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Record W4307309781 · doi:10.31399/asm.hb.v23a.a0006894

Three-Dimensional Bioprinting of Naturally Derived Protein-Based Biopolymers

2022· book-chapter· en· W4307309781 on OpenAlex
Gabriele Griffanti, Showan N. Nazhat

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

VenueASM International eBooks · 2022
Typebook-chapter
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsMcGill University
Fundersnot available
Keywords3D bioprintingBiofabricationDecellularizationSelf-healing hydrogelsMatrigelNanotechnologyElastinTissue engineeringScaffoldMaterials scienceChemistryBiomedical engineeringEngineeringBiologyBiochemistryPolymer chemistry

Abstract

fetched live from OpenAlex

Abstract This article discusses the state of the art in the 3D bioprinting field. It examines the printability of protein-based biopolymers and provides key printing parameters, along with a brief description of the main current 3D bioprinting approaches. The article presents some studies investigating 3D bioprinting of naturally derived proteins for the production of structurally and functionally biomimetic scaffolds, which create a microenvironment for cells resembling that of the native tissues. It describes key structural proteins processed in the form of hydrogels, such as collagen, silk, fibrin, and others such as elastin, decellularized matrix, and Matrigel (Corning), which are used as biomaterials.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.824
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.017
GPT teacher head0.241
Teacher spread0.224 · 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