The role of serum brain injury biomarkers in individuals with a mild-to-moderate COVID infection and Long-COVID - results from the prospective population-based COVI-GAPP study
Bibliographic record
Abstract
Abstract Background During and after mild (no hospitalization) or moderate (hospitalization without ICU) SARS-CoV-2 infections, a wide range of symptoms, including neurological disorders have been reported. It is, however, unknown if these neurological symptoms are associated with brain injury and whether brain injury and related symptoms also emerge in patients suffering from Long-COVID. Neuronal biomarkers such as serum neurofilament light chain and glial fibrillary acidic protein can be used to elucidate neuro-axonal and astroglial injuries. We therefore investigated whether these biomarkers are associated with the COVID-19 infection status (mild-to-moderate), the associated symptoms and Long-COVID. Methods From 146 individuals of the general population with a post-acute, mild-to-moderate SARS-CoV-2 infection, serum neurofilament light chain (sNfL; marker of intra-axonal neuronal injury) and serum glial fibrillary acidic protein (sGFAP; marker of astrocytic activation/injury) were measured. Samples were taken before, during and after (five and ten months) a SARS-CoV-2 infection. Individual symptoms and Long-COVID status were assessed using questionnaires. Results Neurological symptoms were described for individuals after a mild and moderate COVID-19 infection, however, serum markers of brain injury (sNfL/sGFAP) did not change after an infection (sNfL: P = 0.74; sGFAP: P = 0.24) and were not associated with headache ( P = 0.51), fatigue ( P = 0.93), anosmia ( P = 0.77) and ageusia ( P = 0.47). In participants with Long-COVID, sGFAP ( P = 0.038), but not sNfL ( P = 0.58) significantly increased but was not associated with neurological symptoms. Conclusion Neurological symptoms in individuals after a mild-to-moderate SARS-CoV-2 infection with and without Long-COVID were not associated with brain injury, although there was some astroglial injury observed in Long-COVID patients. Funding The COVI-GAPP study received grants from the Innovative Medicines Initiative (IMI grant agreement number 101005177), the Princely House of Liechtenstein, the government of the Principality of Liechtenstein, and the Hanela Foundation (Switzerland). None of the funders played a role in the study design, data collection, data analysis, data interpretation, writing of the report, or decision to publish.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".