Generally, the body’s protein levels are stable. But the researchers found a dramatic difference in the levels of some proteins up to six weeks after infection, suggesting disruption of several important biological processes.
Using an artificial intelligence (AI) algorithm, they identified a “signature” of the abundance of various proteins that successfully predicted whether a person would report ongoing symptoms a year after infection.
The researchers say that if these findings are replicated in a larger, independent group of patients, a test could potentially be offered alongside the polymerase chain reaction (PCR) test that could predict people’s likelihood of developing long-term Covid disease.
The lead author of the study, Dr Gaby Captur (MRC Unit for Lifelong Health and Aging at UCL) said: “Our research shows that even mild or asymptomatic Covid-19 disrupts the profile of proteins in our blood plasma. This means that even mild Covid-19 affects normal biological processes dramatically way, at least six weeks after infection after.
“Our tool that predicts long Covid still needs to be validated in an independent, larger group of patients. However, using our approach, a test that predicts long Covid at first infection could be deployed quickly and cost-effectively. .
“The analysis method we use is readily available in hospitals and is very efficient, meaning it can analyze thousands of samples in an afternoon.”
Senior author Dr Wendy Heywood (UCL Great Ormond Street Institute of Child Health and Great Ormond Street Hospital) said: “If we can identify people who are likely to develop prolonged Covid, this opens the door to trying treatments such as antivirals at this earlier, early stage to see if it can reduce subsequent prolonged The risk of Covid.”
For the study, researchers analyzed blood plasma samples taken every week for six weeks in the spring of 2020 from 54 healthcare workers with PCR, or antibody-confirmed infection, and compared them to samples taken during the same period from 102 healthcare workers who did not have the infection. .
They used targeted mass spectrometry, a form of analysis that is extremely sensitive to small changes in the amount of proteins in blood plasma, to look at how Covid-19 affected these proteins over a six-week period.
Among those infected with SARS-CoV-2, the researchers found abnormally high levels of 12 of the 91 proteins studied, and that the degree of abnormality is tracked by the severity of symptoms.
The research team found that abnormal levels of the 20 proteins studied at the time of the first infection predicted persistent symptoms a year later. Most of these proteins were associated with anticoagulant (anti-clotting) and anti-inflammatory processes.
A machine learning algorithm trained on the participants’ protein profiles was able to distinguish all 11 healthcare workers who reported at least one persistent symptom during the year from infected healthcare workers who did not report persistent symptoms after one year. Another machine learning tool was used to estimate the probability of error, and a potential error rate of 6% was suggested for this method.
HT
Source: ANI
Source: The Nordic Page