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Record W3016329102 · doi:10.1016/j.mad.2020.111246

Muscle stem cell aging: identifying ways to induce tissue rejuvenation

2020· article· en· W3016329102 on OpenAlexfundno aff
Pedro Sousa‐Victor, Joana Neves, Pura Muñoz‐Cánoves

Bibliographic record

VenueMechanisms of Ageing and Development · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsnot available
FundersMedia Development Authority - SingaporeCentro Nacional de Investigaciones CardiovascularesMinisterio de Ciencia, Innovación y UniversidadesAdvanced Foods and Materials Canada
KeywordsRejuvenationStem cellSenescenceSkin AgingBiologyAccelerated agingRegeneration (biology)AgeingCell biologyGerontologyMedicineChemistryGeneticsDermatology

Abstract

fetched live from OpenAlex

Aging is characterized by the functional and regenerative decline of tissues and organs. This regenerative decline is a consequence of the numerical and functional loss of adult stem cells, which are the corner stone of tissue homeostasis and repair. A palpable example of this decline is provided by skeletal muscle, a specialized tissue composed of postmitotic myofibers that contract to generate force. Skeletal muscle stem cells (satellite cells) are long-lived and support muscle regeneration throughout life, but at advanced age they fail for largely undefined reasons. Here, we discuss recent advances in the understanding of how satellite cells integrate diverse intrinsic and extrinsic processes to ensure optimal homeostatic function and how this integration is perturbed during aging, causing regenerative failure. With this increased understanding, it is now feasible to design and test interventions that delay satellite cell aging. We discuss the exciting new therapeutic potential of integrating and combining distinct anti-aging strategies for 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.244
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations15
Published2020
Admission routes1
Has abstractno

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