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Record W3005888597 · doi:10.1038/s41598-020-59463-9

Carbon nanotubes promote cell migration in hydrogels

2020· article· en· W3005888597 on OpenAlexafffund
Hossein Ravanbakhsh, Guangyu Bao, Luc Mongeau

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsMcGill University
FundersNational Institute on Deafness and Other Communication DisordersNational Institutes of HealthFaculty of Engineering, McGill UniversityMcGill UniversityU.S. Department of Health and Human Services
KeywordsSelf-healing hydrogelsCarbon nanotubeCell migrationCellChemistryNanotechnologyMaterials scienceBiochemistryPolymer chemistry

Abstract

fetched live from OpenAlex

Injectable hydrogels are increasingly used for in situ tissue regeneration and wound healing. Ideally, an injectable implant should promote the recruitment of cells from the surrounding native tissue and allow cells to migrate freely as they generate a new extracellular matrix network. Nanocomposite hydrogels such as carbon nanotube (CNT)-loaded hydrogels have been hypothesized to promote cell recruitment and cell migration relative to unloaded ones. To investigate this, CNT-glycol chitosan hydrogels were synthesized and studied. Chemoattractant-induced cell migration was studied using a modified Boyden Chamber experiment. Migrated cells were counted using flow cytometry. Cell adhesion was inferred from the morphology of the cells via an image segmentation method. Cell migration and recruitment results confirmed that small concentrations of CNT significantly increase cell migration in hydrogels, thereby accelerating tissue regeneration and wound healing in situations where there is insufficient migration in the unloaded matrix.

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 score0.034
Threshold uncertainty score0.318

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 head0.191
Teacher spread0.182 · 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

Citations69
Published2020
Admission routes2
Has abstractyes

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