Thrombotic material retrieved from acute ischemic stroke (AIS) patients represents a valuable source of biological information. In this study, we have developed a clinical proteomics workflow to characterize the protein cargo of thrombi derived from AIS patients. To analyze the thrombus proteome in a large-scale format, we developed a workflow that combines the isolation of thrombus by endovascular thrombectomy and peptide chromatographic fractionation coupled to mass-spectrometry. Using this workflow, we have characterized a specific proteomic expression profile derived from four AIS patients included in this study. Around 1600 protein species were unambiguously identified in the analyzed material. Functional bioinformatics analyses were performed, emphasizing a clustering of proteins with immunological functions as well as cardiopathy-related proteins with blood-cell dependent functions and peripheral vascular processes. In addition, we established a reference proteomic fingerprint of 341 proteins commonly detected in all patients. Protein interactome network of this subproteome revealed protein clusters involved in the interaction of fibronectin with 14-3-3 proteins, TGFβ signaling, and TCP complex network. Taken together, our data contributes to the repertoire of the human thrombus proteome, serving as a reference library to increase our knowledge about the molecular basis of thrombus derived from AIS patients, paving the way toward the establishment of a quantitative approach necessary to detect and characterize potential novel biomarkers in the stroke field.
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http://dx.doi.org/10.3390/ijms19020498 | DOI Listing |
J Robot Surg
January 2025
Multimedia University, Cyberjaya, Malaysia.
Artificial intelligence and robotics are revolutionizing surgical practices by enhancing precision, efficiency, and patient outcomes. With global healthcare systems increasingly adopting AI-driven technologies, the integration of robotics in surgery addresses critical challenges such as surgical accuracy, minimally invasive techniques, and healthcare accessibility. However, disparities in access and ethical concerns regarding automation persist globally, necessitating a balanced discourse on these advancements.
View Article and Find Full Text PDFEur J Neurol
January 2025
Department of Neurology, Xinqiao Hospital and the Second Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Background And Objectives: Despite achieving ideal reperfusion (eTICI = 3) through endovascular treatment (EVT), some acute ischemic stroke (AIS) patients still experience poor outcomes. This study aims to evaluate the efficacy and safety of tirofiban in AIS patients with ideal reperfusion, focusing on its effects in large artery atherosclerosis (LAA) and cardioembolic (CE) stroke.
Methods: A total of 474 AIS patients from the RESCUE-BT database were included.
Spine Deform
January 2025
Department of Orthopaedic Surgery, The Johns Hopkins University, 601 N. Caroline Street, JHOC 5223, Baltimore, MD, 21287, USA.
Purpose: Few studies have investigated quality-of-life (QoL)-related outcome measures in adolescent idiopathic scoliosis (AIS) patients over long-term follow-up. We investigated whether patients with any given Lenke type improved relative to other types and whether selective fusions resulted in better QoL-related outcome measures.
Methods: We utilized the Harms Study Group database to select patients with AIS who underwent posterior spinal fusion (PSF) with Scoliosis Research Society questionnaire-22 revised (SRS-22r) scores at minimum 10-year follow-up.
J Clin Gastroenterol
February 2025
Swedish Medical Center, Seattle, WA.
Machine learning and its specialized forms, such as Artificial Neural Networks and Convolutional Neural Networks, are increasingly being used for detecting and managing gastrointestinal conditions. Recent advancements involve using Artificial Neural Network models to enhance predictive accuracy for severe lower gastrointestinal (LGI) bleeding outcomes, including the need for surgery. To this end, artificial intelligence (AI)-guided predictive models have shown promise in improving management outcomes.
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