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Record W2558013825 · doi:10.1002/anie.201610353

Supramolecular Nanofibrillar Thermoreversible Hydrogel for Growth and Release of Cancer Spheroids

2016· article· en· W2558013825 on OpenAlexafffund
Yunfeng Li, Nancy Khuu, Albert Gevorkian, Sharon Sarjinsky, Héloïse Thérien‐Aubin, Yihe Wang, Sangho Cho, Eugenia Kumacheva

Bibliographic record

VenueAngewandte Chemie International Edition · 2016
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSelf-healing hydrogelsSpheroidMaterials sciencePolymerCell encapsulationAqueous suspensionNanotechnologyCancer cellExtracellular matrixChemical engineering3D cell cultureSupramolecular chemistryTissue engineeringBiophysicsChemistryAqueous solutionPolymer chemistryCellCancerMoleculeBiomedical engineeringOrganic chemistryIn vitroBiochemistryComposite material

Abstract

fetched live from OpenAlex

Growth of three-dimensional cancer spheroids (CSs) in man-made hydrogels mimicking natural extracellular matrix is an important and challenging task. Herein, we report on a supramolecular temperature-responsive hydrogel designed for the growth and subsequent release of CSs. A filamentous hydrogel was formed at 37 °C from an aqueous suspension of cellulose nanocrystals surface-functionalized with temperature-responsive polymer molecules. The encapsulation of cells in the hydrogel enabled effective growth of CSs with dimensions determined by the concentration of cellulose nanocrystals in the hydrogel. On demand release of CSs without loss of cell viability and spheroid integrity was achieved upon hydrogel cooling. The tumorigenic properties of the released CSs were examined by encapsulating and re-growing them in fibrin hydrogel. The results in this work can be used in fundamental cancer research and in cancer drug screening.

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 score0.057
Threshold uncertainty score0.247

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 head0.011
GPT teacher head0.265
Teacher spread0.254 · 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

Citations88
Published2016
Admission routes2
Has abstractyes

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