Low perforin expression in CD8+ T lymphocytes during the acute phase of severe SARS-CoV-2 infection predicts long COVID
Bibliographic record
Abstract
T cell cytotoxicity plays a major role in antiviral immunity. Anti-SARS-CoV-2 immunity may determine acute disease severity, but also the potential persistence of symptoms (long COVID). We therefore measured the expression of perforin, a cytotoxic mediator, in T cells of patients recently hospitalized for SARS-CoV-2 infection. We recruited 54 volunteers confirmed as being SARS-CoV-2-infected by RT-PCR and admitted to Intensive Care Units (ICUs) or non-ICU, and 29 age- and sex-matched healthy controls (HCs). Amounts of intracellular perforin and granzyme-B, as well as cell surface expression of the degranulation marker CD107A were determined by flow cytometry. The levels of 15 cytokines in plasma were measured by Luminex. The frequency of perforin-positive T4 cells and T8 cells was higher in patients than in HCs (9.9 ± 10.1% versus 4.6 ± 6.4%, p = 0.006 and 46.7 ± 20.6% vs 33.3 ± 18.8%, p = 0.004, respectively). Perforin expression was neither correlated with clinical and biological markers of disease severity nor predictive of death. By contrast, the percentage of perforin-positive T8 cells in the acute phase of the disease predicted the onset of long COVID one year later. A low T8 cytotoxicity in the first days of SARS-CoV-2 infection might favor virus replication and persistence, autoimmunity, and/or reactivation of other viruses such as Epstein-Barr virus or cytomegalovirus, paving the way for long COVID. Under this hypothesis, boosting T cell cytotoxicity during the acute phase of the infection could prevent delayed sequelae.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".