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

Department of Biomedical Informatics

Predict the future of critical care in #STATMadness

Emory is participating in STAT Madness, a “March Madness” style bracket competition featuring biomedical research advances instead of basketball teams. Universities or research institutes nominate their champions, research papers that were published the previous year. It’s like “Battle of the Bands.” Whoever gets the loudest — or most numerous — cheers wins.

Please check out all 64 entries, follow the 2019 STAT Madness bracket and vote here:
https://www.statnews.com/feature/stat-madness/bracket/

Emory’s entry for 2019:
It’s like the “precogs” who predict crime in the movie Minority Report, but for sepsis, the deadly response to infection. Shamim Nemati and colleagues have been exploring ways to analyze vital signs in ICU patients and predict sepsis, hours before clinical staff might otherwise notice.

As landmark clinical studies have documented, every hour of delay in giving someone with sepsis antibiotics increases their risk of mortality. So detecting sepsis as early as possible could save thousands of lives. Many hospitals have developed “sniffer” systems that monitor patients for sepsis, but this algorithm tries to spot problems way before they become apparent.

As published in 2018 in Critical Care Medicine, the algorithm can predict sepsis onset—with some false alarms—four, eight, even 12 hours ahead of time. No algorithm is going to be perfect, but it was better than any other previous sepsis predictor. The technology is headed for additional testing and evaluation at several medical centers, as part of a project supported by the federal Biomedical Advanced Research and Development Authority (BARDA).

You can fill out a whole bracket or you can just vote for Emory. The contest will last several rounds. The first round began on Monday, March 4, and lasts until the end of the week. Before 10 am Eastern time Monday morning, there were already more than 5,000 brackets entered!

If Emory advances, then people will be able to continue voting for us starting on Friday. Emory’s first opponent is a regional rival, Vanderbilt University School of Medicine. We are on the upper left side of the bracket.

STAT News is a Boston-based news organization covering biomedical research, pharma and biotech. If you feel like it, please share on social media using the hashtag #statmadness.

Posted on by Quinn Eastman in Uncategorized Leave a comment

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

Life-saving predictions from the ICU

It’s similar to the “precogs” who predict crime in the movie Minority Report, but for sepsis, the deadly response to infection. That’s how Tim Buchman, director of the Emory Critical Care Center, described an emerging effort to detect and ward off sepsis in ICU patients hours before it starts to make their vital signs go haywire.

As landmark clinical studies have documented, every hour of delay in giving someone with sepsis antibiotics increases their risk of mortality. So detecting sepsis as early as possible could save lives. Many hospitals have developed “sniffer” systems that monitor patients for sepsis risk. See our 2016 feature in Emory Medicine for more details.

What Shamim Nemati and his colleagues, including bioinformatics chair Gari Clifford, have been exploring is more sophisticated. A vastly simplified way to summarize it is: if someone has a disorderly heart rate and blood pressure, those changes can be an early indicator of sepsis.* It requires continuous monitoring – not just once an hour. But in the ICU, this can be done. The algorithm uses 65 indicators, such as respiration, temperature, and oxygen levels — not only heart rate and blood pressure. See below.

Example patient graph. Green = SOFA score. Purple = Artificial Intelligence Sepsis Expert (AISE) score. Red = official definition of sepsis. Blue = antibiotics. Black + red = cultures.    Around 4 pm on December 20, roughly 8 hr prior to any change in the SOFA score, the AISE score starts to increase. The top contributing factors were slight changes in heart rate, respiration, and temperature, given that the patient had surgery in the past 12hr with a contaminated wound and was on a mechanical ventilator. Close to midnight on December 21, other factors show abnormal changes. Five hours later, the patient met the Sepsis-3 definition of sepsis.

As recently published in the journal Critical Care Medicine, Nemati’s algorithm can predict sepsis onset – with some false alarms – 4, 8 even 12 hours ahead of time. No predictor is going to be perfect, Nemati says. The paper lays out specificity, sensitivity and accuracy under various timelines. They get to an AUROC (area under receiving operating characteristic) performance of 0.83 to 0.85, which this explainer web site rates as good (B), and is better than any other previous sepsis predictor. Read more

Posted on by Quinn Eastman in Heart, Immunology Leave a comment

Big data with heart, for psychiatric disorders

Imagine someone undergoing treatment by a psychiatrist. How do we know the treatment is really working or should be modified?

To assess whether the patient’s condition is objectively improving, the doctor could ask him or her to take home a heart rate monitor and wear it continuously for 24 hours. An app connected to the monitor could then track how much the patient’s heart rate varies over time and how much the patient moves.

Heart rate variability can be used to monitor psychiatric disorders

MD/PhD student Erik Reinertsen is the first author on two papers in Physiological Measurement advancing this approach, working under the supervision of Gari Clifford, interim chair of Emory’s Department of Biomedical Informatics.

Clifford’s team has been evaluating heart rate variability and activity as a tool for monitoring both PTSD (post-traumatic stress disorder) and schizophrenia. Clifford says his team’s research is expanding to look at treatment-resistant depression and other mental health issues.

For clinical applications, Clifford emphasizes that his plans focus on tracking disease severity for patients who are already diagnosed, rather than screening for new diagnoses. His team is involved in much larger studies in which heart rate data is being combined with physical activity data from smart watches, body patches, and clinical questionnaires, as well as other behavioral and exposure data collected through smartphone usage patterns.

Intuitively, heart rate variability makes sense for monitoring PTSD, because one of the core symptoms is hyperarousal, along with flashbacks and avoidance or numbness. However, it turns out that the time that provides the most information is when heart rate is lowest and study participants are most likely asleep, or at their lowest ebb during the night.

Home sleep tests generate a ton of information, which can be mined. This approach also fits into a trend for wearable medical technology, recently highlighted in STAT by Max Blau (subscription needed).

The research on PTSD monitoring grows out of work by cardiologists Amit Shah and Viola Vaccarino on heart rate variability in PTSD-discordant twin veterans (2013 Biological Psychiatry paper). Shah and Vaccarino had found that low frequency heart rate variability is much less (49 percent less) in the twin with PTSD. Genetics influences heart rate variability quite a bit, so studying twins allows those factors to be accounted for. Read more

Posted on by Quinn Eastman in Heart, Neuro Leave a comment