Effects of Macrolide Antibiotics on Th1 Cell and Th2 Cell Development Mediated by Langerhans Cells
Bibliographic record
Abstract
- Background: It is well known that Langerhans cells (LCs) work as the primary orchestrators in the polarization of the immune milieu towards a T helper type 1 (Th1) or a Th2 immune response. In this study, we investigated the effects of macrolide antibiotics on Th1 cell and Th2 cell development mediated by LCs. METHODS: LC-like dendritic cells (LDCs) were generated from mouse bone marrow cells and used as substitutes for LCs. Mice were primed with ovalbumin (OVA) peptide-pulsed LDCs, which had been treated with each macrolide antibiotic, via the hind footpad. After 5 days, the cytokine response in the popliteal lymph nodes was investigated by enzyme-linked immunosorbent assay. The expression of cell surface molecules on LDCs was investigated using reverse transcriptase polymerase chain reaction. RESULTS: Injection of OVA peptide-pulsed LDCs, which had been treated with josamycin or spiramycin, inhibited Th2 cell development as represented by down-regulation of interleukin (IL)-4 production as well as Th1 cell development as represented by down-regulation of interferon (IFN)-g production. This inhibition of Th1 cell and Th2 cell development was associated with suppression of CD86 and T-cell immunoglobulin and mucin domain-containing protein (TIM)-4 expression, respectively, in LDCs. Furthermore, Staphylococcus aureus strains isolated from skin lesions of patients with atopic dermatitis (AD) were more susceptible to josamycin than to spiramycin. CONCLUSIONS: These results suggest that topical application of josamycin to AD lesions colonized with S. aureus would be beneficial for control of AD by acting on both superficial S. aureus and epidermal LCs, and inhibiting the development of Th2 cells.
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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) | 0.001 | 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".