Effects of dual therapy with betamethasone and tetracycline in a NC/Nga mouse model of atopic dermatitis
Bibliographic record
Abstract
Purpose: Our previous study has demonstrated that tetracycline exerts excellent bactericidal activity against Staphylococcus aureus strains isolated from patients with atopic dermatitis (AD) while simultaneously inhibiting the development of T helper (Th) type 2 (Th2) cells. The present study was designed to evaluate the effectiveness of dual therapy with betamethasone and tetracycline for AD. Methods: Betametasone (0.1%) and tetracycline (3%) were topically administered to NC/Nga mice with AD-like skin lesions. Skin severity scores, histological changes to the lesioned skin, and serum IgE levels were assessed as indicators of therapeutic effectiveness. Results: Topical treatment with both drugs reduced the skin severity score more significantly than was the case with betamethasone alone or tetracycline alone. This was associated with a reduction in the degree of epidermal thickening, the density of cellular infiltration into the dermis, the mast cell count in the dermis and the serum IgE concentration. Furthermore, the degree of Th1/Th2 cell development in auricular lymph nodes and the S. aureus count on the lesioned skin were synergistically suppressed by simultaneous application of both drugs. Conclusion: The present results show that simultaneous topical application of betamethasone and tetracycline synergistically ameliorates AD-like skin lesions in NC/Nga mice. This suggests that dual therapy with betamethasone and tetracycline for AD lesions colonized by S. aureus might be one of the best options for inhibiting the development of both Th1 and Th2 cells and acting on superficially located S. aureus.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".