A study of 112 infant deaths found that 41% were associated with a genetic disease, a higher rate than previously recognized, according to a study published in JAMA Network Open.
Genetic diseases contribute more to infant death than previously thought, according to a study published today in the journal JAMA Network Open. Researchers say, however, that the findings can open new avenues for identifying and treating life-threatening illnesses in the youngest children.
Researchers from Rady Children’s Institute for Genomic Medicine have found that the genetic disease contribution to infant deaths is higher than expected. One implication of their study, published on Thursday in JAMA Network Open, is that neonatal diagnosis strategies have the potential to decrease mortality during the first year of life.
Researchers who believe genomics can transform human health love to recount success stories. They’ll tell you about the 3-month-old boy whose heart was failing until researchers pinpointed what was ailing him. Or the baby girl who could have had a life-threatening reaction to anesthesia had researchers not sequenced her DNA ahead of time.
Though they’re not listed in national statistics, single gene disorders may be the largest single cause of death in the first year of life, according to a new research paper from the Rady Genomics Institute in San Diego.
Two thirds of children who underwent genetic testing in the pediatric intensive care unit showed a genetic variant, and a third of these children received changes in care as a result, according to a new study presented at the Society of Critical Care Medicine’s (SCCM) 2023 Critical Care Congress.
The use of diagnostic rapid whole-genome sequencing (rWGS) can play a crucial role in guiding treatment for critically ill children, researchers reported at the 2023 Critical Care Congress, in San Francisco.
Today we highlight three potentially practice-changing studies…. When the reason for a child’s critical illness is unclear, rapid genetic testing often helps identify the problem and point doctors to the right treatment – but it is currently underused, researchers say.
California-based researchers described how they trained a deep-learning model to detect DNA mutations called mosaic mutations that could support the development of treatments for several diseases.
Researchers at the UCSD School of Medicine and RCIGM have created a deep learning tool that uncovers disease-causing mosaic mutations, a first step they say to find ways to develop treatments for many diseases.
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