A Review of Recent Advances in 3D Bioprinting With an Eye on Future Regenerative Therapies in Veterinary Medicine
Bibliographic record
Abstract
3D bioprinting is a rapidly evolving industry that has been utilized for a variety of biomedical applications. It differs from traditional 3D printing in that it utilizes bioinks comprised of cells and other biomaterials to allow for the generation of complex functional tissues. Bioprinting involves computational modeling, bioink preparation, bioink deposition, and subsequent maturation of printed products; it is an intricate process where bioink composition, bioprinting approach, and bioprinter type must be considered during construct development. This technology has already found success in human studies, where a variety of functional tissues have been generated for both in vitro and in vivo applications. Although the main driving force behind innovation in 3D bioprinting has been utility in human medicine, recent efforts investigating its veterinary application have begun to emerge. To date, 3D bioprinting has been utilized to create bone, cardiovascular, cartilage, corneal and neural constructs in animal species. Furthermore, the use of animal-derived cells and various animal models in human research have provided additional information regarding its capacity for veterinary translation. While these studies have produced some promising results, technological limitations as well as ethical and regulatory challenges have impeded clinical acceptance. This article reviews the current understanding of 3D bioprinting technology and its recent advancements with a focus on recent successes and future translation in veterinary medicine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".