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Record W3046077328 · doi:10.1016/j.mtbio.2020.100070

Tissue-in-a-Tube: three-dimensional in vitro tissue constructs with integrated multimodal environmental stimulation

2020· article· en· W3046077328 on OpenAlexafffund
Alireza Shahin‐Shamsabadi, P. Ravi Selvaganapathy

Bibliographic record

VenueMaterials Today Bio · 2020
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsMulticellular organismExtracellular matrixTissue engineeringMatrix (chemical analysis)Process (computing)Biomedical engineeringMaterials scienceTube (container)BiophysicsNanotechnologyCellChemistryComputer scienceCell biologyComposite materialBiologyEngineering

Abstract

fetched live from OpenAlex

Three-dimensional (3D) in vitro tissue models are superior to two-dimensional (2D) cell cultures in replicating natural physiological/pathological conditions by recreating the cellular and cell-matrix interactions more faithfully. Nevertheless, current 3D models lack either the rich multicellular environment or fail to provide appropriate biophysical stimuli both of which are required to properly recapitulate the dynamic in vivo microenvironment of tissues and organs. Here, we describe the rapid construction of multicellular, tubular tissue constructs termed Tissue-in-a-Tube using self-assembly process in tubular molds with the ability to incorporate a variety of biophysical stimuli such as electrical field, mechanical deformation, and shear force of the fluid flow. Unlike other approaches, this method is simple, requires only oxygen permeable silicone tubing that molds the tissue construct and thin stainless-steel pins inserted in it to anchor the construct and could be used to provide electrical and mechanical stimuli, simultaneously. The annular region between the tissue construct and the tubing is used for perfusion. Highly stable, macroscale, and robust constructs anchored to the pins form as a result of self-assembly of the extracellular matrix (ECM) and cells in the bioink that is filled into the tubing. We demonstrate patterning of grafts containing cell types in the constructs in axial and radial modes with clear interface and continuity between the layers. Different environmental factors affecting cell behavior such as compactness of the structure and size of the constructs can be controlled through parameters such as initial cell density, ECM content, tubing size, as well as the distance between anchor pins. Using connectors, network of tubing can be assembled to create complex macrostructured tissues (centimeters length) such as fibers that are bifurcated or columns with different axial thicknesses which can then be used as building blocks for biomimetic constructs or tissue regeneration. The method is versatile and compatible with various cell types including endothelial, epithelial, skeletal muscle cells, osteoblast cells, and neuronal cells. As an example, long mature skeletal muscle and neuronal fibers as well as bone constructs were fabricated with cellular alignment dictated by the applied electrical field. The versatility, speed, and low cost of this method is suited for widespread application in tissue engineering and regenerative 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 score0.001
Threshold uncertainty score0.002

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.236
Teacher spread0.225 · 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

Citations14
Published2020
Admission routes2
Has abstractyes

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