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Cell nanomechanics and focal adhesions are regulated by retinol and conjugated linoleic acid in a dose-dependent manner

2009· article· en· W2146748462 on OpenAlexafffund
Yaron Silberberg, Gleb E. Yakubov, Michael A. Horton, Andrew E. Pelling

Bibliographic record

VenueNanotechnology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsUniversity of Ottawa
FundersBiotechnology and Biological Sciences Research CouncilSackler TrustCanada Research ChairsWellcome Trust
KeywordsNanomechanicsCellCell biologyRetinolCell growthConjugated linoleic acidIntracellularMaterials scienceLinoleic acidBiophysicsBiologyBiochemistryFatty acidNanotechnologyAtomic force microscopyVitamin

Abstract

fetched live from OpenAlex

Retinol and conjugated linoleic acid (CLA) have previously been shown to have an important role in gene expression and various cellular processes, including differentiation, proliferation and cell death. In this study we have investigated the effect of retinol and CLA, both individually and in combination, on the intracellular cytoskeleton, focal adhesions (FAs) and the nanomechanical properties of 3T3 fibroblasts. We observed a dose-dependent decrease in the formation of FAs following treatment with either compound, which was directly correlated to an increase in cell height (>30%) and a decrease in the measured Young's modulus (approximately 28%). Furthermore, treatments with both compounds demonstrated an increased effect and led to a reduction of >70% in the average number of FAs per cell and a decrease of >50% in average cell stiffness. These data reveal that retinol and CLA disrupt FA formation, leading to an increase in cell height and a significant decrease in stiffness. These results may broaden our understanding of the interplay between cell nanomechanics and cellular contact with the external microenvironment, and help to shed light on the important role of retinoids and CLA in health and disease.

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 score0.007
Threshold uncertainty score0.922

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 integrity0.0010.000
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 head0.004
GPT teacher head0.205
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 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

Citations17
Published2009
Admission routes2
Has abstractyes

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