Probing the Effective Treatment Thresholds for Alteplase in Acute Ischemic Stroke With Regression Discontinuity Designs

Measuring the active elements of cognitive-behavioral therapies

Understanding how and for whom cognitive-behavioral therapies work is central to the development and improvement of mental health interventions. Suboptimal quantification of the active elements of cognitive-behavioral therapies has hampered progress in elucidating mechanisms of change. To advance process research on cognitive-behavioral therapies, we describe a theoretical measurement framework that focuses on the delivery, receipt, and application of the active elements of these interventions. We then provide recommendations for measuring the active elements of cognitive-behavioral therapies aligned with this framework. Finally, to support measurement harmonization and improve study comparability, we propose the development of a publicly available repository of assessment tools: the Active Elements of Cognitive-Behavioral Therapies Measurement Kit.

Big Data in Stroke: How to Use Big Data to Make the Next Management Decision

The last decade has seen significant advances in the accumulation of medical data, the computational techniques to analyze that data, and corresponding improvements in management. Interventions such as thrombolytics and mechanical thrombectomy improve patient outcomes after stroke in selected patients; however, significant gaps remain in our ability to select patients, predict complications, and understand outcomes. Big data and the computational methods needed to analyze it can address these gaps. For example, automated analysis of neuroimaging to estimate the volume of brain tissue that is ischemic and salvageable can help triage patients for acute interventions. Data-intensive computational techniques can perform complex risk calculations that are too cumbersome to be completed by humans, resulting in more accurate and timely prediction of which patients require increased vigilance for adverse events such as treatment complications. To handle the accumulation of complex medical data, a variety of advanced computational techniques referred to as machine learning and artificial intelligence now routinely complement traditional statistical inference. In this narrative review, we explore data-intensive techniques in stroke research, how it has informed the management of stroke patients, and how current work could shape clinical practice in the future.

Astragalus polysaccharide (APS) exerts protective effect against acute ischemic stroke (AIS) through enhancing M2 micoglia polarization by regulating adenosine triphosphate (ATP)/ purinergic receptor (P2X7R) axis

Clinically, the effective treatment for patients with acute ischemic stroke (AIS) is very limited. Therefore, this paper aims to investigate the mechanism how astragalus polysaccharide (APS) exerts protective effect against AIS and provide a new method for the treatment of AIS. Cell surface antigen flow cytometry and immunofluorescence were used to identify M1 and M2 microglia. Western blot was used to evaluate the expression of associated protein. Oxygen-glucose deprivation (OGD) was used to simulate the effect of AIS on rat microglia. The middle cerebral artery occlusion (MCAO) model was established to simulate the effect of AIS in vivo. Evans blue dye (EBD) was used to evaluate the permeability of blood–brain barrier (BBB). Western blot and cell surface antigen flow cytometry results showed that APS promoted the M2 polarization of rat microglia by inhibiting the expression of purinergic receptor (P2X7R). APS reversed the effect of OGD on the polarization of rat microglia M1/ M2 by regulating P2X7R. APS reversed the effect of MCAO on the polarization of rat microglia M1/ M2 in vivo. Furthermore, APS inhibited the expression of P2X7R by promoting the degradation of adenosine triphosphate (ATP) in the cerebral cortex of MCAO rats. In addition, APS contributed to maintain the integrity of BBB. Summarily, APS can reduce brain injury by promoting the degradation of ATP in microglia and inhibiting the expression of P2X7R after AIS.