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Record W4223917477 · doi:10.1016/j.ocarto.2022.100263

TRPV4 activation enhances compressive properties and glycosaminoglycan deposition of equine neocartilage sheets

2022· article· en· W4223917477 on OpenAlexafffund
Cristóbal López-Jiménez, Loraine L. Y. Chiu, Stephen D. Waldman, Farshid Guilak, Thomas G. Koch

Bibliographic record

VenueOsteoarthritis and Cartilage Open · 2022
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsToronto Metropolitan UniversitySt. Michael's HospitalUniversity of Guelph
FundersInstitute of Population and Public HealthNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y Tecnología
KeywordsChondrogenesisChemistryTRPV4Mesenchymal stem cellBiomedical engineeringCell biologyBiophysicsReceptorTransient receptor potential channelBiologyBiochemistryMedicine

Abstract

fetched live from OpenAlex

Objective: To evaluate the effect of Transient Receptor Potential Vanilloid 4 (TRPV4) cation channel modulation on mesenchymal stromal cell (MSC)-derived neocartilage. Methods: RT-PCR was performed to evaluate mRNA levels of chondrogenic, hypertrophic and candidate mechanoresponsive genes in equine neocartilage sheets exposed to pulses of the TRPV4 agonist (GSK101) at different concentrations (N ​= ​10). Biochemical assays and mechanical tests (double indentation and unconfined compression) evaluated neocartilage properties (N ​= ​5). Results: at this concentration. This treatment regimen also increased sGAG content and enhanced compressive properties compared to untreated controls. GSK101 showed no effect on candidate mechanoresponsive genes at the time-point of analysis. Conclusions: Chemical activation of TRPV4 signalling can be used as a strategy to enhance matrix synthesis and maturation of MSC-derived engineered neocartilage and augment its load-bearing 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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.764

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 science0.0000.000
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 head0.024
GPT teacher head0.248
Teacher spread0.224 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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