Marijuana Removes Plaque-Forming Alzheimer’s Proteins From Brain Cells
Alzheimer’s disease is a type of dementia and affects memory, thinking and behavior and is on the rise. The number of Americans diagnosed with the disease is now over 5 million, with as many as 1 in 9 of those aged over 65 suffering with it.
Alzheimer’s disease is a type of dementia and affects memory, thinking and behavior and is on the rise. The number of Americans diagnosed with the disease is now over 5 million, with as many as 1 in 9 of those aged over 65 suffering with it. Although scientists and researchers are constantly searching for a cure for the disease, as of yet there is none and the pain and suffering for the patients continues.
However, there may now be a little glimmer of hope for sufferers and their loved ones in the form of Marijuana. Research carried out by scientists from the Salk Institute in California has shown that there are certain properties found in the drug that could potentially slow the progression of Alzheimer’s disease.
Cannabinoids remove plaque-forming Alzheimer’s proteins from brain cells
One of the key ingredients that were found to help was tetrahydrocannabinol (THC). This and other compounds found in marijuana worked together to slow the onset of the disease by reducing the production rate of beta-amyloid proteins which are one of the key contributors what are known to enhance progression in Alzheimer’s. Although the studies have not yet been carried out on humans, only on neurons grown in a lab, the team are happy with their success so far.
Being able to prove that cannabinoids do have an effect on inflammation and amyloid beta production in nerve cells is just the first step for the team of researchers. They will continue in their work and hope to see clinical trials come into practice next before we can finally accept and use marijuana as a treatment for Alzheimer’s.
Read More About The Study ; Salk.Edu
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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