MétaCan
Menu
Back to cohort
Record W2888223014 · doi:10.15562/gnc.53

Stem Cells’ Future: Toward Organ Bioprinting

2017· article· en· W2888223014 on OpenAlexvenueno aff
Jalal Omrani, Madjid Momeni‐Moghaddam

Bibliographic record

VenueJournal of Genes and Cells · 2017
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsnot available
Fundersnot available
KeywordsRegenerative medicineTissue engineeringStem cellMesenchymal stem cellRegeneration (biology)3D bioprintingExtracellular matrixCell typeTransplantationBiologyClinical uses of mesenchymal stem cellsCell biologyCellAdult stem cellCellular differentiationMedicine

Abstract

fetched live from OpenAlex

Organ Bioprinting is a new approach in the field of regenerative medicine that try to make a whole intact functionally active organ from tissue specific cells. Providing such organs are very important because every year many patients need to organ transplantation but the number of donors are so limited. Organs made from different tissue with different kinds of cell types and for Organ Bioprinting first we need to provide these cell types. Currently, with regards to advances in stem cell biology specially invention of easy methods for isolation mesenchymal stem cells (MSCs) as one the appropriate cell type for regeneration purpose and also due to their unique properties including lack of immune-rejection in allograft, MSCs have gained attention for using in organ production. MSCs can isolate from many tissues of adults and differentiate in a targeted manner into cells of interest; provide a main material of tissue engineering triangle (i.e. cells, biomaterial and growth factors) for 3D bioprinting of human organs on a substrate. Printers have the ability of designing tissues and organs with fusing living tissue specific cells and extracellular matrix in layers to produce 3D biologically functional new organ. It is conceivable that in the near future, stem cells will play their predicted role; i.e. whole organ production.

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 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 score0.392
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.258
Teacher spread0.235 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Genes and CellsSame topic3D Printing in Biomedical ResearchFrench-language works237,207