T helper type 2-polarized invariant natural killer T cells reduce disease severity in acute intra-abdominal sepsis
Bibliographic record
Abstract
Sepsis is characterized by a severe systemic inflammatory response to infection that is associated with high morbidity and mortality despite optimal care. Invariant natural killer T (iNK T) cells are potent regulatory lymphocytes that can produce pro- and/or anti-inflammatory cytokines, thus shaping the course and nature of immune responses; however, little is known about their role in sepsis. We demonstrate here that patients with sepsis/severe sepsis have significantly elevated proportions of iNK T cells in their peripheral blood (as a percentage of their circulating T cells) compared to non-septic patients. We therefore investigated the role of iNK T cells in a mouse model of intra-abdominal sepsis (IAS). Our data show that iNK T cells are pathogenic in IAS, and that T helper type 2 (Th2) polarization of iNK T cells using the synthetic glycolipid OCH significantly reduces mortality from IAS. This reduction in mortality is associated with the systemic elevation of the anti-inflammatory cytokine interleukin (IL)-13 and reduction of several proinflammatory cytokines within the spleen, notably interleukin (IL)-17. Finally, we show that treatment of sepsis with OCH in mice is accompanied by significantly reduced apoptosis of splenic T and B lymphocytes and macrophages, but not natural killer cells. We propose that modulation of iNK T cell responses towards a Th2 phenotype may be an effective therapeutic strategy in early sepsis.
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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) | 0.001 | 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 | 0.001 |
| 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".