A causal link between autoantibodies and neurological symptoms in long COVID 2133
Bibliographic record
Abstract
Abstract Description Acute SARS-CoV-2 infection triggers the generation of diverse and functional autoantibodies (AABs), even after mild cases. Persistently elevated autoantibodies have been found in some individuals with long COVID (LC). Using a > 21,000 human protein array, we identified diverse AAB targets in LC patients that correlated with their symptoms. Elevated AABs to proteins in the nervous system were found in LC patients with neurocognitive and neurological symptoms. Purified Immunoglobulin G (IgG) samples from these individuals reacted with human pons tissue and were cross-reactive with mouse sciatic nerves, spinal cord, and meninges. Antibody reactivity to sciatic nerves and meninges correlated with patient-reported headache and disorientation. Passive transfer of IgG from patients to mice led to increased sensitivity and pain, mirroring patient-reported symptoms. Similarly, mice injected with IgG showed loss of balance and coordination, reflecting donor-reporte Funding Sources This work was supported by the Else Kröner Fresenius Prize for Medical Research 2023, grants from the National Institute of Allergy and Infectious Diseases (R01AI157488 to A.I.), the Howard Hughes Medical Institute (A.I.). K.S.G.S. receive research support from Pew Latin American Fellowship. Topic Categories Neuroimmunology (NEUR)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".