Background: Hemovigilance has become one of the important quality check systems of blood transfusion process, but under/non-reporting of transfusion-associated adverse reactions despite the presence of reporting systems emphasize the need to understand the challenges being faced in active reporting of adverse transfusion reactions.
Aim: To identify and document the possible factors leading to under-reporting and impacting the quality of blood transfusion reactions being submitted under Haemovigilance Programme of India (HvPI).
Settings And Design: This was a cross-sectional, observational type study, carried out in six blood banks, two each of government, private, and stand-alone sectors in Delhi National Capital Region enrolled under HvPI.
Objective: To evaluate the effectiveness of combination therapy based on motion feedback training in patients recovering from ischemic stroke.
Methods: A retrospective analysis was conducted on 205 patients in the recovery phase of ischemic stroke admitted between June 2022 and June 2023. Patients were divided into two groups: the conventional treatment group (n=101), receiving standard care, and the combination therapy group (n=104), receiving additional motion feedback training for 30 days.
Advances in biofabrication have enabled the generation of freeform perfusable networks mimicking vasculature. However, key challenges remain in the effective endothelialization of these complex, vascular-like networks, including cell uniformity, seeding efficiency, and the ability to pattern multiple cell types. To overcome these challenges, we present an integrated fabrication and endothelialization strategy to directly generate branched, endothelial cell-lined networks using a diffusion-based, embedded 3D bioprinting process.
View Article and Find Full Text PDFIntroduction: Mechanical thrombectomy (MT) for medium vessel occlusions (MeVO) is emerging as a promising treatment in acute stroke. We aim to evaluate the utility of additional imaging (CTP) in patients with MeVOs who received thrombolysis at a spoke hospital and were transferred to the hub.
Methods: This was a retrospective review of prospectively collected data from April 2018 to June 2023.
More than three billion years of evolution have produced an image of biology encoded into the space of natural proteins. Here we show that language models trained at scale on evolutionary data can generate functional proteins that are far away from known proteins. We present ESM3, a frontier multimodal generative language model that reasons over the sequence, structure, and function of proteins.
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