Longitudinal analysis of mucosa‐associated invariant T cells in sepsis reveals their early numerical decline with prognostic implications and a progressive loss of antimicrobial functions
Bibliographic record
Abstract
Abstract Sepsis‐elicited immunosuppression elevates the risk of secondary infections. We used a clinically relevant mouse model and serial peripheral blood samples from patients to assess the antimicrobial activities of mucosa‐associated invariant T (MAIT) cells in sepsis. Hepatic and splenic MAIT cells from B6‐MAITCAST mice displayed increased CD69 expression and a robust interferon‐γ (IFNγ) production capacity shortly after sublethal cecal ligation and puncture, but not at a late timepoint. Peripheral blood MAIT cell frequencies were reduced in septic patients at the time of intensive care unit (ICU) admission, and more dramatically so among nonsurvivors, suggesting the predictive usefulness of early MAIT cell enumeration. In addition, at ICU admission, MAIT cells from sepsis survivors launched stronger IFNγ responses to several bacterial species compared with those from patients who subsequently died of sepsis. Of note, while low human leukocyte antigen (HLA)‐DR+ monocyte frequencies, widely regarded as a surrogate indicator of sepsis‐induced immunosuppression, were gradually corrected, the numerical insufficiency of MAIT cells was not resolved over time, and their CD69 expression continued to decline. MAIT cell responses to bacterial pathogens, a major histocompatibility complex–related protein 1 (MR1) ligand, and interleukin (IL)‐12 and IL‐18 were also progressively lost during sepsis and did not recover by the time of ICU/hospital discharge. We propose that MAIT cell dysfunctions contribute to post‐sepsis immunosuppression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".