Bibliographic record
Abstract
Abstract Invariant Natural Killer T (iNKT) cells are versatile innate T cells that influence immune responses associated with infection, cancer, inflammation and autoimmunity, notably through the production of a wide array of cytokines and chemokines. iNKT cells respond to lipid antigens presented by the MHC Class Ib molecule CD1d. iNKT T cell receptors (TCRs) have limited diversity, due to exclusive usage of a canonical Vα14-Jα18 TCRα chain and a limited set of TCR Vβ gene segments, but with extensive CDR3β diversity. Recent structural and mutational studies revealed that CD1d-lipid-TCR interactions differ markedly from classical pMHC-TCR interactions. Remarkably, docking onto CD1d-lipid complexes is unaffected by the nature of the lipid involved, nor TCRβ composition (Vβ gene segment usage or CDR3β diversity). Rather, we and others have demonstrated that TCRβ composition modulates TCR avidity for CD1d-lipids. Here, we have generated TCR retrogenic mice using TCRs with various avidities, and found a strong correlation between the frequency and numbers of iNKT cells that develop in vivo and TCR avidity for CD1d-lipids. In addition, TCR strength impacts on iNKT cell maturation as well as cytokine production upon immunization with the prototypical lipid αGalCer. In summary, our results highlight a crucial role for iNKT TCR strength on the selection and functional programing of iNKT cells.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".