Prediction of tissue and clinical thrombectomy outcome in acute ischaemic stroke using deep learning.

The advent of endovascular thrombectomy has significantly improved outcomes for stroke patients with intracranial large vessel occlusion, yet individual benefits can vary widely. As demand for thrombectomy rises and geographic disparities in stroke care access persist, there is a growing need for predictive models that quantify individual benefits. However, current imaging methods for estimating outcomes may not fully capture the dynamic nature of cerebral ischemia and lack a patient-specific assessment of thrombectomy benefits.

Decoding the rhythmic representation and communication of visual contents.

Rhythmic neural activity is considered essential for adaptively modulating responses in the visual system. In this opinion article we posit that visual brain rhythms also serve a key function in the representation and communication of visual contents. Collating a set of recent studies that used multivariate decoding methods on rhythmic brain signals, we highlight such rhythmic content representations in visual perception, imagery, and prediction.

Rescue therapy after failed thrombectomy in medium/distal vessel occlusions: A retrospective analysis of an international, multi-center registry.

Background: There are limited therapeutic options in cases of failed reperfusion (modified thrombolysis in cerebral infarction [mTICI] score < 2b) after stent-retriever and/or aspiration based endovascular treatment (EVT) for acute ischemic stroke. Despite the absence of data supporting its use, rescue therapy (balloon angioplasty and/or stent implantation) is often utilized in such cases. Studies are limited to large vessel occlusions, while the outcomes and complications after rescue therapy in medium/distal vessel occlusions (MDVOs) have not been reported.

Rationale and Design of the Personalized Therapy Study: Evaluating Real-World Performance of Two Automated Defibrillation Therapy Algorithms.

Background: Barriers to maximizing patient benefit with implantable defibrillation devices include limited ability to tailor antitachycardia pacing (ATP) therapy in real time and identify patients at risk of heart failure (HF) events early on. The Personalized Therapy study aims to evaluate the performance of 2 algorithms, intrinsic ATP (iATP) and TriageHF, to address these barriers in routine clinical practice.

Methods And Results: The Personalized Therapy Study was designed as a prospective, multicenter, post-market registry study expected to enroll approximately 2200 patients meeting the following criteria: (1) implanted with a study-eligible device regardless of procedure type, (2) Medtronic CareLink Network enrolled, (3) TriageHF enabled within CareLink and High-Risk Alert notifications turned ON, and (4) iATP enabled.

Ultrasensitive Detection of Chemokines in Clinical Samples with Graphene-Based Field-Effect Transistors.

Due to their ultra-high sensitivity, solution-gated graphene-based field-effect transistors (SG-GFET) have been proposed for applications in bio-sensing. However, challenges regarding the functionalization of GFETs have prevented their applications in clinical diagnostics so far. Here GFET sensors based on van der Waals (vdW) heterostructures of single-layer graphene layered with a molecular ≈1 nm thick carbon nanomembrane (CNM) are presented.