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Record W4206601791 · doi:10.1002/ppap.202100206

A novel 3D in vitro tissue model for bone‐metastasized breast cancer: A preclinical tool in drug discovery and testing

2022· article· en· W4206601791 on OpenAlexafffund
Mansoureh Mohseni Garakani, Megan E. Cooke, M. R. Wertheimer, Derek H. Rosenzweig, Abdellah Ajji

Bibliographic record

VenuePlasma Processes and Polymers · 2022
Typearticle
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsMcGill UniversityMcGill University Health CentrePolytechnique Montréal
FundersCanadian Institutes of Health ResearchCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsBreast cancerDoxorubicinCancer researchTumor microenvironmentCancerBone metastasisIn vitroMedicineBiomedical engineeringChemistryTumor cellsChemotherapyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Bone metastasis is a frequent occurrence following breast cancer. The bone‐tumor microenvironment is heterogeneous and complicated to recapitulate. The development of new chemotherapeutics is ineffective partly due to a lack of precise in vitro tissue models. We developed a three‐dimensional (3D) bone‐tumor interface model for customized chemotherapeutic screening. It comprises a plasma‐modified electrospun mat seeded with osteoblasts to mimic a bone tissue, with a cell‐seeded hydrogel layer containing more and less aggressive or noncancerous cells on top, mimicking the tumor compartment. By screening the model with doxorubicin, we observed different migratory behaviors, with IC50 values that were largely in accordance with those cell lines' characteristics. Our 3D model reproduces the bone microenvironment and has great potential as a drug screening tool for personalized medicine.

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)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.039
GPT teacher head0.327
Teacher spread0.288 · 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

Citations12
Published2022
Admission routes2
Has abstractyes

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