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Record W2742776345 · doi:10.1101/cshperspect.a028761

Mechanosensing and Mechanotransduction at Cell–Cell Junctions

2017· review· en· W2742776345 on OpenAlexaff
Alpha S. Yap, Kinga Duszyc, Virgile Viasnoff

Bibliographic record

VenueCold Spring Harbor Perspectives in Biology · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsCanadian Nautical Research Society
FundersNational Health and Medical Research CouncilMedical Research CouncilMechanobiology Institute, Singapore
KeywordsMechanotransductionBiologyCell biologyCell adhesionCadherinCellCell junctionCell–cell interactionAdherens junctionAdhesionFocus (optics)Cell mechanicsCytoskeletonPhysicsGenetics

Abstract

fetched live from OpenAlex

Cell adhesion systems are defined by their ability to resist detachment force. Our understanding of the biology of cell-cell adhesions has recently been transformed by the realization that many of the forces that act on those adhesions are generated by the cells that they couple together; and that force at adhesive junctions can be sensed to regulate cell behavior. Here, we consider the mechanisms responsible for applying force to cell-cell junctions and the mechanosensory pathways that detect those forces. We focus on cadherins, as these are the best-studied examples to date, but it is likely that similar principles will apply to other molecular systems that can engage with force-generators within cells and physically couple those cells together.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.005

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 head0.044
GPT teacher head0.336
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), 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

Citations210
Published2017
Admission routes1
Has abstractyes

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