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Record W4407024655 · doi:10.1088/1758-5090/adb0f4

Bioinks for engineering gradient-based osteochondral and meniscal tissue substitutes: a review

2025· review· en· W4407024655 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

VenueBiofabrication · 2025
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTissue engineeringMaterials scienceBiomedical engineeringOrthodonticsMedicine

Abstract

fetched live from OpenAlex

Gradient tissues are anisotropic structure with gradual transition in structural and biological properties. The gradient in structural, mechanical and biochemical properties of osteochondral and meniscal tissues play a major role in defining tissue functions. Designing tissue substitutes that replicate these gradient properties is crucial to facilitate regeneration of tissue functions following injuries. Advanced manufacturing technologies such as 3D bioprinting hold great potentials for recreating gradient nature of tissues through using zone-specific bioinks and layer-by-layer deposition of spatially defined biomaterials, cell types and bioactive cues. This review highlighted the gradients in osteochondral and meniscal tissues in detail, elaborated on individual components of the bioink, and reviewed recent advancements in 3D gradient-based osteochondral and meniscal tissue substitutes. Finally, key challenges of the field and future perspectives for developing gradient-based tissue substitutes were discussed. The insights from these advances can broaden the possibilities for engineering gradient tissues.

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)0.001
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 score0.934
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.030
GPT teacher head0.343
Teacher spread0.313 · 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