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Record W4379390754 · doi:10.32920/23296355.v1

The Role of Mitogen Activated Protein Kinase Signalling On Cell Migration and Fluidity via Cytoskeletal Reorganization

2023· preprint· en· W4379390754 on OpenAlexaff
Abinaya Loganaathan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsToronto Metropolitan UniversityOntario Tech University
Fundersnot available
KeywordsFocal adhesionCell biologyCytoskeletonMAPK/ERK pathwayProtein kinase AActinCell migrationActin cytoskeletonKinaseMitogen-activated protein kinaseBiologyChemistryCellSignal transductionBiochemistry

Abstract

fetched live from OpenAlex

The abnormal regulation of the mitogen-activated protein kinase (MAPK) signalling pathway has been associated with the development of tumour cell invasion and metastasis through changes in the expression and activation of diverse cytoskeletal proteins (Qian et al., 2017). This research project intended to investigate the influence of MAPK signalling on cytoskeletal reorganization and its effects of cell fluidity on the migratory processes of MDA MB 231 human breast cancer cells. Selected (S3) cells isolated for enhanced motile characteristics were linked to increased downstream MAPK protein expressions and exhibited reduced actin and focal adhesion densities and lower actin anisotropy, observations that are correlated with softer cell structures. Cells treated with MEK inhibitors reversed these changes, demonstrating increased F-actin density and anisotropy as well as greater focal adhesion densities that were correlated with reduced cell speeds. These results reveal that MAPK signalling has a significant role in regulating cell biomechanics.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.009
GPT teacher head0.220
Teacher spread0.211 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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