Mechanisms that Link Chronological Aging to Cellular Quiescence in Budding Yeast
Bibliographic record
Abstract
After Saccharomyces cerevisiae cells cultured in a medium with glucose consume glucose, the sub-populations of quiescent and non-quiescent cells develop in the budding yeast culture. An age-related chronology of quiescent and non-quiescent yeast cells within this culture is discussed here. We also describe various hallmarks of quiescent and non-quiescent yeast cells. A complex aging-associated program underlies cellular quiescence in budding yeast. This quiescence program includes a cascade of consecutive cellular events orchestrated by an intricate signaling network. We examine here how caloric restriction, a low-calorie diet that extends lifespan and healthspan in yeast and other eukaryotes, influences the cellular quiescence program in S. cerevisiae. One of the main objectives of this review is to stimulate an exploration of the mechanisms that link cellular quiescence to chronological aging of budding yeast. Yeast chronological aging is defined by the length of time during which a yeast cell remains viable after its growth and division are arrested, and it becomes quiescent. We propose a hypothesis on how caloric restriction can slow chronological aging of S. cerevisiae by altering the chronology and properties of quiescent cells. Our hypothesis posits that caloric restriction delays yeast chronological aging by targeting four different processes within quiescent cells.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".