Mother's milk is OK, even for the in-between babies

“Stop feeding him milk right away – just to be safe” was not what a new mother wanted to hear. The call came several days after Tamara Caspary gave birth to fraternal twins, a boy and a girl. She and husband David Katz were in the period of wonder and panic, both recovering and figuring out how to care for them. “A nurse called to ask how my son was doing,” says Caspary, a developmental Read more

Focus on mitochondria in schizophrenia research

Despite advances in genomics in recent years, schizophrenia remains one of the most complex challenges of both genetics and neuroscience. The chromosomal abnormality 22q11 deletion syndrome, also known as DiGeorge syndrome, offers a way in, since it is one of the strongest genetic risk factors for schizophrenia. Out of dozens of genes within the 22q11 deletion, several encode proteins found in mitochondria. A team of Emory scientists, led by cell biologist Victor Faundez, recently analyzed Read more

Fetal alcohol cardiac toxicity - in a dish

Alcohol-induced cardiac toxicity is usually studied in animal models; a cell-culture based approach could make it easier to study possible interventions more Read more

Lee Cooper

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