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Record W2765885344 · doi:10.1083/jcb.201707168

Regulation of reproduction and longevity by nutrient-sensing pathways

2017· review· en· W2765885344 on OpenAlexfundno aff
Nicole M. Templeman, Coleen T. Murphy

Bibliographic record

VenueThe Journal of Cell Biology · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesCanadian Institutes of Health ResearchGlenn Research CenterNational Institutes of HealthPrinceton UniversitySimons FoundationHoward Hughes Medical Institute
KeywordsNutrient sensingAMPKBiologySomatic cellLongevityPI3K/AKT/mTOR pathwaySignal transductionCell biologyTOR signalingMetabolic pathwayInsulin receptorKinaseProtein kinase AInsulinGeneticsMetabolismEndocrinologyInsulin resistanceGene

Abstract

fetched live from OpenAlex

Nutrients are necessary for life, as they are a crucial requirement for biological processes including reproduction, somatic growth, and tissue maintenance. Therefore, signaling systems involved in detecting and interpreting nutrient or energy levels-most notably, the insulin/insulin-like growth factor 1 (IGF-1) signaling pathway, mechanistic target of rapamycin (mTOR), and adenosine monophosphate-activated protein kinase (AMPK)-play important roles in regulating physiological decisions to reproduce, grow, and age. In this review, we discuss the connections between reproductive senescence and somatic aging and give an overview of the involvement of nutrient-sensing pathways in controlling both reproductive function and lifespan. Although the molecular mechanisms that affect these processes can be influenced by distinct tissue-, temporal-, and pathway-specific signaling events, the progression of reproductive aging and somatic aging is systemically coordinated by integrated nutrient-sensing signaling pathways regulating somatic tissue maintenance in conjunction with reproductive capacity.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.895
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.043
GPT teacher head0.293
Teacher spread0.250 · 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 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

Citations176
Published2017
Admission routes1
Has abstractyes

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