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Record W2804570423 · doi:10.1007/s12195-018-0530-2

3D Bioprinting Stem Cell Derived Tissues

2018· review· en· W2804570423 on OpenAlexafffund
Nishat Tasnim, Laura De la Vega, Shweta Kumar, Laila Abelseth, Matthew Alonzo, Meitham Amereh, Binata Joddar, Stephanie M. Willerth

Bibliographic record

VenueCellular and Molecular Bioengineering · 2018
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaInternational Collaboration On Repair DiscoveriesUniversity of Victoria
FundersNational Heart, Lung, and Blood InstituteNatural Sciences and Engineering Research Council of CanadaBritish Columbia Innovation CouncilMitacsCanada Research ChairsStem Cell NetworkNational Institutes of HealthNational Science Foundation
KeywordsStem cell3D bioprintingRegenerative medicineTissue engineeringAdipose tissueCell typeBiologyComputer scienceBiomedical engineeringCell biologyCellMedicine

Abstract

fetched live from OpenAlex

Stem cells offer tremendous promise for regenerative medicine as they can become a variety of cell types. They also continuously proliferate, providing a renewable source of cells. Recently, it has been found that 3D printing constructs using stem cells, can generate models representing healthy or diseased tissues, as well as substitutes for diseased and damaged tissues. Here, we review the current state of the field of 3D printing stem cell derived tissues. First, we cover 3D printing technologies and discuss the different types of stem cells used for tissue engineering applications. We then detail the properties required for the bioinks used when printing viable tissues from stem cells. We give relevant examples of such bioprinted tissues, including adipose tissue, blood vessels, bone, cardiac tissue, cartilage, heart valves, liver, muscle, neural tissue, and pancreas. Finally, we provide future directions for improving the current technologies, along with areas of focus for future work to translate these exciting technologies into clinical applications.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.270
Teacher spread0.247 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations81
Published2018
Admission routes2
Has abstractyes

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