Left: Transmitted light brightfield image of tissue-engineered retinal pigment epithelium (RPE). Mature RPE express melanin, which is a pigment that absorbs light to yield the darkened regions in the image. The individual cells can be seen as the small circular shapes that are about 0.01 to 0.02 mm in diameter. Right: The quantitative "absorbance" image with a calibrated absorbance scale on the bottom right. Artificial intelligence algorithms were able to detect subtle patterns in the pigmentation, not apparent to humans, that could predict the quality of an RPE specimen.
One day in the future when you need medical care, someone will examine you, diagnose the problem, remove some of your body’s healthy cells, and then use them to grow a cure for your ailment. The therapy will be personalized and especially attuned to you and your body, your genes, and the microbes that live in your gut. This is the dream of modern medical science in the field of “regenerative medicine.”
There are many obstacles standing between this dream and its implementation in real life, however. One obstacle is complexity.
Cells often differ so much from one another and differ in so many ways that scientists have a hard time predicting what the cells will do in any given therapeutic scenario. There are literally millions of parameters when it comes to living products. And that means millions of ways that a medical therapy could possibly go wrong.
“It is notoriously difficult to characterize cell products,” says Carl Simon, a biologist at the National Institute of Standards and Technology (NIST). “They are not stable, and they are not homogenous, and the test methods for characterizing them have large error bars.”
Simon and his colleagues want to change that by narrowing down the possibilities and increasing th ..
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