A rapid, high-throughput method for determining chronological lifespan in budding yeast
Bibliographic record
Abstract
is a major model system in the study of aging. Like metazoans, yeast lifespan is extended by caloric restriction and treatment with pharmacological agents which extend lifespan. A major workhorse of aging research in budding yeast is the chronological lifespan assay. Traditionally, chronological lifespan assays consist of taking regular samples of aging yeast cultures, plating out aliquots on agar, and counting the resulting colonies. This method, while highly reliable, is labor-intensive and expensive in terms of materials consumed. Here, we report a novel MTT-based method for assessing chronological lifespan in yeast. We show that this method is equal to the colony counting method in its rigorous and reliable measurement of lifespan extension in yeast as a result of caloric restriction, and is able to distinguish known long-lived and short-lived yeast strains. We have further developed this method into a high-throughput assay that allows rapid screening of potential anti-aging compounds as well as yeast strains with altered lifespan. Application of this method permits the rapid identification of anti-aging activities in yeast and may facilitate identification of materials with therapeutic potential for higher animals and, most importantly, humans.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".