3D Printing‐Based Hydrogel Dressings for Wound Healing
Bibliographic record
Abstract
Skin wounds have become an important issue that affects human health and burdens global medical care. Hydrogel materials similar to the natural extracellular matrix (ECM) are one of the best candidates for ideal wound dressings and the most feasible choices for printing inks. Distinct from hydrogels made by traditional technologies, which lack bionic and mechanical properties, 3D printing can promptly and accurately create hydrogels with complex bioactive structures and the potential to promote tissue regeneration and wound healing. Herein, a comprehensive review of multi-functional 3D printing-based hydrogel dressings for wound healing is presented. The review first summarizes the 3D printing techniques for wound hydrogel dressings, including photo-curing, extrusion, inkjet, and laser-assisted 3D printing. Then, the properties and design approaches of a series of bioinks composed of natural, synthetic, and composite polymers for 3D printing wound hydrogel dressings are described. Thereafter, the application of multi-functional 3D printing-based hydrogel dressings in a variety of wound environments is discussed in depth, including hemostasis, anti-inflammation, antibacterial, skin appendage regeneration, intelligent monitoring, and machine learning-assisted therapy. Finally, the challenges and prospects of 3D printing-based hydrogel dressings for wound healing are presented.
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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 | 0.002 | 0.002 |
| 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) | 0.004 | 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".