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Anti-Inflammatory and Antioxidant Effects of Aqueous Extract on the Liver and Kidney of PCOS Mice.
J Evid Based Integr Med
January 2025
Department of Biological Sciences, Faculty of Science, Beirut Arab University, Beirut, Lebanon.
Background: Polycystic Ovarian Syndrome (PCOS) is an endocrine disorder associated with increased risk of kidney and liver damage. Current treatments have shown contradictory outcomes, and their long-term use causes unwanted side effects. could serve as a complementary medicine to current PCOS treatments.
View Article and Find Full Text PDFAdvances and applications of genome-edited animal models for severe combined immunodeficiency.
Zool Res
January 2025
Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, Guangdong 510280, China. E-mail:
Severe combined immunodeficiency disease (SCID), characterized by profound immune system dysfunction, can lead to life-threatening infections and death. Animal models play a pivotal role in elucidating biological processes and advancing therapeutic strategies. Recent advances in gene-editing technologies, including zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), CRISPR/Cas9, and base editing, have significantly enhanced the generation of SCID models.
View Article and Find Full Text PDFPerformance of Chat Generative Pre-trained Transformer-4o in the Adult Clinical Cardiology Self-Assessment Program.
Eur Heart J Digit Health
January 2025
Cardiovascular Center, Tufts Medical Center, 800 Washington Street, Boston, MA 02111, USA.
Aims: This study evaluates the performance of OpenAI's latest large language model (LLM), Chat Generative Pre-trained Transformer-4o, on the Adult Clinical Cardiology Self-Assessment Program (ACCSAP).
Methods And Results: Chat Generative Pre-trained Transformer-4o was tested on 639 ACCSAP questions, excluding 45 questions containing video clips, resulting in 594 questions for analysis. The questions included a mix of text-based and static image-based [electrocardiogram (ECG), angiogram, computed tomography (CT) scan, and echocardiogram] formats.
Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis.
Eur Heart J Digit Health
January 2025
School of Life Course & Population Sciences, King's College London, SE1 1UL London, UK.
Cardiovascular disease (CVD) remains a major cause of mortality in the UK, prompting the need for improved risk predictive models for primary prevention. Machine learning (ML) models utilizing electronic health records (EHRs) offer potential enhancements over traditional risk scores like QRISK3 and ASCVD. To systematically evaluate and compare the efficacy of ML models against conventional CVD risk prediction algorithms using EHR data for medium to long-term (5-10 years) CVD risk prediction.
View Article and Find Full Text PDFDeterminants of physical function, as measured using PROMIS PF-10a, in patients with rheumatoid arthritis: results from the international COVID-19 Vaccination in Autoimmune Diseases (COVAD) study.
Rheumatol Adv Pract
December 2024
Rheumatology Department, King's College Hospital, London, UK.
Objectives: Physical function in RA is largely influenced by multiple clinical factors, however, there is a growing body of evidence that psychological state and other comorbidities also play an essential role. Using data obtained in the COVID-19 Vaccination in Autoimmune Diseases study, an international self-reported e-survey, we aimed to explore the predictive ability of sociodemographic and clinical variables on Patient-Reported Outcomes Measurement Information System Physical Function Short Form 10a (PROMIS PF-10a) in RA and to investigate variation in disease activity and functional outcomes based on country-level socio-economic parameters.
Methods: Patient demographics, disease characteristics including current symptom status, functional status and treatment variables, as well as income level of the country of residence, were extracted from survey responses.
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