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Record W2800187340 · doi:10.1126/sciadv.aas8998

Hydrogel microenvironments for cancer spheroid growth and drug screening

2018· review· en· W2800187340 on OpenAlexafffund
Yunfeng Li, Eugenia Kumacheva

Bibliographic record

VenueScience Advances · 2018
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSpheroidSelf-healing hydrogelsDrugBiophysicsNanotechnologyCancer cellCancerChemistryMaterials scienceBiologyMedicinePharmacologyBiochemistryInternal medicine

Abstract

fetched live from OpenAlex

Multicellular cancer spheroids (MCSs) have emerged as a promising in vitro model that replicates many features of solid tumors in vivo. Biomimetic hydrogel scaffolds for MCS growth offer a broad spectrum of biophysical and biochemical cues that help to recapitulate the behavior of natural extracellular matrix, essential for regulating cancer cell behavior. This perspective highlights recent advances in the development of hydrogel environments for MCS growth, release, and drug screening. We review the use of different types of hydrogels for MCS growth, the effect of biophysical and biochemical cues on MCS fate, the isolation of MCSs from hydrogel scaffolds, the utilization of microtechnologies, and the applications of MCSs grown in hydrogels. We conclude with the discussion of new research directions in the development of hydrogels for MCS growth.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.033
GPT teacher head0.355
Teacher spread0.322 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations342
Published2018
Admission routes2
Has abstractyes

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