Hydrogels based on galactomannan and κ-carrageenan containing immobilized biomolecules for in vivo thermal-burn wound treatment
Bibliographic record
Abstract
, as a matrix for immobilizing lactoferrin and/or Cramoll, aiming at its applicability as dressings for second-degree burn wounds. The formulations obtained [H - hydrogel, HL - hydrogel + lactoferrin, HC - hydrogel + Cramoll and HLC - hydrogel + lactoferrin + Cramoll] were analyzed rheologically as well as in terms of their stability (pH, color, microbial contamination) for 90 days. The burn was created with an aluminum bar (97 ± 3 °C) in the dorsal region of Wistar rats and subsequently treated with hydrogels (H, HL, HC, HLC) and control saline solution (S). The burn was monitored for 3, 7 and 14 days to evaluate the efficacy of the hydrogels in promoting wound healing. The hydrogels did not reveal significant pH or microbiological changes; there was an increase in brightness and a reduction in opacity for H. The rheological analysis confirmed the gel-like viscoelastic signature of the systems without substantial modification of the basic rheological characteristics, however HLC proved to be more rigid, due to rheological synergy when combining protein biomolecules. Macroscopic analyses confirmed centripetal healing with wound contraction: S < H < HC < HL < HLC. Histopathological analyses showed that hydrogel-treated groups reduced inflammation, tissue necrosis and fibrosis, while promoting re-epithelialization with focal acanthosis, especially in HLC due to a positive synergistic effect, indicating its potential as a promising therapy in the repair of burns.
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 | 0.000 | 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".