Post-acute sequelae of COVID-19 is characterized by diminished peripheral CD8+β7 integrin+ T cells and anti-SARS-CoV-2 IgA response
Bibliographic record
Abstract
Abstract Several millions of individuals are estimated to develop post-acute sequelae SARS-CoV-2 condition (PASC) that persists for months after infection. Here we evaluate the immune response in convalescent individuals with PASC compared to convalescent asymptomatic and uninfected participants, six months following their COVID-19 diagnosis. Both convalescent asymptomatic and PASC cases are characterised by higher CD8+ T cell percentages, however, the proportion of blood CD8+ T cells expressing the mucosal homing receptor β7 is low in PASC patients. CD8 T cells show increased expression of PD-1, perforin and granzyme B in PASC, and the plasma levels of type I and type III (mucosal) interferons are elevated. The humoral response is characterized by higher levels of IgA against the N and S viral proteins, particularly in those individuals who had severe acute disease. Our results also show that consistently elevated levels of IL-6, IL-8/CXCL8 and IP-10/CXCL10 during acute disease increase the risk to develop PASC. In summary, our study indicates that PASC is defined by persisting immunological dysfunction as late as six months following SARS-CoV-2 infection, including alterations in mucosal immune parameters, redistribution of mucosal CD8+β7Integrin+ T cells and IgA, indicative of potential viral persistence and mucosal involvement in the etiopathology of PASC.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".