Transfer of IgG from Long COVID patients induces symptomology in mice
Bibliographic record
Abstract
Abstract SARS-CoV-2 infections worldwide led to a surge in cases of Long COVID, a post-infectious syndrome. It has been hypothesized that autoantibodies play a crucial role in the development of Long COVID and other syndromes, such as fibromyalgia and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). In this study, we tested this hypothesis by passively transferring total IgG from Long COVID patients to mice. Using Glial Fibrillary Acidic Protein (GFAP) and type-I interferon expression, we stratified patients into three Long COVID subgroups, each with unique plasma proteome signatures. Remarkably, IgG transfer from the two subgroups, which are characterized by higher plasma levels of neuronal proteins and leukocyte activation markers, induced pronounced and persistent sensory hypersensitivity with distinct kinetics. Conversely, IgG transfer from the third subgroup, which are characterized by enriched skeletal and cardiac muscle proteome profiles, reduced locomotor activity in mice without affecting their motor coordination. These findings demonstrate that transfer of IgG from Long COVID patients to mice replicates disease symptoms, underscoring IgG’s causative role in Long COVID pathogenesis. This work proposes a murine model that mirrors Long COVID’s pathophysiological mechanisms, which may be used as a tool for screening and developing targeted therapeutics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".