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http://dx.doi.org/10.1177/00030651090570011004 | DOI Listing |
Int J Surg
January 2025
Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, Germany.
Objectives: Every year, around 300 million surgeries are conducted worldwide, with an estimated 4.2 million deaths occurring within 30 days after surgery. Adequate patient education is crucial, but often falls short due to the stress patients experience before surgery.
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March 2025
Aklilu Lemma Institute of Pathobiology, Addis Ababa University, Addis Ababa, Ethiopia.
Objectives: To assess tuberculosis (TB) and associated factors among patients with presumptive TB with chronic kidney disease (CKD).
Methods: A prospective cross-sectional study was conducted from January to December 2023 among 381 patients with CKD attending six hospitals found in five regions of Ethiopia. Sputum and urine specimens were collected and examined for TB using smear microscopy, culture, and Xpert MTB/RIF Ultra assay.
J Occup Environ Med
January 2025
Department of Digital Mental Health, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan.
Drugs Real World Outcomes
January 2025
Department of Cardiology, Angiology and Intensive Care Medicine, German Heart Center of the Charité, Berlin, Germany.
Background: Alirocumab is a fully human monoclonal antibody to proprotein convertase subtilisin kexin type 9 used for the reduction of low-density lipoprotein cholesterol (LDL-C) in high-risk patients not reaching their LDL-C target. Recently, a 2-mL prefilled autoinjector has been developed to support the monthly 300-mg dosing regimen with a single-injection administration.
Methods And Objectives: Monthly application of 300 mg AlirRocumab (Praluent) using the 2-mL SYDNEY Device (MARS) is a non-interventional, open, prospective, multi-center cohort study conducted in Germany between 2021 and 2023 with an observational period of 12 weeks.
Int J Cardiovasc Imaging
January 2025
Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Artificial intelligence-based quantitative coronary angiography (AI-QCA) was introduced to address manual QCA's limitations in reproducibility and correction process. The present study aimed to assess the performance of an updated AI-QCA solution (MPXA-2000) in lesion detection and quantification using manual QCA as the reference standard, and to demonstrate its superiority over visual estimation. This multi-center retrospective study analyzed 1,076 coronary angiography images obtained from 420 patients, comparing AI-QCA and visual estimation against manual QCA as the reference standard.
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