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Crucial information about disease outbreaks can be gleaned earlier.
Problem: Our systems for detecting outbreaks of disease are unreliable. Typically, word of outbreaks bubbles up as patients see health professionals, who report cases to authorities. Those authorities often can’t piece the reports together in time to prevent significant numbers of other people from getting sick.
Solution: Rumi Chunara, a researcher at Boston Children’s Hospital and Harvard Medical School, is mining social media and other online sources for information outside of medical settings. In one study, Chunara found that a rise in cholera-related Twitter posts in Haiti correlated with an outbreak of the disease. “That’s important, because it takes the ministry of health in Haiti a couple of weeks to get their data aggregated,” she says. In future outbreaks, tweets could help direct medical workers earlier and ensure that supplies like water purification tablets get where they’re needed.
Small RNA molecules, including microRNAs (miRNAs) and small interfering RNAs (siRNAs), offer tremendous potential as new therapeutic agents to inhibit cancer-cell growth. However, delivering thesesmall RNAs to solid tumors remains a significant challenge, as the RNAs must target the correct cells and avoid being broken down by enzymes in the body. To date, most work in this area has focused on delivery to the liver, where targeting is relatively straightforward.
This week in the journal Proceedings of the National Academy of Sciences, researchers at the Koch Institute for Integrative Cancer Research at MIT report that they have successfully delivered small RNA therapies in a clinically relevant mouse model of lung cancer to slow and shrink tumor growth. Their research offers promise for personalized RNA combination therapies to improve therapeuticresponse.
Leda Zimmerman | School of Engineering
July 14, 2014
Alumnus strikes delicate balances in big data — helping define the future of health care.
There is a “deluge of information” at the intersection of biology and computation, says Gaurav Bhatia PhD '14, a graduate of the Harvard-MIT Division of Health, Science and Technology (HST). However, Bhatia seeks to “push complexity away, and solve problems in the simplest incarnation possible.” Using newly developed statistical methods and computer models, he is accomplishing just that, with some of modern biology’s largest and most challenging data sets — those involving the human genome.