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Record W4408201235 · doi:10.1016/j.cobme.2025.100587

Emerging views of biomechanics via embedded sensors in model tissues: Pathways to the clinic

2025· review· en· W4408201235 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCurrent Opinion in Biomedical Engineering · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsMcGill University Health CentreMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaMitacsCanada Research Chairs
KeywordsBiomechanicsComputer scienceMedicinePhysical medicine and rehabilitationAnatomy

Abstract

fetched live from OpenAlex

Mechanical features of tissues have been recognised as key drivers of disease progression and are increasingly investigated as diagnostic and therapeutic targets. Engineered tissue models with integrated embedded biomechanical sensors have recently uncovered complex mechanical behaviors across micro- and nanoscale environments, offering novel insights into developmental and disease mechanisms. This short opinion synthesizes emerging mechanical signatures that have been identified at high measurement sensitivities and spatial resolutions by embedding customized biomechanical sensors into engineered tissues, particularly for soft tissue pathologies like cancer and fibrosis. We then describe the challenges of achieving these increased resolutions in clinical practice, and highlight recent innovative strategies that may ultimately bridge these gaps. If successful, these improved biomechanical measurement systems could open new pathways for improving diagnostics and patient outcomes.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.066
GPT teacher head0.381
Teacher spread0.315 · 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