Participatory role of natural killer and natural killer T cells in atherosclerosis: lessons learned from in vivo mouse studiesThis paper is one of a selection of papers published in this Special Issue, entitled Young Investigator's Forum.
Bibliographic record
Abstract
Atherosclerosis is a multifactor, highly complex disease with numerous aetiologies that work synergistically to promote lesion development. One of the emerging components that drive the development of both early- and late-stage atherosclerotic lesions is the participation of both the innate and acquired immune systems. In both humans and animal models of atherosclerosis, the most prominent cells that infiltrate evolving lesions are macrophages and T lymphocytes. The functional loss of either of these cell types reduces the extent of atherosclerosis in mice that were rendered susceptible to the disease by deficiency of either apolipoprotein E or the LDL (low density lipoprotein) receptor. In addition to these major immune cell participants, a number of less prominent leukocyte populations that can modulate the atherogenic process are also involved. This review will focus on the participatory role of two "less prominent" immune components, namely natural killer (NK) cells and natural killer T (NKT) cells. Although this review will highlight the fact that both NK and NKT cells are not sufficient for causing the disease, the roles played by both these cells types are becoming increasingly important in understanding the complexity of this disease process.
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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) | 0.002 | 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 | 0.000 |
| Research integrity | 0.001 | 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".