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Record W3046049502 · doi:10.1038/s41598-020-69267-6

β1 integrin, ILK and mTOR regulate collagen synthesis in mechanically loaded tendon cells

2020· article· en· W3046049502 on OpenAlexafffund
Rouhollah Mousavizadeh, Payman Hojabrpour, Felipe Eltit, Paul C. McDonald, Shoukat Dedhar, Robert G. McCormack, Vincent Duronio, Seyed Mehdi Jafarnejad, Alex Scott

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIntegrinTendonPI3K/AKT/mTOR pathwayCell biologyChemistryMedicineBiologyAnatomySignal transductionBiochemistryCell

Abstract

fetched live from OpenAlex

Tendons are specialized tissues composed primarily of load-responsive fibroblasts (tenocytes) embedded in a collagen-rich extracellular matrix. Habitual mechanical loading or targeted exercise causes tendon cells to increase the stiffness of the extracellular matrix; this adaptation may occur in part through collagen synthesis or remodeling. Integrins are likely to play an important role in transmitting mechanical stimuli from the extracellular matrix to tendon cells, thereby triggering cell signaling pathways which lead to adaptive regulation of mRNA translation and protein synthesis. In this study, we discovered that mechanical stimulation of integrin β1 leads to the phosphorylation of AKT, an event which required the presence of integrin-linked kinase (ILK). Repetitive stretching of tendon cells activates the AKT and mTOR pathways, which in turn regulates mRNA translation and collagen expression. These results support a model in which integrins are an upstream component of the mechanosensory cellular apparatus, regulating fundamental tendon cell functions relevant to exercise-induced adaptation and mechanotherapy.

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 score0.016
Threshold uncertainty score0.525

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 head0.016
GPT teacher head0.235
Teacher spread0.219 · 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

Citations80
Published2020
Admission routes2
Has abstractyes

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