Senescence and Apoptosis: Architects of Mammalian Development
Bibliographic record
Abstract
Mammalian development involves an exquisite choreography of cell division, differentiation, locomotion, programmed cell death, and senescence that directs the transformation of a single cell zygote to a mature organism containing on the order of 40 trillion cells in humans. How a single totipotent zygote undergoes the rapid stages of embryonic development to form over 200 different cell types is complex in the extreme and remains the focus of active research. Processes such as programmed cell death or apoptosis has long been known to occur during development to help sculpt organs and tissue systems. Other processes such as cellular senescence, long thought to only occur in pathologic states such as aging and tumorigenesis have been recently reported to play a vital role in development. In this review, we focus on apoptosis and senescence; the former as an integral mechanism that plays a critical role not only in mature organisms, but that is also essential in shaping mammalian development. The latter as a well-defined feature of aging for which some reports indicate a function in development. We will dissect the dual roles of major gene families, pathways such as Hox, Rb, p53, and epigenetic regulators such as the ING proteins in both early and the late stages and how they play antagonistic roles by increasing fitness and decreasing mortality early in life but contribute to deleterious effects and pathologies later in life.
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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 | 0.001 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".