Is it possible to 3D bioprint load-bearing bone implants? A critical review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".