A Novel Primarily Bioresorbable Customized Prosthetic with the Concept of Stem-Cell-Based Attachments to Treat Canine Osteosarcoma
Bibliographic record
Abstract
Introduction: Osteosarcoma affects 25,000 dogs annually in the United States, often necessitating limb amputation. Current treatments such as external limb prosthetics, metallic endoprostheses, autografts, and allografts pose limited locomotion, infection, or residual bone/device fracture. This study explores limb-sparing, 3D-printing, biomaterials, and cell therapy techniques for a novel prosthetic. Materials and Methods: Custom-made prosthetics were designed based on patients’ medical images with an emphasis on the incorporation of a growth factor injection matrix at the ends to stimulate stem cell propagation and bone development. 3D-printable filaments were formulated with mixture ratios of titanium (0–18%) and polylactic acid (PLA) to ensure compatibility with the biomechanical properties of radius. Biomechanical analyses were conducted using finite element analysis focusing on three parameters: maximum stress/strain, and the buckling safety factor (BSF). The results were systematically compared with those of a healthy bone and a previously prototyped clinically tested customized metallic prosthetic. Results: The composite structure of 6, 12, and 18%-titanium and PLA exhibits strain/stress distribution and BSFs close to those of a healthy radius for small to large breed, and superior to a previously prototyped clinically tested metallic prosthetic. Discussion/Conclusion: The proposed composite prosthetic demonstrates considerable reduced susceptibility to biomechanical bone/device failures. Clinically, gradual device decomposition facilitates natural tissue replacement. The customized composite structure is highly cost-effective for small and medium size breeds since filament-based 3D-printing is particularly feasible. However, for larger breeds, with the 18%-titanium fraction, printability may be an issue regarding the thermoplastic properties of PLA; non-filament-based 3D-printing methods are practical and cost-effective. Acknowledgements: Special thanks are extended to Dr. Bernard Seguin for providing the medical images of the patients, Dr. Vladimir Brailovski for providing the models of the prototyped metallic prosthetics, and Drs. Roman Krawetz and Elena Di Martino for their consultations to this research project. Publication History Article published online: 16 September 2024 Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".