MétaCan
Menu
Back to cohort
Record W2036540051 · doi:10.4161/15476278.2014.970089

Age associated communication between cells and matrix: a potential impact on stem cell-based tissue regeneration strategies

2014· review· en· W2036540051 on OpenAlexaff
Kevin Lynch, Ming Pei

Bibliographic record

VenueOrganogenesis · 2014
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsCanadian Society for Exercise Physiology
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesAO Foundation
KeywordsDecellularizationExtracellular matrixCell biologyMesenchymal stem cellStem cellRegeneration (biology)Regenerative medicineBiologyTissue engineeringImmunologyChondrogenesisChemistryGenetics

Abstract

fetched live from OpenAlex

A recent paper demonstrated that decellularized extracellular matrix (DECM) deposited by synovium-derived stem cells (SDSCs), especially from fetal donors, could rejuvenate human adult SDSCs in both proliferation and chondrogenic potential, in which expanded cells and corresponding culture substrate (such as DECM) were found to share a mutual reaction in both elasticity and protein profiles (see ref. (1) ). It seems that young DECM may assist in the development of culture strategies that optimize proliferation and maintain "stemness" of mesenchymal stem cells (MSCs), helping to overcome one of the primary difficulties in MSC-based regenerative therapies. In this paper, the effects of age on the proliferative capacity and differentiation potential of MSCs are reviewed, along with the ability of DECM from young cells to rejuvenate old cells. In an effort to highlight some of the potential molecular mechanisms responsible for this phenomenon, we discuss age-related changes to extracellular matrix (ECM)'s physical properties and chemical composition.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.025
GPT teacher head0.324
Teacher spread0.300 · 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 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

Citations80
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueOrganogenesisSame topicTissue Engineering and Regenerative MedicineFrench-language works237,207