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Record W2979484018 · doi:10.1002/jbm.a.36804

Directing hMSCs fate through geometrical cues and mimetics peptides

2019· article· en· W2979484018 on OpenAlexafffund
Laurence Padiolleau, Christel Chanseau, Stéphanie Durrieu, Cédric Ayela, Gaétan Laroche, Marie‐Christine Durrieu

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2019
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversité LavalHôpital Saint-François d'AssiseCentre hospitalier universitaire de Québec
FundersNatural Sciences and Engineering Research Council of CanadaAgence Nationale de la Recherche
KeywordsExtracellular matrixMesenchymal stem cellCell biologyStem cellCellular differentiationPeptideChemistryCellStem cell nicheNanotechnologyBiophysicsMaterials scienceBiologyProgenitor cellBiochemistryGene

Abstract

fetched live from OpenAlex

The native microenvironment of mesenchymal stem cells (hMSCs)-the extracellular matrix (ECM), is a complex and heterogenous environment structured at different scales. The present study aims at mimicking the hierarchical microorganization of proteins or growth factors within the ECM using the photolithography technique. Polyethylene terephthalate substrates were used as a model material to geometrically defined regions of RGD + BMP-2 or RDG + OGP mimetic peptides. These ECM-derived ligands are under research for regulation of mesenchymal stem cells osteogenic differentiation in a synergic manner. The hMSCs osteogenic differentiation was significantly affected by the spatial distribution of dually grafted peptides on surfaces, and hMSCs cells reacted differently according to the shape and size of peptide micropatterns. Our study demonstrates the presence of a strong interplay between peptide geometric cues and stem cell differentiation toward the osteoblastic lineage. These tethered surfaces provide valuable tools to investigate stem cell fate mechanisms regulated by multiple ECM cues, thereby contributing to the design of new biomaterials and improving hMSCs differentiation cues.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.203
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.329
Teacher spread0.284 · 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 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

Citations17
Published2019
Admission routes2
Has abstractyes

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Same venueJournal of Biomedical Materials Research Part ASame topicBone Tissue Engineering MaterialsFrench-language works237,207