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In a population-based study, Diede Loopik and colleagues investigate associations between cervical intraepithelial neoplasia and risk of preterm birth among Dutch women. ....
Study offers clues to clinical diagnosis of nephronophthisis in adults Nephronophthisis (NPH) is a kidney disease affecting mainly children. Now, for the first time, researchers at Tokyo Medical and Dental University (TMDU) have studied a number of adults with NPH and highlighted clinical, genetic and pathological characteristics that could help in confirming this challenging diagnosis. NPH is inherited in an autosomal recessive pattern and, though rare, is the commonest genetic cause of kidney failure in children. The name is derived from nephron , the functional filtration units of the kidney and phthisis , Greek for wasting away . Recent genetic studies have highlighted that NPH may also present in adults resulting in end-stage renal disease (ESRD). However, there are only a few case-reports and no cohort studies, possibly because of rarity and diagnostic difficulties. ....
TEAM QUANTUM Scientists at the Institute of Advanced Study in Science and Technology (IASST), Guwahati, have developed a deep-learning-based (part of artificial intelligence) method to evaluate hormone status for prognosis of breast cancer. This approach will help in early detection of the cancer. Breast cancer is the most common invasive cancer, accounting for 14 per cent of cancers among Indian women, both in rural and urban India. The post-cancer survival rate related to breast cancer was reported to be 60 per cent. Cancer detection has always posed a major challenge. Medical professionals use several techniques for detection, such as X-ray, computer tomography (CT) scan, positron emission tomography (PET), ultrasound, and magnetic resonance imaging (MRI), besides pathological tests such as urine and blood examination. ....
March 15, 2021 Machine-learning models can predict whether a CT-guided transthoracic biopsy will have complications and if so, how severe they will be, according to researchers from the University of Pennsylvania in Philadelphia. After training and testing machine-learning models on CT-guided biopsies from nearly 1,500 patients, the researchers found that the algorithms could produce a high level of accuracy for predicting complications and their severity. But more importantly, [they could] identify that technical procedural factors mattered the most, and delineate specific factors and their relative importance, thereby allowing an actionable prescription to optimize procedural technique and improve patient safety of transthoracic biopsies, said Dr. Eduardo Mortani Barbosa Jr., who presented the findings in a talk at ECR 2021. ....