The discovery of a unique ratio of metabolites in blood samples taken from early-stage Alzheimer's patients could be a critical new biomarker for early detection of the neurodegenerative disease.
The levels of two protein biomarkers, GFAP and UCH-L1 found in blood samples taken 24 hours after a traumatic brain injury can help to predict which patients will die and which will obtain a severe disability.
Six proteins in the blood can be used to assess a person's risk of developing cerebral small vessel disease (CSVD). CSVD has been linked to an increased risk of stroke and Alzheimer's disease.
A new light-based technique for measuring levels of the toxic protein that causes Huntington's disease (HD) has been used to demonstrate that the protein builds up gradually in blood cells.
A newly developed blood test screens for biomarkers associated with anxiety to determine a person's risk of developing the disorder as well as monitor the severity of symptoms in those with anxiety.
A new blood test can distinguish the severity of a person's depression and their risk for developing severe depression at a later point. The test can also determine if a person is at risk for developing bipolar disorder. Researchers say the blood test can also assist in tailoring individual options for therapeutic interventions.
A newly developed blood test can detect brain-derived tau (BD-tau), a biomarker of Alzheimer's disease neurodegeneration.
Researchers have developed a new algorithm that uses data based on metabolites in blood samples to successfully predict if a child is on the autism spectrum.
Researchers have developed a new blood test for brain-derived Tau that can follow and track the progression of Alzheimer's disease while excluding other dementias.
A newly developed immuno-infrared sensor allowed researchers to discover biomarkers for Alzheimer's disease in blood samples 17 years before clinical symptoms appeared. The sensory is able to detect the misfolding of amyloid beta.
New evidence confirms COVID-19 infection is the cause of the Kawasaki-like syndrome affecting children.
Combining artificial intelligence technology and blood samples, researchers were able to predict and explain the progression of Alzheimer's and Huntington's disease. The new algorithm was able to detect alterations in gene expression over decades from patients' blood samples.
