Bioprinting-By-Design of Hydrogel-Based Biomaterials for In Situ Skin Tissue Engineering
Bibliographic record
Abstract
Burns are one of the most common trauma injuries worldwide and have detrimental effects on the entire body. However, the current standard of care is autologous split thickness skin grafts (STSGs), which induces additional injuries to the patient. Therefore, the development of alternative treatments to replace traditional STSGs is critical, and bioprinting could be the future of burn care. Specifically, in situ bioprinting offers several advantages in clinical applications compared to conventional in vitro bioprinting, primarily due to its ability to deposit bioink directly onto the wound. This review provides an in-depth discussion of the aspects involved in in situ bioprinting for skin regeneration, including crosslinking mechanisms, properties of natural and synthetic hydrogel-based bioinks, various in situ bioprinting methods, and the clinical translation of in situ bioprinting. The current limitations of in situ bioprinting is the ideal combination of bioink and printing mechanism to allow multi-material dispensing or to produce well-orchestrated constructs in a timely manner in clinical settings. However, extensive ongoing research is focused on addressing these challenges, and they do not diminish the significant potential of in situ bioprinting for skin regeneration.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".