Surface slicing and toolpath planning for in-situ bioprinting of skin implants
Bibliographic record
Abstract
Abstract Bioprinting has emerged as a successful method for fabricating engineered tissue implants, offering great potential for wound healing applications. This study focuses on an advanced surface-based slicing approach aimed at designing a skin implant specifically for in-situ bioprinting. The slicing step plays a crucial role in determining the layering arrangement of the tissue during printing. By utilizing surface slicing, a significant shift from planar fabrication methods is achieved. The developed methodology involves the utilization of a customized robotic printer to deliver biomaterials. A multilayer slicing and toolpath generation procedure is presented, enabling the fabrication of skin implants that incorporate the epidermal, dermal, and hypodermal layers. One notable advantage of using the approximate representation of the native wound site surface as the slicing surface is the avoidance of planar printing effects such as staircasing. This surface slicing method allows for the design of non-planar and ultra-thin skin implants, ensuring a higher degree of geometric match between the implant and the wound interface. Furthermore, the proposed methodology demonstrates superior surface quality of the in-situ bio-printed implant on a hand model, validating its ability to create toolpaths on implants with complex surfaces.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".