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From nano to micro: topographical scale and its impact on cell adhesion, morphology and contact guidance

2016· review· en· W2340602993 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Physics Condensed Matter · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsUniversity of Waterloo
FundersMechanobiology Institute, SingaporeMinistry of Education - SingaporeNational Research Foundation Singapore
KeywordsMicroscale chemistryExtracellular matrixNanoscopic scaleNanotechnologyAdhesionMaterials scienceCell adhesionRegenerative medicineMorphology (biology)CellChemistryBiologyComposite materialCell biology

Abstract

fetched live from OpenAlex

Topography, among other physical factors such as substrate stiffness and extracellular forces, is known to have a great influence on cell behaviours. Optimization of topographical features, in particular topographical dimensions ranging from nanoscale to microscale, is the key strategy to obtain the best cellular performance for various applications in tissue engineering and regenerative medicine. In this review, we provide a comprehensive survey on the significance of sizes of topography and their impacts on cell adhesion, morphology and alignment, and neurite guidance. Also recent works mimicking the hierarchical structure of natural extracellular matrix by combining both nanoscale and microscale topographies are highlighted.

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.

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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.809
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.012
GPT teacher head0.297
Teacher spread0.285 · 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