Bibliographic record
Abstract
Natural killer T (NKT) cells were first recognized more than two decades ago as a distinct lymphocyte lineage that regulates a broad range of immune responses. The activation of NKT cells paradoxically can lead to either suppression or stimulation of immune responses, but despite this uncertainty, many investigators are hopeful that immune therapies can be developed based on NKT cell modulation. To date, the biology of NKT cells is not well characterized and details of their development have only just started to emerge. It remains unclear how NKT cells migrate from the thymus to the peripheral organs and tissues, and in turn play such diverse roles from one type of immune response to another. Despite this, recent advances in intravital microscopy represent a powerful tool for revealing new insights into NKT cellular dynamics, their patrolling and immunoregulatory functions, which could not have been gained by non-microscopy means. Indeed, imaging has revolutionized the way we visualize with exceptional resolution the cells of the immune system. Instead of seeking a comprehensive review of NKT cell biology, this review attempts to highlight some of the recent studies that use in vivo imaging technologies to address NKT cell responses in a variety of animal models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".