Combining microfluidics and coaxial 3D-bioprinting for the manufacturing of diabetic wound healing dressings
Bibliographic record
Abstract
Diabetic foot ulcers (DFUs) are a crucial complication of diabetes, as in a diabetic wound, each step of the physiological healing process is affected. This entails a more easily infectable wound, and delayed tissue regeneration due to the inflammation that occurs, leading to a drastic decrease in the overall patient's quality of life. As a strategy to manage DFUs, skin alternatives and wound dressings are currently receiving a lot of attention as they keep the wound environment "under control", while providing bioactive compounds that help to manage infection and inflammation and promote tissue repair. This has been made possible thanks to the advent of emerging technologies such as 3D Bioprinting to produce skin resembling constructs or microfluidics (MFs) that allows the manufacture of nanoparticles (NPs) that act as drug carriers, in a prompt and less expensive way. In the present proof-of-concept study, the possibility of combining two novel and appealing techniques in the manufacturing of wound dressings has been demonstrated for first time. The novelty of this work consists in the combination of liposomes (LPs) encapsulating the active pharmaceutical ingredient (API) into a hydrogel that is further printed into a three-dimensional scaffold for wound dressing; to the knowledge of the authors this has never been done before. A grid-shaped scaffold has been produced through the coaxial 3D bioprinting technique which has allowed to combine, in one single filament, two different bioinks. The inner core of the filament is a nanocomposite hydrogel consisting of hydroxyethyl cellulose (HEC) and PEGylated LPs encapsulated with thyme oil (TO) manufactured via MFs for the first time. The outer shell of the filament, instead, is represented by a hybrid hydrogel composed of sodium alginate/cellulose nanocrystals (SA/CNC) and enriched with free TO. This provides a combination of two different release ratios of the API, a bulk release for the first 24 h thanks to the free TO in the shell of the filament and a sustained release for up to 10 days provided from the API inside the LPs. Confocal Microscopy verified the actual presence of the LPs inside the scaffold after printing and evaluation using the zone of inhibition test proved the antibacterial activity of the manufactured scaffolds against both Gram-positive and Gram-negative bacteria.
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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) | 0.000 | 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) | 0.001 | 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".