Bibliographic record
Abstract
A substantial number of patients recovering from acute SARS-CoV-2 infection present serious lingering symptoms, often referred to as long COVID (LC). However, a subset of these patients exhibits the most debilitating symptoms characterized by ongoing myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS). We specifically identified and studied ME/CFS patients from two independent LC cohorts, at least 12 months post the onset of acute disease, and compared them to the recovered group (R). ME/CFS patients had relatively increased neutrophils and monocytes but reduced lymphocytes. Selective T cell exhaustion with reduced naïve but increased terminal effector T cells was observed in these patients. LC was associated with elevated levels of plasma pro-inflammatory cytokines, chemokines, Galectin-9 (Gal-9), and artemin (ARTN). A defined threshold of Gal-9 and ARTN concentrations had a strong association with LC. The expansion of immunosuppressive CD71<sup>+</sup> erythroid cells (CECs) was noted. These cells may modulate the immune response and contribute to increased ARTN concentration, which correlated with pain and cognitive impairment. Serology revealed an elevation in a variety of autoantibodies in LC. Intriguingly, we found that the frequency of 2B4<sup>+</sup>CD160<sup>+</sup> and TIM3<sup>+</sup>CD160<sup>+</sup> CD8<sup>+</sup> T cells completely separated LC patients from the R group. Our further analyses using a multiple regression model revealed that the elevated frequency/levels of CD4 terminal effector, ARTN, CEC, Gal-9, CD8 terminal effector, and MCP1 but lower frequency/levels of TGF-β and MAIT cells can distinguish LC from the R group. Our findings provide a new paradigm in the pathogenesis of ME/CFS to identify strategies for its prevention and treatment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.016 | 0.002 |
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".