T4 apoptosis in the acute phase of SARS-CoV-2 infection predicts long COVID
Bibliographic record
Abstract
Background: As about 10% of patients with COVID-19 present sequelae, it is important to better understand the physiopathology of so-called long COVID. Method: , quantified 19 soluble factors in their plasma and in the supernatant of their peripheral blood mononuclear cells, including inflammatory and anti-inflammatory cytokines and chemokines, Th1/Th2/Th17 cytokines, and endothelium activation markers. We also measured their T4, T8 and NK differentiation, activation, exhaustion and senescence, T cell apoptosis, and monocyte subpopulations by flow cytometry. We compared these markers between participants who developed long COVID or not one year later. Results: None of these markers was predictive for sequelae, except programmed T4 cell death. T4 lymphocytes from participants who later presented long COVID were more apoptotic in culture than those of sequelae-free participants at Month 12 (36.9 ± 14.7 vs. 24.2 ± 9.0%, p = 0.016). Conclusions: Our observation raises the hypothesis that T4 cell death during the acute phase of SARS-CoV-2 infection might pave the way for long COVID. Mechanistically, T4 lymphopenia might favor phenomena that could cause sequelae, including SARS-CoV-2 persistence, reactivation of other viruses, autoimmunity and immune dysregulation. In this scenario, inhibiting T cell apoptosis, for instance, by caspase inhibitors, could prevent long COVID.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".