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Hot Topics of the Day|PHGKB
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03/04/2020

Hot Topics of the Day are picked by experts to capture the latest information and publications on public health genomics and precision health for various diseases and health topics. Sources include published scientific literature, reviews, blogs and popular press articles.

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Air, Surface Environmental, and Personal Protective Equipment Contamination by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) From a Symptomatic Patient
SWX Ong et al, JAMA, March 4, 2020

Real-time reverse transcriptase–polymerase chain reaction (RT-PCR) targeting RNA-dependent RNA polymerase was used to show air, surface environmental & personal protective equipment contamination by COVID19 from a symptomatic patient.

Coronavirus Map: Tracking the Spread of the Outbreak
NY Times, March 4, 2020

Direct-to-Consumer Nutrigenetics Testing: An Overview.
Floris Matteo et al. Nutrients 2020 Feb 12(2)

In this article, we analyze the online nutrigenetics services offered by 45 companies worldwide, to obtain an overall picture of the costs, the types of nutritional traits considered and the level of scientific precision of the services proposed.

The de-coders: A historical perspective of the genetic counseling profession.
Cohen Leslie et al. Birth defects research 2020 Mar 112(4) 307-315

Using a machine learning approach to predict mortality in critically ill influenza patients: a cross-sectional retrospective multicentre study in Taiwan.
Hu Chien-An et al. BMJ open 2020 Feb 10(2) e033898

Current mortality prediction models used in the intensive care unit (ICU) have a limited role for specific diseases such as influenza, and we aimed to establish an explainable machine learning (ML) model for predicting mortality in critically ill influenza patients using a real-world severe influenza data set.

Identification of newborns at risk for autism using electronic medical records and machine learning.
Rahman Rayees et al. European psychiatry : the journal of the Association of European Psychiatrists 2020 Feb 63(1) e22

We conclude that machine learning algorithms combined with electronic medical records capture early life ASD risk as well as reveal previously unknown features to be associated with ASD-risk. Such approaches may be able to enhance the ability for accurate and efficient early detection of ASD in large populations of children.

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms
T Schaffer et al, JAMA Network Open, March 2020

In this diagnostic accuracy study using 144?231 screening mammograms from 85?580 women from the United States and 166?578 screening mammograms from 68?008 women from Sweden, no single artificial intelligence algorithm outperformed US community radiologist benchmarks.

The Path to Implementation of Artificial Intelligence in Screening Mammography Is Not All That Clear.
Mello-Thoms Claudia et al. JAMA network open 2020 Mar 3(3) e200282

It is necessary not to lose sight of the fact that the results of the recent largest study are temporary. They do not show that AI is never going to be useful for screening mammography; they simply show that today, even with an incredible amount of resources, arguably the best AI teams in the world could not meet or beat the radiologists.

NCATS Rare Diseases Are Not Rare! 2020 Challenge Details
NCATS, March 2020 Brand

The goals of this Challenge are: 1) to raise awareness for all rare diseases in a collective manner; 2) to bring attention to the many people with rare diseases; 3) to highlight the need for research and the development of new treatments; 4)to build and foster collaborations across the community.

Blood RNA analysis can increase clinical diagnostic rate and resolve variants of uncertain significance.
Wai Htoo A et al. Genetics in medicine : official journal of the American College of Medical Genetics 2020 Mar

Diagnosis of genetic disorders is hampered by large numbers of variants of uncertain significance (VUSs) identified through next-generation sequencing. Many such variants may disrupt normal RNA splicing. We examined effects on splicing of a large cohort of clinically identified variants and compared performance of bioinformatic splicing prediction tools.


Disclaimer: Articles listed in Hot Topics of the Day are selected by Public Health Genomics Branch to provide current awareness of the scientific literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the Clips, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
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