Apoptosis in health, disease, and therapy: overview and methodology
Bibliographic record
Abstract
Introduction: life cannot exist without cellular death Apoptosis, or programmed cell death, is the mechanism by which most cells die both physiologically and pathologically. The realization in the mid 1980s that cells die by an active, genetically defined process changed not only our views on cellular life but led to a whole new discipline of biologic study with significant implications for medicine (Thompson, 1995; Robertson et al ., 2002). Apoptosis research has advanced our understanding of a basic cellular process, shed insight into many diseases, and is poised to affect the future practice of medicine by the introduction of therapies targeting this cell death process. In this book, the term “apoptosis” is used synonymously, for right or wrong, with programmed cell death (PCD). While PCD may be a more appropriate term, encompassing all forms of active physiological cell death, apoptosis, which is defined morphologically and biochemically, is used here for historical purposes (Lockshin and Zakeri, 2002; Melino, 2002; Sloviter, 2002). The original “anatomical” characteristics of apoptosis were noted in the nineteenth century (reviewed in Clarke and Clarke, 1996; Rich et al ., 1999). However, it was not until publications in 1951 and in the 1960s described developmental cell death or “shrinkage necrosis” that the PCD concept was recognized, re-introduced, and formalized (Lockshin and Zakeri, 2001; Kerr, 2002; Vaux, 2002). The term “apoptosis” was coined in 1972, referring to this morphologically defined form of cell death (Kerr et al ., 1972).
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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 | 0.000 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".