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AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios.
BMC Pregnancy Childbirth
January 2025
Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, University of Utah Health, 30 N. Mario Capecchi Dr., Level 5 South, Salt Lake City, UT, 84132, USA.
Background: Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR.
Methods: Using data from 9,558 pregnancies delivered at ≥ 20 weeks with available outcome data, we derived and validated a PGM using randomly selected sub-cohorts of 80% (n = 7645) and 20% (n = 1,912), respectively, to discriminate cases of FGR resulting in composite perinatal morbidity from those that did not.
Explore the factors related to the death of offspring under age five and appraise the hazard of child mortality using machine learning techniques in Bangladesh.
BMC Public Health
January 2025
Department of Statistics and Data Science, Jahangirnagar University, Dhaka, 1342, Bangladesh.
Background: Child mortality is a reliable and significant indicator of a nation's health. Although the child mortality rate in Bangladesh is declining over time, it still needs to drop even more in order to meet the Sustainable Development Goals (SDGs). Machine Learning models are one of the best tools for making more accurate and efficient forecasts and gaining in-depth knowledge.
View Article and Find Full Text PDFAnatomical characterization of Semi-arid Bignoniaceae using light and scanning electron microscopy.
BMC Plant Biol
January 2025
Plant Production Department, College of Food and Agricultural Sciences, King Saud University, P.O. Box. 2460, Riyadh, 11451, Saudi Arabia.
Background: The present research work was done to evaluate the anatomical differences among selected species of the family Bignoniaceae, as limited anatomical data is available for this family in Pakistan. Bignoniaceae is a remarkable family for its various medicinal properties and anatomical characterization is an important feature for the identification and classification of plants.
Methodology: In this study, several anatomical structures were examined, including stomata type and shape, leaf epidermis shape, epidermal cell size, and the presence or absence of trichomes and crystals (e.
Orbital inflammatory disease: a joint clinical experience in rheumatology and ophthalmology.
Int Ophthalmol
January 2025
Department of Ophtalmology, Dokuz Eylul University School of Medicine, Izmir, Turkey.
Purpose: This retrospective study aimed to characterize the clinical features, histopathological findings, and treatment outcomes of patients diagnosed with orbital inflammatory disease (OID) co-managed by the rheumatology and ophthalmology departments in a tertiary hospital.
Methods: Medical records of 14 patients with OID were analyzed. Data on demographics, clinical presentation, laboratory investigations, radiological imaging, histopathological results, treatment regimens, and disease outcomes were collected and reviewed.
Evaluation of the role of circular RNA (circ-FAF1) as a diagnostic biomarker for breast cancer in a cohort of Egyptian breast cancer patients.
Mol Biol Rep
January 2025
Department of Clinical Pathology, Faculty of Medicine, Alexandria University, Alexandria, Egypt.
Background: The identification of circulating potential biomarkers may help earlier diagnosis of breast cancer, which is critical for effective treatment and better disease outcomes. We aimed to study the role of circ-FAF1 as a diagnostic biomarker in female breast cancer using peripheral blood samples of these patients, and to investigate the relation between circ-FAF1 and different clinicopathological features of the included patients.
Methods And Results: This case-control study enrolled 60 female breast cancer patients and 60 age-matched healthy control subjects.
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