Two items relevant to long COVID

One of the tricky issues in studying in long COVID is: how widely do researchers cast their net? Initial reports acknowledged that people who were hospitalized and in intensive care may take a while to get back on their feet. But the number of people who had SARS-CoV-2 infections and were NOT hospitalized, yet experienced lingering symptoms, may be greater. A recent report from the United Kingdom, published in PLOS Medicine, studied more than Read more

All your environmental chemicals belong in the exposome

Emory team wanted to develop a standard low-volume approach that would avoid multiple processing steps, which can lead to loss of material, variable recovery, and the potential for Read more

Signature of success for an HIV vaccine?

Efforts to produce a vaccine against HIV/AIDS have been sustained for more than a decade by a single, modest success: the RV144 clinical trial in Thailand, whose results were reported in 2009. Now Emory, Harvard and Case Western Reserve scientists have identified a gene activity signature that may explain why the vaccine regimen in the RV144 study was protective in some individuals, while other HIV vaccine studies were not successful. The researchers think that this signature, 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