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Record W2731202217 · doi:10.1371/journal.pone.0178981

Corneal epithelial cells exposed to shear stress show altered cytoskeleton and migratory behaviour

2017· article· en· W2731202217 on OpenAlexafffund
Sara Molladavoodi, Matthew Robichaud, David Wulff, Maud Gorbet

Bibliographic record

VenuePLoS ONE · 2017
Typearticle
Languageen
FieldMedicine
TopicCorneal Surgery and Treatments
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCytoskeletonShear stressCell biologyDownregulation and upregulationCorneaActin cytoskeletonCorneal epitheliumCell migrationActinChemistryShear (geology)In vitroBiophysicsMaterials scienceBiologyCellComposite materialBiochemistry

Abstract

fetched live from OpenAlex

Cells that form the corneal epithelium, the outermost layer of the cornea, are exposed to shear stress through blinking during waking hours. In this in vitro study, the effect of fluid shear stress on human corneal epithelial cells (HCECs) was investigated. Following exposure to shear stresses of 4 and 8 dyn/cm2, HCECs showed cytoskeletal rearrangement with more prominent, organized and elongated filamentous actin. Cytoskeletal changes were time-dependent, and were most significant after 24 hours of shear stress. Higher rates of migration and proliferation, as evaluated by a scratch assay, were also observed following 24 hours of low shear stress exposure (4 dyn/cm2). This result contrasted the poor migration observed in samples scratched before shear exposure, indicating that shear-induced cytoskeletal changes played a key role in improved wound healing and must therefore precede any damage to the cell layer. HCEC cytoskeletal changes were accompanied by an upregulation in integrin β1 and downregulation of ICAM-1. These results demonstrate that HCECs respond favourably to flow-induced shear stress, impacting their proliferation and migration properties as well as phenotype.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.641

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 head0.063
GPT teacher head0.264
Teacher spread0.201 · 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 designObservational
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

Citations62
Published2017
Admission routes2
Has abstractyes

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