Publications by authors named "S Abraham Ravid"

Studies in recent years indicate that reproductive tract microbial communities are crucial for shaping mammals' health and reproductive outcomes. Following parturition, uterine bacterial contamination often occurs due to the open cervix, which may lead to postpartum uterine inflammatory diseases, especially in primiparous individuals. However, investigations into spatio-temporal microbial transitions in the reproductive tract of primigravid females remain limited.

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Background: The thalamus L-sign, characterized by damage to the lateral and posterior parts of the thalamus, has recently been identified as a potential marker of partial prolonged hypoxic-ischemic injury (HII). Although prematurity-related thalamic injury is well documented, its association with the thalamus L-sign is infrequently described.

Objective: The primary objective of this study was to further investigate the thalamus L-sign in premature birth and white matter injury.

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Epilepsy is a severe chronic neurological disease affecting 60 million people worldwide. Primary treatment is with anti-seizure medicines (ASMs), but many patients continue to experience seizures. We used retrospective insurance claims data on 280,587 patients with uncontrolled epilepsy (UE), defined as status epilepticus, need for a rescue medicine, or admission or emergency visit for an epilepsy code.

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Increasingly, next-generation sequencing (NGS) is becoming an invaluable tool in the diagnosis of unexplained acute neurological disorders, such as acute encephalopathy/encephalitis. Here, we describe a brief series of pediatric patients who presented at the pediatric intensive care unit with severe acute encephalopathy, initially suspected as infectious or inflammatory but subsequently diagnosed with a monogenic disorder. Rapid exome sequencing was performed during the initial hospitalization of three unrelated patients, and results were delivered within 7-21 days.

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Chronic gastrointestinal (GI) conditions, such as inflammatory bowel diseases (IBD), offer a promising opportunity to create classification systems that can enhance the accuracy of predicting the most effective therapies and prognosis for each patient. Here, we present a novel methodology to explore disease subtypes using our open-sourced BiomedSciAI toolkit. Applying methods available in this toolkit on the UK Biobank, including subpopulation-based feature selection and multi-dimensional subset scanning, we aimed to discover unique subgroups from GI surgery cohorts.

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