Cytotoxic lymphocytes, apoptosis, and autoimmunity
Bibliographic record
Abstract
Introduction Cytotoxic lymphocytes – you cannot live without them, but sometimes you have trouble living with them.the Jeckyl and Hyde character of these cells relates to their ability to induce death in target cells. On the one hand, they can recognize and destroy pathogenic cells, such as those infected with viruses, but, on the other hand, they can also mistakenly turn their attention to normal cells, resulting in autoimmunity. Lymphocytes can kill, either through direct cell contact or via the secretion of cytokines, and, in the case of B lymphocytes, antibodies. These secreted proteins are important in killing and autoimmune disorders. They can act directly but often function via the activation and/or recruitment of lytic and inflammatory effector cells. Most of the discussion in this chapter, however, will focus on the pathways that involve close apposition of effector and target cells. A knowledge of the molecular killing mechanisms used by cytotoxic lymphocytes may allow us to develop novel strategies to either curb or amplify target cell destruction. The current models for apoptosis induced by cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells will be outlined, and some insights into which pathways are used in autoimmune disorders will be provided. Over the last few years, it has become clear that CTLs and NK cells can kill via two distinct pathways. The first to be described involves the exocytosis of lytic proteins from dense granules in the cytoplasm of the effector cells toward the targets (Henkart, 1985).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".