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Record W1168336773 · doi:10.1007/s12079-015-0305-7

Signs of stress on soft surfaces

2015· article· en· W1168336773 on OpenAlexaff
Yousef Shafieyan, Boris Hinz

Bibliographic record

VenueJournal of Cell Communication and Signaling · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStress fiberMyocardinBiophysicsMechanotransductionStress (linguistics)Materials scienceCellStiffnessTranscription factorCell biologyChemistryBiologyComposite materialCytoskeletonBiochemistrySerum response factor

Abstract

fetched live from OpenAlex

Cells experience mechanical stimuli during growth and differentiation and transduce these stimuli into biochemical signals that in turn regulate cell responses to the imposed forces. Reduced spreading and impaired stress fiber formation are indicators of the mechano-response to growth on soft elastic culture substrates. However, Cui and coworkers demonstrate that cell spreading and stress fiber formation on soft substrates is possible if simultaneous cyclic stretching compensates for the lack of substrate stiffness-induced cell stress. The stress(ed) response is dependent on cyclic stretch amplitude and frequency and, at least in part, mediated by myocardin related transcription factor A (MRTF-A) and Yes-associated protein (YAP). The study thus provides novel insight into the mechanisms of cell mechanosensing and how materials can be designed to mimic mechanical conditions of body tissues.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.030
GPT teacher head0.274
Teacher spread0.244 · 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 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

Citations16
Published2015
Admission routes1
Has abstractyes

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