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Record W4386448891 · doi:10.1088/1758-5090/acf6e1

Is it possible to 3D bioprint load-bearing bone implants? A critical review

2023· review· en· W4386448891 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 · 2023
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLoad bearingScaffoldBiomedical engineeringBearing (navigation)Computer scienceMaterials scienceMedicine

Abstract

fetched live from OpenAlex

Rehabilitative capabilities of any tissue engineered scaffold rely primarily on the triad of (i) biomechanical properties such as mechanical properties and architecture, (ii) chemical behavior such as regulation of cytokine expression, and (iii) cellular response modulation (including their recruitment and differentiation). The closer the implant can mimic the native tissue, the better it can rehabilitate the damage therein. Among the available fabrication techniques, only 3D bioprinting (3DBP) can satisfactorily replicate the inherent heterogeneity of the host tissue. However, 3DBP scaffolds typically suffer from poor mechanical properties, thereby, driving the increased research interest in development of load-bearing 3DBP orthopedic scaffolds in recent years. Typically, these scaffolds involve multi-material 3D printing, comprising of at-least one bioink and a load-bearing ink; such that mechanical and biological requirements of the biomaterials are decoupled. Ensuring high cellular survivability and good mechanical properties are of key concerns in all these studies. 3DBP of such scaffolds is in early developmental stages, and research data from only a handful of preliminary animal studies are available, owing to limitations in print-capabilities and restrictive materials library. This article presents a topically focused review of the state-of-the-art, while highlighting aspects like available 3DBP techniques; biomaterials' printability; mechanical and degradation behavior; and their overall bone-tissue rehabilitative efficacy. This collection amalgamates and critically analyses the research aimed at 3DBP of load-bearing scaffolds for fulfilling demands of personalized-medicine. We highlight the recent-advances in 3DBP techniques employing thermoplastics and phosphate-cements for load-bearing applications. Finally, we provide an outlook for possible future perspectives of 3DBP for load-bearing orthopedic applications. Overall, the article creates ample foundation for future research, as it gathers the latest and ongoing research that scientists could utilize.

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)0.002
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 score0.893
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.015

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 head0.183
GPT teacher head0.436
Teacher spread0.253 · 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