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Deep learning accurately stains digital biopsy slides

Activation maps of neural network model for digital staining of tumors. Credit: Massachusetts Institute of Technology

Tissue biopsy slides stained using hematoxylin and eosin (H&E) dyes are a cornerstone of histopathology, especially for pathologists needing to diagnose and determine the stage of cancers. A research team led by MIT scientists at the Media Lab, in collaboration with clinicians at Stanford University School of Medicine and Harvard Medical School, now shows that digital scans of these biopsy slides can be stained computationally, using deep learning algorithms trained on data from physically dyed slides.

Pathologists who examined the computationally stained H&E images in a blind study could not tell them apart from traditionally stained slides while using them to accurately identify and grade prostate cancers. What’s more, the slides could also be computationally “de-stained” in a way that resets them to an original state for use in future studies, the researchers conclude in their May 20 study published in JAMA Network Open.

This process of computational digital staining and de-staining preserves small amounts of tissue biopsied from cancer patients and allows researchers and clinicians to analyze slides for multiple kinds of diagnostic and prognostic tests, without needing to extract additional tissue sections.

“Our development of a de-staining tool may allow us to vastly expand our capacity to perform research on millions of archived slides with known clinical outcome data,” says Alarice Lowe, an associate professor of pathology and director of the Circulating Tumor Cell Lab at Stanford University, who was a co-author on the paper. “The possibilities of applying this work and rigorously validating the findings are really limitless.”

The researchers also analyzed the steps by which the deep learning neural networks stained the slides, which is key for clinical translation of these deep learning systems, says Pratik Shah, MIT

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Mind and Body Practices for Older Adults

In 2012, the American College of Rheumatology issued recommendations for using pharmacologic and nonpharmacologic approaches for osteoarthritis (OA) of the hand, hip, and knee. The guidelines conditionally recommend tai chi, along with other non-drug approaches such as manual therapy, walking aids, and self-management programs, for managing knee OA. Acupuncture is also conditionally recommended for those who have chronic moderate-to-severe knee pain and are candidates for total knee replacement but are unwilling or unable to undergo surgical repair.

Current clinical practice guidelines from the American Academy of Sleep Medicine recommend psychological and behavioral interventions, such as stimulus control therapy or relaxation therapy, or cognitive behavioral therapy for insomnia (CBT-I), in the treatment of chronic primary and secondary insomnia for adults of all ages, including older adults. 

Overall, research suggests that some mind and body approaches, such as yoga, tai chi, and meditation-based programs may provide some benefit in reducing common menopausal symptoms.

There have only been a few studies on the effects of tai chi on cell-mediated immunity to varicella zoster virus following vaccination, but the results of these studies have shown some benefit.

There is evidence that tai chi may reduce the risk of falling in older adults. There is also some evidence that tai chi may improve balance and stability with normal aging and in people with neuro-degenerative conditions, including mild-to-moderate Parkinson’s disease and stroke.

There is some evidence that suggests mind-and-body exercise programs such as tai chi and yoga may have the potential to provide modest enhancements of cognitive function in older adults without cognitive impairment.

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