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Record W2810685948 · doi:10.1387/ijdb.180041pm

New mechanisms driving muscle stem cell regenerative decline with aging

2018· review· en· W2810685948 on OpenAlexaff
Pedro Sousa‐Victor, Laura García‐Prat, Pura Muñoz‐Cánoves

Bibliographic record

VenueThe International Journal of Developmental Biology · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
FundersCentro Nacional de Investigaciones CardiovascularesFondation pour la Recherche MédicaleE-RareGlenn Foundation for Medical Research
KeywordsBiologyStem cellCell biologyRegenerative medicineSenescenceSkeletal muscleHomeostasisAdult stem cellRegeneration (biology)Stem cell theory of agingNeuroscienceCellular differentiationAnatomyProgenitor cellStem cell factorGenetics

Abstract

fetched live from OpenAlex

Stem cells must preserve their function in order to sustain organ and tissue formation, homeostasis and repair. Adult stem cells, particularly those resident in tissues with little turnover, remain quiescent for most of their life, activating only in response to regenerative demands. Among the best studied long-lived quiescent stem cells are skeletal muscle stem cells, which are fully equipped to sustain repair in response to tissue trauma. Recent evidence indicates that the preservation of muscle stem-cell quiescence and regenerative capacity depends on intracellular networks linking metabolism and protein homeostasis. Here, we review recent research into how these networks control stem cell function and how their dysregulation contributes to aging, with a particular focus on senescence entry in extreme old age. We also discuss the implications of these new findings for anti-aging research in muscle stem-cell-based regenerative medicine.

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)0.000
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 score0.969
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.020
GPT teacher head0.293
Teacher spread0.273 · 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

Citations21
Published2018
Admission routes1
Has abstractyes

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