Invariant Natural Killer T Cells: Boon or Bane in Immunity to Intracellular Bacterial Infections?
Bibliographic record
Abstract
Invariant natural killer T (iNKT) cells represent a specialized subset of innate lymphocytes that recognize lipid and glycolipid antigens presented to them by nonclassical MHC-I CD1d molecules and are able to rapidly secrete copious amounts of a variety of cytokines. iNKT cells possess the ability to modulate innate as well as adaptive immune responses against various pathogens. Intracellular bacteria are one of the most clinically significant human pathogens that effectively evade the immune system and cause a myriad of diseases of public health concern globally. Emerging evidence suggests that iNKT cells can confer immunity to intracellular bacteria but also inflict pathology in certain cases. We summarize the current knowledge on the contribution of iNKT cells in the host defense against intracellular bacterial infections, with a focus on the underlying mechanisms by which these cells induce protective or pathogenic reactions including the pathways of direct action (acting on infected cells) and indirect action (modulating dendritic, NK and T cells). The rational exploitation of iNKT cells for prophylactic and therapeutic purposes awaits a profound understanding of their functional biology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".