Emergent Carotid Stenting During Thrombectomy in Tandem Occlusions Secondary to Dissection: A STOP-CAD Secondary Study.

The optimal endovascular management of cervical carotid dissection causing tandem occlusion remains uncertain. We investigated the impact of emergent carotid stenting during endovascular treatment (EVT) for acute ischemic stroke (AIS) in patients with tandem occlusion secondary to cervical carotid artery dissection. This was a secondary analysis of patients treated with EVT for AIS due to occlusive carotid artery dissection and tandem occlusion included in the retrospective international Antithrombotic Treatment for Stroke Prevention in Cervical Artery Dissection (STOP-CAD) study.

Racial Differences in Discrepancies Between Subjective Ratings and Objective Assessments of Hearing Impairment.

Background And Objectives: To better understand racial/ethnic disparities in hearing aid use, we examined racial differences in discrepancies between subjective hearing ratings and objective hearing tests as a potential source of this disparity.

Research Design And Methods: A cross-sectional assessment was conducted using the data from the Health and Retirement Study (HRS). Our analytic sample included 2,568 participants aged 50 and older: 1,814 non-Hispanic White Americans and 754 non-Hispanic Black Americans.

The Power of Three: Dynactin associates with three dyneins under load for greater force production.

Cytoplasmic dynein is an essential microtubule motor protein that powers organelle transport and mitotic spindle assembly. Its activity depends on dynein-dynactin-cargo adaptor complexes, such as dynein-dynactin-BicD2 (DDB), which typically function with two dynein motors. We show that mechanical tension recruits a third dynein motor via an auxiliary BicD adaptor binding the light intermediate chain of the third dynein, stabilizing multi-dynein assemblies and enhancing force generation.

Beyond Averaging: A Transformer Approach to Decoding Event Related Brain Potentials.

The objective of this study is to assess the potential of a transformer-based deep learning approach applied to event-related brain potentials (ERPs) derived from electroencephalographic (EEG) data. Traditional methods involve averaging the EEG signal of multiple trials to extract valuable neural signals from the high noise content of EEG data. However, this averaging technique may conceal relevant information.

The challenge of long-term stroke outcome prediction and how statistical correlates do not imply predictive value.

Personalized prediction of stroke outcome using lesion imaging markers is still too imprecise to make a breakthrough in clinical practice. We performed a combined prediction and brain mapping study on topographic and connectomic lesion imaging data to evaluate (i) the relationship between lesion-deficit associations and their predictive value and (ii) the influence of time since stroke. In patients with first-ever ischaemic stroke, we first applied high-dimensional machine learning models on lesion topographies or structural disconnection data to model stroke severity (National Institutes of Health Stroke Scale 24 h/3 months) and functional outcome (modified Rankin Scale 3 months) in cross-validation.