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Record W3165992805 · doi:10.1111/wrr.12955

Engineered microenvironment for the study of myofibroblast mechanobiology

2021· article· en· W3165992805 on OpenAlexaff
Ying Xu, Richard C. Koya, Kjetil Ask, Ruogang Zhao

Bibliographic record

VenueWound Repair and Regeneration · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsMcMaster UniversityPrograms for Assessment of Technology in Health Research Institute
FundersNational Institute of Biomedical Imaging and BioengineeringNational Heart, Lung, and Blood InstituteKorea National Institute of Health
KeywordsMechanobiologyMyofibroblastMechanosensitive channelsExtracellular matrixCell biologyMechanotransductionCell cultureCell migrationBiologyFibrosisPathologyMedicineBiochemistry

Abstract

fetched live from OpenAlex

Myofibroblasts are mechanosensitive cells and a variety of their behaviours including differentiation, migration, force production and biosynthesis are regulated by the surrounding microenvironment. Engineered cell culture models have been developed to examine the effect of microenvironmental factors such as the substrate stiffness, the topography and strain of the extracellular matrix (ECM) and the shear stress on myofibroblast biology. These engineered models provide well-mimicked, pathophysiologically relevant experimental conditions that are superior to those enabled by the conventional two-dimensional (2D) culture models. In this perspective, we will review the recent advances in the development of engineered cell culture models for myofibroblasts and outline the findings on the myofibroblast mechanobiology under various microenvironmental conditions. These studies have demonstrated the power and utility of the engineered models for the study of microenvironment-regulated cellular behaviours. The findings derived using these models contribute to a greater understanding of how myofibroblast behaviour is regulated in tissue repair and pathological scar formation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.168

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 head0.008
GPT teacher head0.222
Teacher spread0.213 · 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 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

Citations10
Published2021
Admission routes1
Has abstractyes

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