Autoimmunity and Apoptosis - Therapeutic Implications
Bibliographic record
Abstract
Acquisition of a complex immune system during evolution provided organisms with the most effective defense mechanism against "foreign" or "non-self" invaders. This efficient protection against pathogens, however, has been achieved at the expense of a higher risk for "self"-directed reaction or autoimmunity. Establishment of self-tolerance and homeostasis in the immune system is regulated at different physiological stages of immune cells development. The breakdown in discrimination between "self" and "non-self" causes an aberrant immune response against autoantigens that promote damage to the "self" cells and tissue(s), resulting in various autoimmune phenotypes. Whereas activation and clonal proliferation of autoreactive T- and B- lymphocytes underlies the pathogenesis of autoimmune diseases, the mechanism by which self-tolerance is lost and autoimmune responses are induced is not clear yet. Autoimmunity is a multi-step process that occurs as a consequence of complex interaction between genetic susceptibility and non-genetic factors. Programmed cell death, as a key mechanism to regulate immune system function, has a crucial influence on both the selection process of immune cells and the maintenance of this immune tolerance in peripheral repertoire. Thus, defects in apoptotic death pathways may contribute to the development of autoimmune response in susceptible individuals in certain conditions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".