Quanterix biomarker technology used to measure neuronal injury in COVID-19 patients
Quanterix Corp., of Billerica, Mass., reported that quantitative testing using its serum neurofilament light chain (sNfL) assay and Simoa technology was used to show that COVID-19 may affect the neurological integrity of adult patients who experience a mild-to-moderate form of the virus. Researchers in Germany and Switzerland analyzed a cohort of 100 health care workers without known comorbidities, composed of 84 females and 16 males, following a COVID-19 outbreak in a major hospital. Subjects were stratified by infection status and age, and their sNfL concentrations were measured about 23 days after disease onset and again roughly 35 days later using the Simoa NF-light kit on the Simoa HD-X Analyzer. Notably, all positive patients reported mild-to-moderate symptoms with recovery within one to three weeks and showed no or only minor neurological symptoms. Results revealed that COVID-19 status was significantly associated with sNfL when controlling for age and gender. In patients with two sNfL measurements, sNfL levels were highly correlated. As NfL is a well-established marker for neuronal damage, elevated levels in serum suggest acute or chronic neuro-axonal damage as a result of COVID-19, even in mild or moderate forms. Published July 9, 2020, in the Journal of Neurology, these results support the utility of sNfL as a screening and monitoring tool for measuring neuronal injury throughout COVID-19 disease progression and recovery, as well as lays the foundation for the evaluation of potential long-term neurological impact following COVID-19 recovery.
AI could speed up and improve Alzheimer's diagnosis
Artificial intelligence (AI) could help to diagnose Alzheimer's faster and improve patient prognosis, a new study from the University of Sheffield has revealed. The research, published July 15, 2020, in the journal Nature Reviews Neurology, highlights how AI technologies can detect neurodegenerative disorders before progressive symptoms worsen. This can improve patients' chances of benefiting from successful disease-modifying treatment. "It is too early to talk about outcomes in terms of treatments, but, in this study, we examined how machine learning methods can be used to identify the best course of treatment for patients based on their disease progression or how it could be used to identify new therapeutic targets and drugs,” said first author Monika Myszczynska. Some of the technologies examined in the review included machine learning algorithms that can be trained to recognize changes caused by diseases in medical images, patient movement information, speech recordings or footage showing patient behavior. "Further research will now focus on the improvement of current diagnostic technologies, as well as a generation of new algorithms to make the use of AI in prognosis prediction and drug discovery a reality. AI feeds on data, therefore generation of international consortia and collaborations are the key to these future endeavors,” Myszczynska said.
Mental fatigue of MS linked to inefficient recruitment of neural resources
Researchers at the Kessler Foundation conducted a pilot study comparing the effects of mental fatigue on brain activation patterns in people with and without multiple sclerosis (MS). Their findings indicate significant differences between the two groups in their recruitment of neural resources in response to increased task demands. The study included 36 participants, 19 with MS, and 17 controls. Participants underwent functional magnetic resonance imaging (fMRI) while performing the Symbol Digit Modalities Test (SDMT), a standard cognitive test modified for use with fMRI. Changes in brain activity were recorded while the SDMT was administered under two conditions: high and low cognitive loads. "We found higher levels of fatigue and longer response times in the MS group," said Michelle Chen, postdoctoral fellow in the Center for Neuropsychology and Neuroscience Research at the Kessler Foundation. "With increasing mental fatigue, the control group showed increased activation of the anterior brain regions and faster speed of response, to meet the demands of the high load condition. The MS group did not show activation of these regions or an increase in processing speed, suggesting a less efficient response to the higher cognitive demands of the task." The findings are published in the August 2020 issue of the Journal of Neurology.