Publications by authors named "Shivangi Parmar"

Objective: To assess prevalence of elevated blood pressure readings, rates of established hypertension diagnosis, and blood pressure control medication prescription rates in a cohort of older people experiencing unsheltered homelessness presenting to a Street Medicine clinic in Miami-Dade County, Florida. In addition, we will compare outcomes found in the study cohort to that of the general population.

Methods: Demographic information, clinical history, blood pressure control medication prescription, and blood pressure measurements were taken by a Street Medicine team of medical providers.

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Introduction: Preventative surgical procedures for patients who are breast cancer (BRCA) positive-namely, bilateral salpingo-oophorectomy and mastectomy-have been linked to changes in sexual function, including surgically induced menopause. A patient's decision to undergo preventive surgery as opposed to high-risk screening is heavily reliant on advice received from one's health care provider. Quality of life should be considered when shared decision making is conducted with patients.

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Vocal communication in animals often involves taking turns vocalizing. In humans, turn-taking is a fundamental rule in conversation. Among non-human primates, the common marmoset is known to engage in antiphonal calling using phee calls and trill calls.

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Mutation or disruption of the SH3 and ankyrin repeat domains 3 (SHANK3) gene represents a highly penetrant, monogenic risk factor for autism spectrum disorder, and is a cause of Phelan-McDermid syndrome. Recent advances in gene editing have enabled the creation of genetically engineered non-human-primate models, which might better approximate the behavioural and neural phenotypes of autism spectrum disorder than do rodent models, and may lead to more effective treatments. Here we report CRISPR-Cas9-mediated generation of germline-transmissible mutations of SHANK3 in cynomolgus macaques (Macaca fascicularis) and their F1 offspring.

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This paper introduces an end-to-end feedforward convolutional neural network that is able to reliably classify the source and type of animal calls in a noisy environment using two streams of audio data after being trained on a dataset of modest size and imperfect labels. The data consists of audio recordings from captive marmoset monkeys housed in pairs, with several other cages nearby. The network in this paper can classify both the call type and which animal made it with a single pass through a single network using raw spectrogram images as input.

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