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Record W4411192997 · doi:10.1113/jp286508

Leveraging mechanobiology: Identifying mechanotransducers with potential applications for tendon repair

2025· review· en· W4411192997 on OpenAlexfundno aff
Tabitha R. Stephenson, Megan Wong, Nathan R. Schiele

Bibliographic record

VenueThe Journal of Physiology · 2025
Typereview
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesHamilton Health SciencesNational Institutes of HealthNational Science Foundation
KeywordsMechanobiologyTendonBiomedical engineeringComputer scienceMedicineAnatomy

Abstract

fetched live from OpenAlex

Tendons are collagen-rich tissues that are necessary for movement and, as such, are exposed to mechanical forces. Mechanical loading impacts tendon formation, homeostasis and injury. Frequent injury and poor healing of tendon is a major clinical issue. An improved understanding of how tendon cells respond to mechanical forces is needed to advance new therapies to treat tendon injuries and limit degeneration caused by aberrant mechanical loading. In this review, we highlight recent discoveries in how mechanical stimulation impacts tendon and enthesis formation during development, as well as tendon maintenance and degradation during adulthood. We focus on understanding the cell-level mechanotransduction mechanisms, which include calcium signalling, activation of specific cell receptors and ion channels, and the effect on primary cilia and other downstream cell signalling pathways. These recently identified mechanotransducers in tendon cells show promise as future therapeutic targets, which can be leveraged for tendon healing.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.935
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.036
GPT teacher head0.345
Teacher spread0.308 · 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 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

Citations3
Published2025
Admission routes1
Has abstractyes

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