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Record W2739759579 · doi:10.1158/1538-7445.am2017-5791

Abstract 5791: 3D tissue engineering bladder model for cancer invasion study

2017· article· en· W2739759579 on OpenAlexaff
Cassandra Ringuette Goulet, Geneviève Bernard, Stéphane Chabaud, Frédéric Pouliot, Stéphane Bolduc

Bibliographic record

VenueCancer Research · 2017
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsBladder cancerSpheroidCell cultureCancerTissue engineeringIn vivoCancer researchCancer cellFluorescence microscopeCellBiologyPathologyCell biologyMedicineInternal medicineBiomedical engineeringBiotechnologyBiochemistryFluorescenceGenetics

Abstract

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Abstract INTRODUCTION: Our understanding of the biological processes involved in bladder cancer (BCa) is greatly limited by the models currently available. In fact, the combination of in vitro and in vivo models of BCa has failed to elucidate all the fundamental aspects of the disease. The eighth most commonly diagnosed cancer in Western societies, BCa has become a growing public health concern, and more realistic models are needed to reveal the mechanisms involved in tumor initiation and progression. METHODS: Bladder substitutes have been constructed by tissue engineering with healthy human fibroblasts and urothelial cells, using the self-assembly method. Meanwhile, spheroids have been produced from non-invasive (RT4) and invasive (T24) BCa cell lines expressing DsRed fluorescent protein. The invasive potential of these spheroids was characterized in a type-I collagen gel (2.5mg/mL). Then, the spheroids were implanted on the surface of bladder substitutes, after which their development was followed by fluorescence microscopy. RESULTS: Both of the cancer cell lines used were able to form compact spheroids and grow on bladder equivalents. The invasive behaviour of spheroids varied depending on the nature of the cells used. The non-invasive RT4 cell line was unable to cross the basal lamina whereas the invasive T24 cell line was able to do so. CONCLUSION: The establishment of such a model for studying cancer biology in a physiological environment will help bridge the gap between overly simple cell culture models and more complex transgenic mice models. This new model offers a unique opportunity to study separately the players involved in the development of BCa and thus represents a powerful tool for the mechanistic analysis of this complex pathology. Citation Format: Cassandra Ringuette Goulet, Geneviève Bernard, Stéphane Chabaud, Frédéric Pouliot, Stéphane Bolduc. 3D tissue engineering bladder model for cancer invasion study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5791. doi:10.1158/1538-7445.AM2017-5791

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.211
GPT teacher head0.481
Teacher spread0.271 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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