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Record W2732174907 · doi:10.1038/s41598-017-04691-9

Directing the Self-assembly of Tumour Spheroids by Bioprinting Cellular Heterogeneous Models within Alginate/Gelatin Hydrogels

2017· article· en· W2732174907 on OpenAlexafffund
Tao Jiang, José G. Munguia-López, Salvador Flores-Torres, Joel Grant, Sanahan Vijayakumar, Antonio De León‐Rodríguez, Joseph M. Kinsella

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsMcGill University
FundersChina Scholarship CouncilConsejo Nacional de Ciencia y TecnologíaMcGill University
KeywordsSpheroidSelf-healing hydrogels3D bioprintingGelatinStromal cellFibroblastExtracellular matrixIn vitroCell biologyMatrix (chemical analysis)Cancer-Associated FibroblastsTumor microenvironmentMulticellular organismChemistryCancer researchBiologyBiomedical engineeringTissue engineeringMedicineBiochemistryCellTumor cells

Abstract

fetched live from OpenAlex

Human tumour progression is a dynamic process involving diverse biological and biochemical events such as genetic mutation and selection in addition to physical, chemical, and mechanical events occurring between cells and the tumour microenvironment. Using 3D bioprinting we have developed a method to embed MDA-MB-231 triple negative breast cancer cells, and IMR-90 fibroblast cells, within a cross-linked alginate/gelatin matrix at specific initial locations relative to each other. After 7 days of co-culture the MDA-MB-231 cells begin to form multicellular tumour spheroids (MCTS) that increase in size and frequency over time. After ~15 days the IMR-90 stromal fibroblast cells migrate through a non-cellularized region of the hydrogel matrix and infiltrate the MDA-MB-231 spheroids creating mixed MDA-MB-231/IMR-90 MCTS. This study provides a proof-of-concept that biomimetic in vitro tissue co-culture models bioprinted with both breast cancer cells and fibroblasts will result in MCTS that can be maintained for durations of several weeks.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.175
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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

Citations134
Published2017
Admission routes2
Has abstractyes

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