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The use of intravenous thrombolytic therapy (ITT) in acute ischemic stroke (AIS) patients is still debated in China. We present the analysis of clinico-demographic retrospective data of 646 AIS patients that were treated by alteplase ITT at our hospital. The data collected included age, gender, education, income, drug use before disease onset, and awareness of stroke/ITT. The risk factors studied were hypertension, diabetes, hyperlipidemia, atrial fibrillation, coronary heart disease, cerebral infarction, transient ischemic attack, valvular heart disease, thyroid disease, migraine, asymptomatic carotid stenosis, family history of stroke, hyperhomocysteinemia, smoking, drinking, and gingivitis. Pre-ITT patient data included blood pressure and time from onset to hospital. Post-ITT patient data included National Institutes of Health Stroke Scale (NIHSS) scores, clinical outcome, revascularization, hemorrhage, healing rate, and 90-day mortality. Hospital management information included monthly ITT cases, discharges, bed turnaround times, length of hospital stay, bed utilization, drug ratio, massive cerebral infarction decompressive craniectomy, and social impact. Prognosis evaluation was based on post-ITT NIHSS and modified Rankin Scale (mRS) scores. We found that ITT success rate was 75.85 %, with a bleeding rate of 1.55 % and a 90-day mortality rate of 2.01 %. Overall, the data suggest that the ITT therapy was highly successful in AIS patients treated at our hospital.
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http://dx.doi.org/10.1007/s12013-014-0394-6 | DOI Listing |
J Palliat Med
September 2025
Skaggs School of Pharmacy & Pharmaceutical Sciences, UC San Diego Health Sciences, San Diego, California, USA.
Artificial intelligence (AI), particularly large language models (LLMs), offers the potential to augment clinical decision-making, including in palliative care pharmacy, where personalized treatment and assessments are important. Despite the growing interest in AI, its role in clinical reasoning within specialized fields such as palliative care remains uncertain. This study examines the performance of four commercial-grade LLMs on a Script Concordance Test (SCT) designed for pharmacy students in a pain and palliative care elective, comparing AI outputs with human learners' performance at baseline.
View Article and Find Full Text PDFInt J Surg
September 2025
Department of Respiratory and Critical Care Medicine, Hubei Province Clinical Research Center for Major Respiratory Diseases, Key Laboratory of Pulmonary Diseases of National Health Commission, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
Background: Precise preoperative discrimination of invasive lung adenocarcinoma (IA) from preinvasive lesions (adenocarcinoma in situ [AIS]/minimally invasive adenocarcinoma [MIA]) and prediction of high-risk histopathological features are critical for optimizing resection strategies in early-stage lung adenocarcinoma (LUAD).
Methods: In this multicenter study, 813 LUAD patients (tumors ≤3 cm) formed the training cohort. A total of 1,709 radiomic features were extracted from the PET/CT images.
Rev Cardiovasc Med
August 2025
Department of Radiology, The Fourth Affiliated Hospital of Soochow University (Suzhou Dushu Lake Hospital), 215124 Suzhou, Jiangsu, China.
Background: Identifying the etiology of acute ischemic stroke (AIS) is critical for secondary prevention and treatment choice in stroke patients. This study aimed to investigate the dual-energy computed tomography (DECT) quantitative thrombus parameters associated with cardioembolic (CE) stroke and develop a nomogram that combines DECT and clinical data to identify CE stroke.
Methods: We retrospectively reviewed all consecutive patients from January 2020 to July 2022 with anterior circulation stroke and proximal intracranial occlusions.
Alpha Psychiatry
August 2025
Information Sciences and Technology, George Mason University, Fairfax, VA 22030, USA.
Background: Herein, we report on the initial development, progress, and future plans for an autonomous artificial intelligence (AI) system designed to manage major depressive disorder (MDD). The system is a web-based, patient-facing conversational AI that collects medical history, provides presumed diagnosis, recommends treatment, and coordinates care for patients with MDD.
Methods: The system includes seven components, five of which are complete and two are in development.
Comput Methods Programs Biomed
September 2025
Electrical and Computer Engineering Department, School of Engineering, Morgan State University, Baltimore, MD, 21251, USA. Electronic address:
Breast Cancer (BC) remains a leading cause of morbidity and mortality among women globally, accounting for 30% of all new cancer cases (with approximately 44,000 women dying), according to recent American Cancer Society reports. Therefore, accurate BC screening, diagnosis, and classification are crucial for timely interventions and improved patient outcomes. The main goal of this paper is to provide a comprehensive review of the latest advancements in BC detection, focusing on diagnostic BC imaging, Artificial Intelligence (AI) driven analysis, and health disparity considerations.
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