MétaCan
Menu
Back to cohort
Record W4367397140 · doi:10.1016/j.arr.2023.101941

Biological resilience and aging: Activation of stress response pathways contributes to lifespan extension

2023· review· en· W4367397140 on OpenAlexafffund
Sonja K. Soo, Zenith D. Rudich, Bokang Ko, Alibek Moldakozhayev, Abdelrahman AlOkda, Jeremy M. Van Raamsdonk

Bibliographic record

VenueAgeing Research Reviews · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsMcGill University Health CentreMcGill University
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsLongevityBiologyModel organismOrganismMutantStressorMechanism (biology)Cellular stress responseGeneticsFight-or-flight responsePhenotypeCell biologyNeuroscienceGene

Abstract

fetched live from OpenAlex

While aging was traditionally viewed as a stochastic process of damage accumulation, it is now clear that aging is strongly influenced by genetics. The identification and characterization of long-lived genetic mutants in model organisms has provided insights into the genetic pathways and molecular mechanisms involved in extending longevity. Long-lived genetic mutants exhibit activation of multiple stress response pathways leading to enhanced resistance to exogenous stressors. As a result, lifespan exhibits a significant, positive correlation with resistance to stress. Disruption of stress response pathways inhibits lifespan extension in multiple long-lived mutants representing different pathways of lifespan extension and can also reduce the lifespan of wild-type animals. Combined, this suggests that activation of stress response pathways is a key mechanism by which long-lived mutants achieve their extended longevity and that many of these pathways are also required for normal lifespan. These results highlight an important role for stress response pathways in determining the lifespan of an organism.

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)0.006
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.222
GPT teacher head0.427
Teacher spread0.205 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations51
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueAgeing Research ReviewsSame topicGenetics, Aging, and Longevity in Model OrganismsFrench-language works237,207