A Study on the Mechanisms by Which AGEs and RAG Promote Wound Healing in Patients with Second-Degree Burns undergoing Eschar Grinding Combined with Drug Therapy
Bibliographic record
Abstract
Objective: To evaluate the improvement effect of combined treatment of eschar abrasion, nanosilver dressing, and mussel mucin spray on wound healing in patients with second-degree burns, and to explore their effects on the expression level of (advanced glycation end products) AGEs in wound tissue, so as to provide a basis for the application of AGEs expression level in wound tissue in the future clinical treatment of second-degree burns. Methods: Patients with second-degree burns admitted to the Department of Burns and Plastic Surgery of the Second Affiliated Hospital of Shantou University Medical College from July 2023 to July 2024 were selected as the research subjects. This study was a non-double-blind study, and both patients and researchers were aware of the treatment methods. They were randomly divided into Group A (control group) and Group B (study group). According to the order of their visits, the patients were numbered in advance; then the seed number was taken, and 70 random numbers were generated on the computer using SAS, the first 35 corresponding to group A, and the last 35 corresponding to group B; the random numbers were arranged from small to large, and the rank of the random numbers was the patient number; finally, the patient numbers were arranged from small to large, and the corresponding groups were the grouping scheme. 1) Wound healing time: The wound healing was observed every day, and the wound healing time was calculated when the wound was completely epithelialized. 2) Wound healing: The wound healing area was measured 1, 7, and 14 days after treatment, and transparent paper was used to record combined with a computer-assisted imaging system. Wound healing rate = wound healing area (cm2)/total wound area before treatment (cm2) × 100%. The time for complete wound healing was recorded in the two groups of patients. 3) Pain: The pain was evaluated at 1, 7, and 14 days after treatment using the visual analogue scale (VAS). The higher the score, the more severe the pain. 4) Scar condition: Scar formation was evaluated 1, 3, and 6 months after wound healing using the VAS scale, with a total score of 15 points. The higher the score, the more severe the scar. 5) Detect the expression of AGEs in wound tissue. Results: The wound healed 14.03 ± 2.28 days after eschar removal, with a cure rate of 97.8%. No infection occurred in the wound after eschar removal in all patients. The wound healing time ranged from 9 to 23 days, with an average healing time of 28.41 ± 1.45 days. The healing quality was satisfactory. The Vancouver Scar Scale scored the wound healing scar as 0.81 ± 0.73 points. Western blot was used to detect the expression of AGEs, (receptor for advanced glycation end products) RAGE, and protein in the wound tissue. Results After 7, 14, and 28 days of medication, the wounds of diabetic patients healed well. The drug treatment efficacy and hydroxyproline content showed an upward trend, and the expression levels of AGEs, RAGE, and HIF-1a proteins showed a downward trend. Compared with day 0, the hydroxyproline content of the wound granulation tissue on days 7, 14, and 28 was significantly increased (P≪ 0.01), and the expression levels of AGEs, RAGE, and HIF-1α proteins were significantly downregulated (P≪ 0.05 or P≪ 0.01). Conclusion: Monitoring the expression levels of AGEs and RAGE can reflect the wound-healing effect of patients with second-degree burns, and the prognosis of the wound is closely related to the expression levels of AGEs, RAGE, and scab abrasion. In the treatment of second-degree burn wounds, scab abrasion can grasp the level of necrotic scab removal, can achieve “relatively accurate” removal of necrotic tissue, maximally protect the ecological tissue between wounds and retaining normal tissue, and can play a positive role in promoting the healing process of burn wounds; at the same time, nanosilver dressings have good antibacterial properties and high safety, and mussel mucin has anti-inflammatory and antioxidant activities and the characteristics of blocking nerve endings, which can effectively relieve the burning, stinging, and itching of sensitive skin and effectively promote wound healing.
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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) | 0.001 | 0.001 |
| 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".