Dissecting the "end game": clinical relevance, molecular mechanisms and laboratory assessment of apoptosis.
Bibliographic record
Abstract
BACKGROUND: Apoptosis, the process of cell death, is a complex subject. In this review we highlight recent developments in the regulation and dysregulation of apoptosis in health and disease and summarize common laboratory techniques used to assess the process. METHODS: We accessed MEDLINE publications within the past 10 years, that reported on the clinical relevance, molecular mechanisms and laboratory assessment of apoptosis. PRINCIPAL FINDINGS: Apoptosis is a physiological event essential for normal biologic processes at all stages of life, including embryogenesis, tissue remodelling, cell turnover, reproduction and regulation of immune responses. Dysregulation of apoptosis, either excessive or inadequate, features prominently in the pathophysiology of many diseases, ranging from congenital anomalies to degenerative disorders, ischemic and reperfusion injury, chronic inflammatory or autoimmune diseases, certain infections and malignant disease. CONCLUSION: Improved understanding of the molecular mechanisms underlying apoptosis and its laboratory assessment is critical for reversing the pathophysiological processes associated with dysregulation of apoptosis.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".