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Record W2940504419 · doi:10.1002/smll.201805530

3D Bioprinting in Skeletal Muscle Tissue Engineering

2019· review· en· W2940504419 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.

Bibliographic record

VenueSmall · 2019
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Toronto
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institutes of HealthNational Institute of Arthritis and Musculoskeletal and Skin DiseasesDeutsche ForschungsgemeinschaftNarodowe Centrum Badań i RozwojuSTART, Global Change System for Analysis, Research, and Training
KeywordsTissue engineeringSkeletal muscleMaterials scienceBiomedical engineeringMuscle tissueNanotechnologyAnatomyEngineeringMedicine

Abstract

fetched live from OpenAlex

Skeletal muscle tissue engineering (SMTE) aims at repairing defective skeletal muscles. Until now, numerous developments are made in SMTE; however, it is still challenging to recapitulate the complexity of muscles with current methods of fabrication. Here, after a brief description of the anatomy of skeletal muscle and a short state-of-the-art on developments made in SMTE with "conventional methods," the use of 3D bioprinting as a new tool for SMTE is in focus. The current bioprinting methods are discussed, and an overview of the bioink formulations and properties used in 3D bioprinting is provided. Finally, different advances made in SMTE by 3D bioprinting are highlighted, and future needs and a short perspective are provided.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.056
GPT teacher head0.320
Teacher spread0.264 · 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