A fabricated hydrogel of hyaluronic acid/curcumin shows super-activity to heal the bacterial infected wound
Bibliographic record
Abstract
High risk of acute morbidities and even mortality from expanding the antibiotics resistant infectious wounds force indefinite efforts for development of high performance wound-healing materials. Herein, we design a procedure to fabricate a hyaluronic acid (HA)-based hydrogel to conjugate curcumin (Gel-H.P.Cur). The highlight of this work is to provide a favorite condition for capturing curcumin while protecting its structure and intensifying its activities because of the synchronization with HA. Accordingly, HA as a major component of dermis with a critical role in establishing skin health, could fortify the wound healing property as well as antibacterial activity of the hydrogel. Gel-H.P.Cur showed antibacterial properties against Pseudomonas aeruginosa (P. aeruginosa), which were examined by bactericidal efficiency, disk diffusion, anti-biofilm, and pyocyanin production assays. The effects of Gel-H.P.Cur on the inhibition of quorum sensing (QS) regulatory genes that contribute to expanding bacteria in the injured place was also significant. In addition, Gel-H.P.Cur showed high potential to heal the cutaneous wounds on the mouse excisional wound model with repairing histopathological damages rapidly and without scar. Taken together, the results strongly support Gel-H.P.Cur as a multipotent biomaterial for medical applications regarding the treatment of chronic, infected, and dehiscent 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) | 0.002 | 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".