Effects of montelukast on burn wound healing in a rat model
Bibliographic record
Abstract
PURPOSE: Montelukast, a selective cysteinyl leukotriene D4-receptor antagonist, is used in the treatment of asthma. In a rat model, our aim was to investigate the effects of montelukast, alone or in combination with topical antibiotics, on local burn wound healing. METHODS: Rats were randomly allocated to four groups after local burn development: Group 1; rats were left to secondary healing without treatment, Group 2; a dose of 10 mg/kg montelukast was given by gastric gavage once a day for 10 days, Group 3; rats were treated with topical pomade (bacitracin neomycin sulphate), and Group 4; rats were treat with a combination of topical antibiotic and montelukast (10 mg/kg were given by gastric gavage once a day for 10 days). Skin biopsies were taken on days 3, 10, 14, and 20 relative to burn induction. RESULTS: Reepithelialization in the pomade and montelukast+pomade groups on the 10th day was significantly greater, in comparison with control and montelukast groups (p < 0.05). For the montelukast group, edema (on the 14th day) and angiogenesis, fibroblast proliferation, edema and macrophage infiltration (on the 20th day) were statistically improved in comparison with the control group (p < 0.05). For the montelukast+pomade group, angiogenesis, fibroblast proliferation and macrophage infiltration (on the 10th day), and angiogenesis, fibroblast proliferation, edema and macrophage infiltration (on the 14th and 20th days) were statistically improved in comparison with the control group (p < 0.05). CONCLUSION: In conclusion, montelukast was effective on burn wound healing. Moreover, the effect was amplified when combined with topical antibiotics applied in the early stage of burn wound healing.
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) | 0.001 | 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 | 0.001 |
| 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".