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Last Posted: May-16-2021 07:06:57

SARS-CoV-2 genomic surveillance identifies naturally occurring truncation of ORF7a that limits immune suppression.
A Nemundryi et al, Cell Reports, May 13, 2021

Over 950,000 whole genome sequences of SARS-CoV-2 have been determined for viruses isolated from around the world. These sequences have been critical for understanding the spread and evolution of SARS-CoV-2. Using global phylogenomics, we show that mutations frequently occur in the C-terminal end of ORF7a. We have isolated one of these mutant viruses from a patient sample and used viral challenge experiments to link this isolate (ORF7a?115) to a growth defect. ORF7a has been implicated in immune modulation, and we show that the C-terminal truncation negates anti-immune activities of the protein, which results in elevated type I interferon response to the viral infection.

Precision medicine needs an equity agenda
Nature Medicine editorial, May 14, 2021

Twenty years after release of the first human genome, genomic profiling is becoming a tool in mainstream precision medicine across health conditions. Realizing its full potential, however, will require gaining a more diverse perspective of genetic variability across human populations, and their diversity within, to ensure that the clinical application of genetics is equitable and that it reaches global impact in the next twenty years.

Deep learning for detecting congenital heart disease in the fetus
SA Morris et al, Nature Medicine, May 14, 2021

Despite substantial advances in obstetric ultrasound imaging over the past several decades, a large proportion of CHD still goes unrecognized in the prenatal period. New advances in machine learning could facilitate and reduce disparities in the prenatal diagnosis of congenital health disease, the most common and lethal birth defect.

Deep learning in histopathology: the path to the clinic
J van der Laak et al, Nature Medicine, May 14, 2021

Machine learning techniques have great potential to improve medical diagnostics, offering ways to improve accuracy, reproducibility and speed, and to ease workloads for clinicians. In the field of histopathology, deep learning algorithms have been developed that perform similarly to trained pathologists for tasks such as tumor detection and grading. However, despite these promising results, very few algorithms have reached clinical implementation.

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Disclaimer: Articles listed in the Public Health Genomics and Precision Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the 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 update, 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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