MétaCan
Menu
Back to cohort
Record W2785687289 · doi:10.1039/c7bm01050h

3D cellular structures and co-cultures formed through the contactless magnetic manipulation of cells on adherent surfaces

2018· article· en· W2785687289 on OpenAlexafffund
Abdel Rahman Abdel Fattah, Sarah Mishriki, Tobias Kammann, Rakesh P. Sahu, Fei Geng, Ishwar K. Puri

Bibliographic record

VenueBiomaterials Science · 2018
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaMitacsDeutscher Akademischer AustauschdienstCanada Foundation for InnovationOntario Research Foundation
KeywordsChemistryBiophysicsCell biologyBiology

Abstract

fetched live from OpenAlex

A magnet array is employed to manipulate diamagnetic cells that are contained in paramagnetic medium to demonstrate for the first time the contactless bioprinting of three-dimensional (3D) cellular structures and co-cultures of breast cancer MCF-7 and endothelial HUVEC at prescribed locations on tissue culture treated well plates. Sequential seeding of different cell lines and the spatial displacement of the magnet array creates co-cultured cellular structures within a well without using physically intrusive well inserts. Both monotypic and co-culture experiments produce morphologically rich 3D cell structures that are otherwise absent in regular monolayer cell cultures. The magnetic contactless bioprinting of cells provides further insight into cell behaviour, invasion strategies and transformations that are useful for potential applications in drug screening, 3D cell culture formation and tissue engineering.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.029
GPT teacher head0.309
Teacher spread0.279 · 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

Citations21
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueBiomaterials ScienceSame topic3D Printing in Biomedical ResearchFrench-language works237,207