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Record W3011099411 · doi:10.1021/acsami.0c01172

Nanoporosity Stimulates Cell Spreading and Focal Adhesion Formation in Cells with Mutated Paxillin

2020· article· en· W3011099411 on OpenAlexafffund
Dainelys Guadarrama Bello, Aurélien Fouillen, Antonella Badia, Antonio Nanci

Bibliographic record

VenueACS Applied Materials & Interfaces · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsUniversité de Montréal
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsPaxillinFocal adhesionFilopodiaNanotopographyCell biologyCell adhesionMaterials scienceAdhesionActinCDC42LamellipodiumCell migrationCellBiophysicsPhosphorylationBiologyBiochemistry

Abstract

fetched live from OpenAlex

We have evaluated the response to nanotopography of CHO-K1 cells that express wild-type paxillin or paxillin with mutations at serine 273 that inhibit phosphorylation. Cells were grown on nanoporous and polished titanium surfaces. With all cell types, immunofluorescence showed that adhesion and spreading were minimally affected on the treated surface and that the actin filaments were more abundant and well-aligned. Scanning electron microscopy revealed changes in cell shape and abundant filopodia with lateral nanoprotrusions in response to nanoporosity. Gene expression of proteins associated with cellular adhesion and protrusions was significantly increased on the nanoporous surface regardless of the cell type. In particular, α-actinin, Rac1, Cdc42, and ITGα1 were upregulated in S273 cells with alanine substitutions, whereas FAK, Pxn, and Src were downregulated, leading to improved focal adhesion formation. These findings suggest that the surface nanoporosity can "compensate for" the genetic mutations that affect the biomechanical relationship of cells to surfaces.

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 score0.001
Threshold uncertainty score0.550

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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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

Citations28
Published2020
Admission routes2
Has abstractyes

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