Fly model of repetitive head trauma speeds up time

Behnke and Zheng describe their model as a platform for future studies on repetitive head injury, in which they can unleash all of the genetic tools fruit flies have to Read more

Brain organoid model shows molecular signs of Alzheimer’s before birth

In a model of human fetal brain development, Emory researchers can see perturbations of epigenetic markers in cells derived from people with familial early-onset Alzheimer’s disease, which takes decades to appear. This suggests that in people who inherit mutations linked to early-onset Alzheimer’s, it would be possible to detect molecular changes in their brains before birth. The results were published in the journal Cell Reports. “The beauty of using organoids is that they allow us to Read more

The earliest spot for Alzheimer's blues

How the most common genetic risk factor in AD interacts with the earliest site of neurodegeneration Read more

Digital Slide Archive

CAPTCHA some cancer cells

Humans are good at deciphering complex images, compared to computers. Until recently, internet users often needed to verify that they were human by completing a CAPTCHA security check. A familiar variety asked the user to check all the boxes that contain a car, or a street sign.

If we asked random people off the street to look at pathology slides and “quick, check all the boxes that contain tumor cells,” what would happen? The accuracy, compared to a trained pathologist, wouldn’t be very good.

Not as easy as labeling which boxes contain street signs!

This challenge of expertise – crowdsourcing and pathology are not immediately compatible – is what Lee Cooper and colleagues sought to overcome in a recent paper published in Bioinformatics. So they put together something they called “structured crowdsourcing.”

“We are interested in describing how the immune system behaves in breast cancers, and so we built an artificial intelligence system to look at pathology slides and identify the tissue components,” Cooper says.

His group was particularly interested in the aggressive form of breast cancer: triple negative. They used pathology slide images from the Cancer Genome Atlas, a National Cancer Institute resource. The goal was to mark up the slides and label which sections contained tumor, stroma, white blood cells, dead cells etc.

They used social media to recruit 25 volunteers — medical students and pathologists from around the world (Egypt, Bangladesh, Saudi Arabia, United Arab Emirates, Syria, USA). Participants underwent training and used Slack to communicate and learn about how to classify images. They collaborated using the Digital Slide Archive, a tool developed at Emory. Read more

Posted on by Quinn Eastman in Cancer Leave a comment