Solid emulsion gel as a vehicle for delivery of polyunsaturated fatty acids: implications for tissue repair, dermal angiogenesis and wound healing
Bibliographic record
Abstract
The paper describes preparation and biological characterization of the solid hybrid biomaterial that was designed for cell-targeted lipid delivery in healing tissues. The material referred to as 'solid emulsion gel' combines a protein-stabilized lipid emulsion and a hydrogel structure in a single compartment. The potential of the omega-3 (n-3)-fatty acids rich solid emulsion gel for tissue repair applications was investigated at the macro-, micro-, molecular and gene expression levels, using human fibroblasts and endothelial cells and a porcine model of full-thickness wounds. Being non-cytotoxic in vitro and in vivo, the biomaterial was found to affect cell metabolism, modulate expression of certain genes, stimulate early angiogenesis and promote wound repair in vivo. The neovascular response in vivo was correlated with upregulated expression of the genes involved in lipid transport (e.g. adipophilin), anti-apoptosis (e.g. heat shock proteins, haem oxygenase 1) and angiogenesis (vascular endothelial growth factor, placental growth factor). Collectively, the results of this study provide first evidence that the angiogenic response provided by solid emulsion gel-mediated delivery of n-3 fatty acids is an alternative to the topical administration of exogenous growth factors or gene therapy, and can be advantageously used for the stimulation of tissue repair in complex wounds.
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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".