Evaluating the Biocompatibility of an Injectable Wound Matrix in a Murine Model
Bibliographic record
Abstract
(1) Background: Developing a high-quality, injectable biomaterial that is labor-saving, cost-efficient, and patient-ready is highly desirable. Our research group has previously developed a collagen-based injectable scaffold for the treatment of a variety of wounds including wounds with deep and irregular beds. Here, we investigated the biocompatibility of our liquid scaffold in mice and compared the results to a commercially available injectable granular collagen-based product. (2) Methods: Scaffolds were applied in sub-dermal pockets on the dorsum of mice. To examine the interaction between the scaffolds and the host tissue, samples were harvested after 1 and 2 weeks and stained for collagen content using Masson’s Trichrome staining. Immunofluorescence staining and quantification were performed to assess the type and number of cells infiltrating each scaffold. (3) Results: Histological evaluation after 1 and 2 weeks demonstrated early and efficient integration of our liquid scaffold with no evident adverse foreign body reaction. This rapid incorporation was accompanied by significant cellular infiltration of stromal and immune cells into the scaffold when compared to the commercial product (p < 0.01) and the control group (p < 0.05). Contrarily, the commercial scaffold induced a foreign body reaction as it was surrounded by a capsule-like, dense cellular layer during the 2-week period, resulting in delayed integration and hampered cellular infiltration. (4) Conclusion: Results obtained from this study demonstrate the potential use of our liquid scaffold as an advanced injectable wound matrix for the management of skin wounds with complex geometries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".