; Histone Code ; Humans ; Machine Learning ; MicroRNAs ; Models, Animal ; Risk Factors