Flash Forward is a show about possible (and not so possible) future scenarios. What would the warranty on a sex robot look like? How would diplomacy work if we couldn’t lie? Could there ever be a fecal transplant black market? (Complicated, it wouldn’t, and yes, respectively, in case you’re curious.) Hosted and produced by award winning science journalist Rose Eveleth, each episode combines audio drama and journalism to go deep on potential tomorrows, and uncovers what those futures might re ...
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TGen Talks에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 TGen Talks 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
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Episode 67: Tiny particles offer big clues toward predicting Alzheimer’s decades in advance
Manage episode 398360345 series 1936276
TGen Talks에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 TGen Talks 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
Alzheimer’s disease affects an estimated six million Americans. Diagnosing and treating the disease is challenging, and for families taking care of a loved one with Alzheimer’s, it’s even more difficult. Detecting and addressing the disease early on is crucial due to its progressive nature. However, Alzheimer’s symptoms can resemble those of other non-progressive conditions. In a recent Cells publication, a team of scientists describe using machine learning models to identify changes in RNA molecules of plasma extracellular vesicles (EVs) that may hold potential for identifying Alzheimer’s disease (AD) at its earliest stages. This is one of the first studies to show changes in the RNA molecules of plasma EVs that precede neurodegeneration and provides evidence that some of the hidden pathology taking place early in the disease is reflected in plasma EVs, where it can be accessed in a minimally invasive manner and used for biomarker development. On this edition of TGen Talks, study co-author and TGen Neurogenomics Division staff scientist Joanna Palade, Ph.D., discusses their findings, and how what sound like magic or a fortune teller's promise, is the goal of the scientists working to develop a simple test; one that wouldn't simply indicate whether your symptoms might progress to an Alzheimer's diagnosis, but could also estimate the timeframe for when it might occur.
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77 에피소드
Manage episode 398360345 series 1936276
TGen Talks에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 TGen Talks 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
Alzheimer’s disease affects an estimated six million Americans. Diagnosing and treating the disease is challenging, and for families taking care of a loved one with Alzheimer’s, it’s even more difficult. Detecting and addressing the disease early on is crucial due to its progressive nature. However, Alzheimer’s symptoms can resemble those of other non-progressive conditions. In a recent Cells publication, a team of scientists describe using machine learning models to identify changes in RNA molecules of plasma extracellular vesicles (EVs) that may hold potential for identifying Alzheimer’s disease (AD) at its earliest stages. This is one of the first studies to show changes in the RNA molecules of plasma EVs that precede neurodegeneration and provides evidence that some of the hidden pathology taking place early in the disease is reflected in plasma EVs, where it can be accessed in a minimally invasive manner and used for biomarker development. On this edition of TGen Talks, study co-author and TGen Neurogenomics Division staff scientist Joanna Palade, Ph.D., discusses their findings, and how what sound like magic or a fortune teller's promise, is the goal of the scientists working to develop a simple test; one that wouldn't simply indicate whether your symptoms might progress to an Alzheimer's diagnosis, but could also estimate the timeframe for when it might occur.
…
continue reading
77 에피소드
Alle Folgen
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