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Page 12 - ம்க்கார்‌மிக் பள்ளி ஆஃப் பொறியியல் News Today : Breaking News, Live Updates & Top Stories | Vimarsana

Sweat Sticker Diagnoses Cystic Fibrosis on the Skin in Real Time

New wearable device changes color to detect early disease marker in newborns sweat Mar 31, 2021 // Amanda Morris A team of Northwestern investigators have developed a soft, flexible and skin-like device that can measure chloride levels in a patient’s sweat for improved diagnosis of cystic fibrosis. A Northwestern Engineering-led research team has developed a novel skin-mounted sticker that absorbs sweat and then changes color to provide an accurate, easy-to-read diagnosis of cystic fibrosis within minutes. While measuring chloride levels in sweat to diagnose cystic fibrosis is standard, the soft, flexible, skin-like “sweat sticker” offers a stark contrast to current diagnostic technologies, which require a rigid, bulky, wrist-strapped device to collect sweat.

New blueprint for zero-emission cement and concrete by 2050

New blueprint for zero-emission cement and concrete by 2050 Northwestern University researchers, in partnership with ClimateWorks Foundation, have developed a new blueprint for reducing carbon emissions in concrete, the world’s most-used building material. The report, “Decarbonizing Concrete: Deep decarbonization pathways for the cement and concrete cycle in the United States, India and China,” was published today (March 16) on ClimateWorks’ website. It highlights multiple ways including production-size mitigation measures and demand reduction by use of lean construction and sustainable building materials to drive the cement industry toward net-zero emissions by 2050. “One clear conclusion we arrived at in the course of our research is that there is no single solution, but rather a range of small and large changes that will be necessary to achieve net-zero emission targets,” said Eric Masanet, the project’s principal investigator.

AI identifies pain levels from patient data

AI identifies pain levels from patient data A research team led by Northwestern University faculty and alumni has found it’s possible to understand a patient’s pain level by examining data from vital signs. In a new study, the team developed and applied artificial intelligence (AI), or machine-learning, algorithms to physiological data including respiratory rate, blood pressure, heart rate, body temperature and oxygen levels from patients with chronic pain from sickle cell disease. Not only did the researchers’ approach outperform baseline models to estimate subjective pain levels, it also detected changes in pain and atypical pain fluctuations. The study was published March 11 in the journal PLOS Computational Biology. This is the first paper to demonstrate that machine learning can be used to find clues to pain hidden within data from patients’ vital signs.

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