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There may be two distinct subtypes of multiple sclerosis, according to a new study led by scientists at University College London . The finding, if validated, could help doctors provide more specialized care for patients.
Machine learning models are trained to pick up subtle patterns that humans might miss.Blood Signals May Predict Multiple Sclerosis 7 Years Before Symptoms, a structure that connects the left and right hemispheres of the brain. This subtype seemed to be more aggressive, with patients developing brain lesions faster than others. The second subtype was named"late-sNfL", and it seemed to progress more slowly. For patients in this group, the first signs were shrinkage in the limbic cortex and the grey matter deep inside the brain. Levels of sNfL in their blood serum didn't start to rise until later. "By using an AI model combined with a highly available blood marker with MRI, we have been able to show two clear biological patterns of MS for the first time,""This will help clinicians understand where a person sits on the disease pathway and who may need closer monitoring or earlier, targeted treatment." A visual summary of the study, which used machine learning to analyze select features on MRI scans and blood test results to classify patients into two groups. , then tested on a further 445 patients who had been recently diagnosed with the disease. Neurofilaments are proteins that provide support for neurons throughout the central and peripheral nervous systems, and in healthy patients, they have a fairly slow turnover. ButUnfortunately, the difference is fairly subtle in blood serum, which makes it hard to use for diagnosis. MRI scans can also resolve patterns in theThe scientists behind the new study suggest that combining neurofilament levels with other data, such as MRI scans, makes those measures more useful. "By adding sNfL, an established indicator of neuroaxonal injury, we have advanced beyond the structural snapshot provided by MRI alone," the researchers Currently, MS is classified and treated based on symptoms and the progression of the disease, but this doesn't account for the underlying mechanisms. The researchers of the new study say that their combined technique could help doctors recommend
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