MétaCan
Menu
Back to cohort
Record W2614261539 · doi:10.1002/adbi.201700058

3D Printing of Microstructured and Stretchable Chitosan Hydrogel for Guided Cell Growth

2017· article· en· W2614261539 on OpenAlexafffund
Qinghua Wu, Marion Maire, Sophie Lerouge, Daniel Therriault, Marie‐Claude Heuzey

Bibliographic record

VenueAdvanced Biosystems · 2017
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsÉcole de Technologie SupérieureUniversité de MontréalPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaConcordia UniversityChina Scholarship CouncilCanada Foundation for Innovation
KeywordsSelf-healing hydrogelsMicrofiberMaterials scienceNanotechnologyChitosanTissue engineeringCell encapsulationComposite materialBiomedical engineeringChemical engineeringPolymer chemistry

Abstract

fetched live from OpenAlex

The ability to produce complex micro‐ or nanostructures from naturally derived hydrogels is significant for biomedical applications. However, precisely controlled architectures of soft hydrogels are difficult to be achieved due to their limited mechanical properties. Despite intensive research, significant challenges persist to fabricate hydrogels with ordered structures and adequate mechanical and biological properties for mimicking native tissues. In this work, a 3D printing technique is proposed to fabricate chitosan hydrogel with highly flexible and organized microfiber networks. The microstructured hydrogel scaffolds are obtained through a neutralization step. The strain at failure of hydrogel filaments can reach up to ≈400% and maximum strength is ≈7.5 MPa. The hydrogel scaffolds feature surface textures that can guide and align cell growth. This approach of tailoring hydrogels opens doors to design and produce 3D tissue constructs with topographical, biological, and mechanical compatibility.

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 score0.012
Threshold uncertainty score0.583

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 head0.013
GPT teacher head0.269
Teacher spread0.257 · 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

Citations96
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueAdvanced BiosystemsSame topic3D Printing in Biomedical ResearchFrench-language works237,207