{"id":753,"date":"2020-08-26T09:31:58","date_gmt":"2020-08-26T09:31:58","guid":{"rendered":"https:\/\/clinlabint.3wstaging.nl\/big-data-approach-shown-to-be-effective-for-evaluating-autism-treatments\/"},"modified":"2021-01-08T11:08:12","modified_gmt":"2021-01-08T11:08:12","slug":"big-data-approach-shown-to-be-effective-for-evaluating-autism-treatments","status":"publish","type":"post","link":"https:\/\/clinlabint.com\/big-data-approach-shown-to-be-effective-for-evaluating-autism-treatments\/","title":{"rendered":"Big data approach shown to be effective for evaluating autism treatments"},"content":{"rendered":"<p><span lang=\"EN-GB\">Researchers at Rensselaer Polytechnic Institute who developed a blood test to help diagnose autism spectrum disorder have now successfully applied their distinctive big data-based approach to evaluating possible treatments.<\/span><br \/>\n<span lang=\"EN-GB\">The findings have the potential to accelerate the development of successful medical interventions. One of the challenges in assessing the effectiveness of a treatment for autism is how to measure improvement. Currently, diagnosis and evaluating the success of an intervention rely heavily on observations by professionals and caretakers.<\/span><br \/>\n<span lang=\"EN-GB\">\u201cHaving some kind of a measure that measures something that\u2019s happening inside the body is really important,\u201d said Juergen Hahn, systems biologist, professor, and head of the Rensselaer Department of Biomedical Engineering.<\/span><br \/>\n<span lang=\"EN-GB\">Hahn and his team use machine-learning algorithms to analyse complex data sets. That is how he previously discovered patterns with certain metabolites in the blood of children with autism that can be used to successfully predict diagnosis. You can watch Hahn discuss that here.<\/span><br \/>\n<span lang=\"EN-GB\">In this most recent analysis, the team used a similar set of measurements from three different clinical trials that examined potential metabolic interventions. The researchers were able to compare data from before and after treatment, and look for correlations between those results and any observed changes of adaptive behavior.<\/span><br \/>\n<span lang=\"EN-GB\">\u201cWhat we did here is showed that if you actively try to change concentrations of these metabolites that are being measured, then you will also see changes in the behaviour,\u201d Hahn said.<\/span><br \/>\n<span lang=\"EN-GB\">Hahn said that this approach was unique in that it analysed multiple medical markers at the same time, unveiling correlations not seen in the data if each measurement is investigated individually.<\/span><br \/>\n<span lang=\"EN-GB\">\u201cIt can speed up the development process because you now have an additional tool that tells you how well a treatment has worked,\u201d he said.<\/span><br \/>\n<span lang=\"EN-GB\">Hahn expects this type of approach to become an important component of clinical trials for autism in the future. \u201cHaving medical tests that measure quantities directly related to the physiology is important and we hope that they get incorporated into future trials,\u201d he said.<\/span><br \/>\n<span lang=\"EN-GB\">Rensselaer Center for Biotechnology and Interdisciplinary Studies<\/span>\n<link https:\/\/tinyurl.com\/y4t4f54s><span lang=\"EN-GB\">https:\/\/tinyurl.com\/y4t4f54s<\/span><\/link>\n","protected":false},"excerpt":{"rendered":"<p>Researchers at Rensselaer Polytechnic Institute who developed a blood test to help diagnose autism spectrum disorder have now successfully applied their distinctive big data-based approach to evaluating possible treatments. The findings have the potential to accelerate the development of successful medical interventions. One of the challenges in assessing the effectiveness of a treatment for autism [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[35],"tags":[],"_links":{"self":[{"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/posts\/753"}],"collection":[{"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/comments?post=753"}],"version-history":[{"count":0,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/posts\/753\/revisions"}],"wp:attachment":[{"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/media?parent=753"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/categories?post=753"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/clinlabint.com\/wp-json\/wp\/v2\/tags?post=753"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}