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Research Roundup: Oral medication for COVID-19, air pollution increases infant mortality, AI predicts depression


Each week, The Daily’s Science & Tech section produces a roundup of the most exciting and influential research happening on campus or otherwise related to Stanford. Here’s our digest for the week of June 28 – July 4.

Researchers begin clinical trial for oral COVID-19 treatment

A new Stanford Medicine clinical trial involving favipiravir, an oral medication, is underway to test the effectiveness of the drug in reducing symptoms and shortening the duration of COVID-19. The clinical trial will begin on July 6 and seeks to enroll 120 patients in total.

“We hope that this drug can help to reduce transmission within families, groups and schools,” clinical medicine professor Aruna Subramanian told Stanford Medicine News. “Plus, it would be really nice to have pills that can be given early on to make people get better faster.”

Favipiravir is not approved for treating coronavirus by the Food and Drug Administration and the Stanford clinical trial will be the first to test favipiravir on outpatients. The oral medication is approved to treat the novel coronavirus in Russia, China and India.

“Favipiravir could be very important for symptom relief, especially for patients with mild cases who can have symptoms for a long time,” Subramanian told Stanford Medicine News. “We’ve seen a number of symptoms continue, such as coughs, shortness of breath, fatigue.”

Air pollution linked to infant mortality

Air pollution dust particles from the Sahara Desert significantly increase infant mortality, a study published on June 29 in “Nature Sustainability” found. Currently, these dust particles are making their way across the Southeast U.S., leading to higher air pollution levels in the region.

“Africa and other developing regions have made remarkable strides overall in improving child health in recent decades, but key negative outcomes such as infant mortality remain stubbornly high in some places,” earth system science associate professor Marshall Burke B.A. ’03 told Stanford News. “We wanted to understand why that was, and whether there was a connection to air pollution, a known cause of poor health.”

The researchers analyzed nearly 1 million births spanning 15 years from household surveys in 30 Sub-Saharan African countries. The findings suggest there was a 25% increase in the annual mean particulate concentrations, which led to an 18% increase in infant mortality in West Africa. 

The team estimates that irrigation systems to decrease particulate density in the air could save 37,000 infant deaths annually.

“Standard policy instruments can’t be counted on to reduce all forms of air pollution,” Samuel Heft-Neal, a research scholar at the Center on Food Security and the Environment, told Stanford News. “While our calculation doesn’t consider logistical constraints to project deployment, it highlights the possibility of a solution that targets natural pollution sources and yields enormous benefits at a modest cost.”

AI predicts improving depression symptoms during treatment

An artificial intelligence (AI) algorithm can interpret unique brainwaves from patients with depression, and recognize which depression symptoms change during a patient’s treatment, a study published on June 22 in “JAMA Network Open” reports.

“We know that depression is very heterogeneous, and that there are at least 1,000 unique combinations of symptoms that can be diagnosed as depression,” psychiatry and behavioral sciences professor Leanne Williams told Stanford Medicine’s blog, SCOPE. “We’ve found that brainwave measurements can be used to help identify which particular symptoms change with antidepressant treatment and which do not.”

The study utilized brainwave data from a little over 500 patients diagnosed with depression. Each participant was randomly assigned one of three different antidepressant drugs for eight weeks. The findings suggest the AI correctly predicted which symptoms improved with treatment.

“We can apply artificial intelligence to learn complex relationships in data,” fifth-year computer science Ph.D. student Pranav Rajpurkar told SCOPE. “We are able to learn and discover interesting relationships between a patient’s depression symptoms — and EEG readings — at start of treatment, and their depression symptoms eight weeks in.”

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