From Stanford: “A voyage to inner space: The diagnostics emerging from Stanford”

Stanford University Name
Stanford University

December 12, 2016
Kathy Zonana

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Paul Wearing

I was 5, or maybe 6. We were standing in line at Disneyland. And, suddenly, I was terrified.

I had thought we were in line for Mission to Mars, a benign if uninspired ride in which you sat in a round room that vibrated as you pretended to travel to the red planet. Instead, we were in line for Adventures Thru Inner Space, an attraction that promised — indeed, visually demonstrated — that it would shrink us down to microscopic versions of ourselves, so that we could travel through the hydrogen and oxygen atoms that make up a snowflake. I was sure we’d have to join an alternate society of miniaturized humans, never to see our friends and family again.

I thought about Inner Space more than once when reporting my Stanford Medicine story on innovative diagnostics emerging from Stanford. They include, for example, a machine that detects a dozen human diseases from one drop of blood. And a micropipette that can gently tug on a one-celled human embryo, helping scientists evaluate its viability for a healthy pregnancy. And a technique to inject teeny, tiny bits of iron into the bloodstream of a pediatric cancer patient, providing better and safer imaging than ever before.

That last development comes from the research of radiologist Heike Daldrup-Link, MD. Daldrup-Link uses ferumoxytol, or iron nanoparticles, as an MRI contrast agent, rather than the traditional gadolinium chelates. “Gadolinium is a heavy metal, so it’s not very natural to our body, whereas an iron product is basically a concentrated steak, or a ton of strawberries,” she told me. Using ferumoxytol for whole-body MRI, she is hoping to decrease the number of pediatric cancer patients who need CT scans, thereby reducing their exposure to radiation and their risk of secondary cancers.

Not all of the developments I write about involve work at the molecular or cellular level. Some, in fact, use big data, including a “machine-learning pathologist” that can distinguish between the two most common types of lung cancer better than humans can, and an algorithm that can predict up to 15 percent of pancreatic cancer diagnoses based on people’s web searches of ailments from the previous year. The story describes other innovative uses of technology, too, including a crowd-sourcing experiment that sheds light on how people learn to diagnose bladder cancer, and an inexpensive retinal camera that works with your smartphone.

Turns out that exploring our insides isn’t so scary after all.

See the full article here .

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