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Record W2909148680 · doi:10.14440/jbm.2018.272

A rapid, high-throughput method for determining chronological lifespan in budding yeast

2018· article· en· W2909148680 on OpenAlexafffund
Zachery R. Belak, Troy A. A. Harkness, Christopher H. Eskiw

Bibliographic record

VenueJournal of Biological Methods · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCollege of Agriculture and Bioresources, University of Saskatchewan
KeywordsYeastBudding yeastSaccharomyces cerevisiaeBiologyBuddingIdentification (biology)Model organismGeneticsComputational biologyGeneBotany

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.152
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.069
GPT teacher head0.396
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

Quick stats

Citations18
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Biological MethodsSame topicGenetics, Aging, and Longevity in Model OrganismsFrench-language works237,207