Publications by authors named "M C Hedges"
Hematologic complications are common in pregnancy and can significantly impact both maternal and fetal health. Recognizing and treating these complications can be challenging due to the limited evidence available to guide clinical consultants. Iron deficiency anemia is the most prevalent hematologic issue in pregnancy and often occurs due to increased maternal blood volume and the nutritional demands of the growing fetus.
View Article and Find Full Text PDFObjective: Metabolic dysfunction-associated steatotic liver disease (MASLD) and vasomotor symptoms (VMS) are associated with increased cardiovascular disease risk. Although visceral adiposity has been linked to MASLD and VMS independently, this study aimed to evaluate associations between the two in midlife women.
Methods: A cross-sectional study of women aged 45-60 years receiving primary care at one of four sites was conducted from March 1 through June 30, 2021.
Article Synopsis
- * Out of 4,295 women surveyed, 19.7% were caregivers, and those providing more hours of caregiving reported worse menopause symptoms, with significant increases as caregiving hours rose.
- * The findings indicate a strong link between higher caregiving hours and increased menopause symptoms, suggesting the need for better support for midlife women juggling caregiving responsibilities.
Article Synopsis
- - A new scintillation material featuring InAs quantum dots in a GaAs matrix was developed and tested for its efficiency with various types of radiation.
- - The research introduced a design methodology for an integrated photodetector with low defect density, optimizing its performance to match the quantum dot emission spectrum using a specialized buffer layer.
- - Results showed the detector's electron yield for α-particles and photons, with alpha particles yielding 13 electrons/keV and photons yielding between 30-60 electrons/keV, alongside improved radiation hardness of InAs QDs in AlGaAs compared to those in GaAs.
Pharmacogenomic (PGx) biomarkers integrated using machine learning can be embedded within the electronic health record (EHR) to provide clinicians with individualized predictions of drug treatment outcomes. Currently, however, drug alerts in the EHR are largely generic (not patient-specific) and contribute to increased clinician stress and burnout. Improving the usability of PGx alerts is an urgent need.
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