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The Stockholm early detection of cancer study (STEADY-CAN): rationale, design, data collection, and baseline characteristics for 2.7 million participants.
Eur J Epidemiol
January 2025
Department of Neurobiology, Care Sciences and Society, Division of Family Medicine and Primary Care, Karolinska Institutet, Stockholm, Sweden.
The Stockholm Early Detection of Cancer Study (STEADY-CAN) cohort was established to investigate strategies for early cancer detection in a population-based context within Stockholm County, the capital region of Sweden. Utilising real-world data to explore cancer-related healthcare patterns and outcomes, the cohort links extensive clinical and laboratory data from both inpatient and outpatient care in the region. The dataset includes demographic information, detailed diagnostic codes, laboratory results, prescribed medications, and healthcare utilisation data.
View Article and Find Full Text PDFRisk Factors for Anastomotic Leakage: A Comprehensive Single-Center Analysis of Colorectal Anastomoses for Ovarian and Gastrointestinal Cancers.
Ann Surg Oncol
January 2025
Department Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Rome, Italy.
Background: Anastomotic leakage (AL) is a major complication in colorectal surgery, particularly following rectal cancer surgery, necessitating effective prevention strategies. The increasing frequency of colorectal resections and anastomoses during cytoreductive surgery (CRS) for peritoneal carcinomatosis further complicates this issue owing to the diverse patient populations with varied tumor distributions and surgical complexities. This study aims to assess and compare AL incidence and associated risk factors across conventional colorectal cancer surgery (CRC), gastrointestinal CRS (GI-CRS), and ovarian CRS (OC-CRS), with a secondary focus on evaluating the role of protective ostomies.
View Article and Find Full Text PDFData from the All of Us research program reinforces existence of activity inequality.
NPJ Digit Med
January 2025
Department of Biomedical Engineering, Duke University, Durham, NC, USA.
Large-scale and detailed analyses of activity in the United States (US) remain limited. In this work, we leveraged the comprehensive wearable, demographic, and survey data from the All of Us Research Program, the largest and most diverse population health study in the US to date, to apply and extend the previous global findings on activity inequality within the context of the US. We found that daily steps differed by sex at birth, age, body characteristics, geography, and built environment.
View Article and Find Full Text PDFResearch progress of non-coding RNA regulating the role of PANoptosis in diabetes mellitus and its complications.
Apoptosis
January 2025
Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, China.
Diabetes is a chronic metabolic disease that is endemic worldwide and is characterized by persistent hyperglycemia accompanied by multiple severe complications, including cardiovascular disease, kidney dysfunction, neuropathy, and retinopathy. The pathogenesis of diabetes mellitus and its complications is multifactorial, involving various molecular and cellular pathways. In recent years, research has indicated that mechanisms of cell death play a significant role in the advancement of diabetes and its complications.
View Article and Find Full Text PDFMachine learning-based interpretation of non-contrast feature tracking strain analysis and T1/T2 mapping for assessing myocardial viability.
Sci Rep
January 2025
Cardiovascular Research Center, Rajaie Cardiovascular, Medical, and Research Center, University of Medical Sciences, Tehran, Iran.
Assessing myocardial viability is crucial for managing ischemic heart disease. While late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is the gold standard for viability evaluation, it has limitations, including contraindications in patients with renal dysfunction and lengthy scan times. This study investigates the potential of non-contrast CMR techniques-feature tracking strain analysis and T1/T2 mapping-combined with machine learning (ML) models, as an alternative to LGE-CMR for myocardial viability assessment.
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