It has been reported that the incidence of thrombotic events can display a gender disparity. In particular, a lower thrombotic risk has been described in female gender. The mechanisms underlying this disparity are still poorly understood. Of great interest is the hypothesis that hormones, estrogen in particular, could play a key role. In fact, the possibility that some hormonal factors could protect women from thrombotic events appears well documented in literature. For instance, several studies aimed at the analysis of the impact of estrogen and estrogen receptors in thrombogenesis claim for the implication of these hormones either in megakaryocyte differentiation or, more intriguingly, directly affecting platelet integrity and function. In consideration of the absence of the nucleus, platelet susceptibility appears quite striking and probably due to the non-nuclear estrogen receptor function. In this review we briefly summarize our knowledge as concerns the role of estrogen and estrogen receptors in determining megakaryocyte/platelet functions and thrombogenicity.
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http://dx.doi.org/10.1016/j.ijcard.2015.03.145 | DOI Listing |
Front Pharmacol
January 2025
Department of Fetal Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, China.
Onasemnogene Abeparvovec (Zolgensma) is a gene therapy for the treatment of Spinal Muscular Atrophy (SMA) with improved motor neuron function and the potential for a singular treatment. Information on its adverse drug reactions is mainly from clinical trials and real-world studies with extensive sample sizes are lacking. In this study, we analyzed the U.
View Article and Find Full Text PDFClin Case Rep
January 2025
Emergency Intensive Care Unit Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China Chengdu Sichuan China.
We report a rare complication of left ventricular giant thrombus in a patient with fulminant myocarditis after venoarterial extracorporeal membrane oxygenation therapy. This case report offers simple anticoagulant treatment experiences to eliminate significant LV thrombosis in patient undergoing extracorporeal membrane oxygenation, so that patients do not need surgery.
View Article and Find Full Text PDFRes Pract Thromb Haemost
January 2025
Section of Hematology & Medical Oncology, Boston University School of Medicine, Boston, Massachusetts, USA.
Background: Cancer-associated thrombosis (CAT) is a leading cause of death in patients diagnosed with cancer. However, pharmacologic thromboprophylaxis use in cancer patients must be carefully evaluated due to a 2-fold increased risk of experiencing a major bleeding event within this population. The electronic health record CAT (EHR-CAT) risk assessment model (RAM) was recently developed, and reports improved performance over the widely used Khorana score.
View Article and Find Full Text PDFPlast Reconstr Surg Glob Open
January 2025
Division of Plastic, Reconstructive and Aesthetic Surgery, University of Toronto, Toronto, ON, Canada.
Background: Breast reconstruction with the deep inferior epigastric perforator (DIEP) free flap has become the gold standard for autologous breast reconstruction. Flap take-back to the operating room (OR) is an uncommon but difficult situation, requiring prompt and accessible resources. We conducted a literature review and independent expert review to inform evidence-based perioperative algorithms in the event of DIEP flap compromise.
View Article and Find Full Text PDFFront Public Health
January 2025
Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Background: Machine learning is pivotal for predicting Peripherally Inserted Central Catheter-related venous thrombosis (PICC-RVT) risk, facilitating early diagnosis and proactive treatment. Existing models often assess PICC-RVT risk as static and discrete outcomes, which may limit their practical application.
Objectives: This study aims to evaluate the effectiveness of seven diverse machine learning algorithms, including three deep learning and four traditional machine learning models, that incorporate time-series data to assess PICC-RVT risk.
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