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Record W2913239115 · doi:10.1088/1758-5090/ab0798

ExCeL: combining extrusion printing on cellulose scaffolds with lamination to create<i>in vitro</i>biological models

2019· article· en· W2913239115 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiofabrication · 2019
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsScaffoldTissue engineeringMaterials science3D bioprintingLayer (electronics)ExtrusionExtracellular matrix3D printingNanotechnologyLaminationBiomedical engineeringComputer scienceChemistryComposite materialEngineering

Abstract

fetched live from OpenAlex

Bioprinting is rapidly developing into a powerful tool in tissue engineering, for both organ printing and the development of in vitro models that can be used in drug discovery, toxicology and in vitro bioreactors. Nevertheless, the ability to create complex 3D culture systems with different types of cells and extracellular matrices integrated with perfusable channels has been a challenge. Here we develop an approach that combines the xurography of a scaffold material (cellulose) with extrusion printing of bioinks onto it, followed by assembly in a layer-by-layer fashion to create complex 3D culture systems that could be used as in vitro models of biological processes. This new method, termed ExCeL, can recapitulate the complexities of natural tissues with a proper 3D distribution of cells, extracellular matrices, and different molecules, while providing the whole structure with mechanical stability, and direct and easy access to the cells, even the ones that are positioned deep in the bulk of the structure, without the need to fix or section the samples. Briefly, the bioprinting of predefined patterns with a feature size of ∼1 mm has been made possible by treating paper with the hydrogel's crosslinker and printing cell-embedded hydrogel that will solidify immediately upon contact with the paper. These impregnated layers can be used as single layers or in a layer-by-layer approach by stacking them (here up to four layers) for applications such as cell migration and proliferation in 3D structures composed of collagen or alginate. Cells are generally sensitive to the amount of FBS in their culture media and we have shown how FBS amount will effect cell migration. By cutting the paper in certain patterns, printing hydrogel on the remaining parts of it, and stacking the paper in layers, features like embedded channels are formed that will provide cells will better mass transfer in thick structures. This technique provides biologists with a unique tool to perform sophisticated in vitro assays.

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.

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)0.001
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.243
Teacher spread0.221 · 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