By Sara Reardon April 26, 2021Reprints A mutation in an autism-related gene is shown. By studying Covid-19 patients, psychiatrists hope to gain new insights about how disorders like schizophrenia and autism arise, as well as why people with these conditions tend to suffer worse effects from viral infection.
National Institute of Neurological Disorders and Stroke, NIH
While the Covid-19 pandemic put many human research studies on hold, neuroscientist Grainne McAlonan of Kings College London saw it as a fortuitous opportunity a chance to accelerate her search for early signs of neurodevelopmental disorders in fetuses and newborns.
McAlonan knew that if a mother is infected by a virus during pregnancy, her child has a slightly greater chance of developing such disorders, including autism, although the overall risk is very low. The novel coronavirus gave her a way to study how viral infection and the immune response affect the developing brain, and why a small numb
Researchers Claim 100% Accuracy Predicting Autism Risk Factors in Mom s Blood
29 JANUARY 2021
For nearly one in five children diagnosed with autism spectrum disorder (ASD), the origins of their distinct mix of behavioural characteristics can be traced back to an attack carried out by their mother s immune system on their developing brain.
With help from a program designed to hunt for subtle patterns hidden in complex mixes of data, researchers have come up with a test for the antibodies responsible for the misguided assault, allowing them to predict the risk of a child being born with autism with unprecedented confidence.
Scientists from the University of California, Davis, and Stanford University in the US analysed plasma taken from 450 mothers with children diagnosed with ASD, and from 342 mothers who had children without a diagnosis.
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Using machine learning, researchers at the UC Davis MIND Institute have identified several patterns of maternal autoantibodies highly associated with the diagnosis and severity of autism.
Their study, published Jan. 22 in
Molecular Psychiatry, specifically focused on maternal autoantibody-related autism spectrum disorder (MAR ASD), a condition accounting for around 20% of all autism cases. The implications from this study are tremendous, said Judy Van de Water, a professor of rheumatology, allergy and clinical immunology at UC Davis and the lead author of the study. It s the first time that machine learning has been used to identify with 100% accuracy MAR ASD-specific patterns as potential biomarkers of ASD risk.
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Using machine learning, researchers at the UC Davis MIND Institute have identified several patterns of maternal autoantibodies highly associated with the diagnosis and severity of autism.
Their study, published in
Molecular Psychiatry, specifically focused on maternal autoantibody-related autism spectrum disorder (MAR ASD), a condition accounting for around 20% of all autism cases. The implications from this study are tremendous, said Judy Van de Water, a professor of rheumatology, allergy and clinical immunology at UC Davis and the lead author of the study. It s the first time that machine learning has been used to identify with 100% accuracy MAR ASD-specific patterns as potential biomarkers of ASD risk.