Posts tagged Colorado Boulder
CU: Stem cells boost aging muscles
Feb 16th
to new methods of mitigating muscle loss
New findings on why skeletal muscle stem cells stop dividing and renewing muscle mass during aging points up a unique therapeutic opportunity for managing muscle-wasting conditions in humans, says a new University of Colorado Boulder study.
According to CU-Boulder Professor Bradley Olwin, the loss of skeletal muscle mass and function as we age can lead to sarcopenia, a debilitating muscle-wasting condition that generally hits the elderly hardest. The new study indicates that altering two particular cell-signaling pathways independently in aged mice enhances muscle stem cell renewal and improves muscle regeneration.
One cell-signaling pathway the team identified, known as p38 MAPK, appears to be a major player in making or breaking the skeletal muscle stem cell, or satellite cell, renewal process in adult mice, said Olwin of the molecular, cellular and developmental biology department. Hyperactivation of the p38 MAPK cell-signaling pathway inhibits the renewal of muscle stem cells in aged mice, perhaps because of cellular stress and inflammatory responses acquired during the aging process.
The researchers knew that obliterating the p38 MAPK pathway in the stem cells of adult mice would block the renewal of satellite cells, said Olwin. But when the team only partially shut down the activity in the cell-signaling pathway by using a specific chemical inhibitor, the adult satellite cells showed significant renewal, he said. “We showed that the level of signaling from this cellular pathway is very important to the renewal of the satellite cells in adult mice, which was a very big surprise,” said Olwin.
A paper on the subject appeared online Feb. 16 in the journal Nature Medicine.
One reason the CU-Boulder study is important is that the results could lead to the use of low-dose inhibitors, perhaps anti-inflammatory compounds, to calm the activity in the p38 MAPK cell-signaling pathway in human muscle stem cells, said Olwin.
The CU-Boulder research team also identified a second cell-signaling pathway affecting skeletal muscle renewal – a receptor known as the fibroblast growth factor receptor-1, or FGFR-1. The researchers showed when the FGFR-1 receptor protein was turned on in specially bred lab mice, the renewal of satellite cells increased significantly. “We still don’t understand how that particular mechanism works,” he said.
Another major finding of the study was that while satellite cells transplanted from young mice to other young mice showed significant renewal for up to two years, those transplanted from old mice to young mice failed. “We found definitively that satellite cells from an aged mouse are not able to maintain the ability to replenish themselves,” Olwin said. “This is likely one of the contributors to loss of muscle mass during the aging process of humans.”
Co-authors included first author and CU-Boulder postdoctoral researcher Jennifer Bernet, former CU-Boulder graduate student John K. Hall, CU-Boulder undergraduate Thomas Carter, and CU-Boulder postdoctoral researchers Jason Doles and Kathleen Kelly-Tanaka. The National Institutes of Health and the Ellison Medical Foundation funded the study.
Olwin said skeletal muscle function and mass decline with age in humans beginning at roughly age 40. While there are a variety of muscle-wasting diseases — ranging from muscular dystrophy to Lou Gehrig’s disease — the condition known as sarcopenia can lead to severe muscle loss, frailty and eventual death and is leading to skyrocketing health care costs for the elderly. “If you live long enough, you’ll get it,” he said.
Olwin and his team worked closely on the research with a team from Stanford University led by Professor Helen Blau, which published a companion paper in the same issue of Nature Medicine. “We shared data with the Stanford team during the entire process and we all were very pleased with the study outcomes,” said Olwin. “This is how science should work.”
Mining big data for performance clues as a study guide
Jan 21st
forgetting with personalized content review
Computer software similar to that used by online retailers to recommend products to a shopper can help students remember the content they’ve studied, according to a new study by the University of Colorado Boulder.
The software, created by computer scientists at CU-Boulder’s Institute for Cognitive Science, works by tapping a database of past student performance to suggest what material an individual student most needs to review.
For example, the software might know that a student who forgot one particular concept but remembered another three weeks after initially learning them is likely to need to review a third concept six weeks after it was taught. When a student who fits that profile uses the software, the computer can pull up the most useful review questions.
“If you have two students with similar study histories for specific material, and one student couldn’t recall the answer, it’s a reasonable predictor that the other student won’t be able to either, especially when you take into consideration the different abilities of the two students,” said CU-Boulder Professor Michael Mozer, senior author of the study published in the journal Psychological Science.
The process of combing “big data” for performance clues is similar to strategies used by e-commerce sites, Mozer said.
“They know what you browsed and didn’t buy and what you browsed and bought,” Mozer said. “They measure your similarity to other people and use purchases of similar people to predict what you might want to buy. If you substitute ‘buying’ with ‘recalling,’ it’s the same thing.”
The program is rooted in theories that psychologists have developed about the nature of forgetting. Researchers know that knowledge—whether of facts, concepts or skills—slips away without review, and that spacing the review out over time is crucial to obtaining robust and durable memories.
Still, it’s uncommon for students to do the kind of extended review that favors long-term retention. Students typically review material that was presented only in the most recent unit or chapter—often in preparation for a quiz—without reviewing previous units or chapters at the same time.
This leads to rapid forgetting, even for the most motivated learners, Mozer said. For example, a recent study found that medical students forget roughly 25 to 35 percent of basic science knowledge after one year and more than 50 percent by the next year.
Over the last decade, Mozer has worked with University of California, San Diego, psychologist Harold Pashler, also a co-author of the new study, to create a computer model that could predict how spaced review affects memory. The new computer program described in the study is an effort to make practical use of that model.
Robert Lindsey, a CU-Boulder doctoral student collaborating with Mozer, built the personalized review program and then tested it in a middle school Spanish class.
For the study, Lindsey and Mozer divided the material students were learning into three groups. For material in a “massed” group, the students were drilled only on the current chapter. For material in a “generic-spaced” group, the students were drilled on the most recent two chapters. For material in a “personalized-spaced” group, the algorithm determined what material from the entire semester each student would benefit most from reviewing.
In a cumulative test taken a month after the semester’s end, personalized-spaced review boosted remembering by 16.5 percent over massed study and by 10 percent over generic-spaced review.
In a follow-up experiment, Mozer and his colleagues compared their personalized review program to a program that randomly quizzes students on all units that have been covered so far. Preliminary results show that the personalized program also outperforms random reviews of all past material.
So far, the program has been tested only in foreign language classes, but Mozer believes the program could be helpful for improving retention in a wide range of disciplines, including math skills.
It’s not necessary to have a prior database of student behavior to implement the personalized review program. Students can begin by using the program as a traditional review tool that asks random questions, and as students answer, the computer begins to search for patterns in the answers. “It doesn’t take long to get lots and lots of data,” Mozer said.
The research was funded by the National Science Foundation and the McDonnell Foundation.
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CU report: Colorado economy to stay warm next year
Dec 11th
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