Azithromycin Differentially Alters TCR-Activated Helper T Cell Subset Phenotype and Effector Function
Bibliographic record
Abstract
In addition to their antibiotic activities, azithromycin (AZM) exhibits anti-inflammatory effects in various respiratory diseases. One of the potent anti-inflammatory mechanisms is through inhibition of CD4+ helper T (Th) cell effector function. However, their impact on specific Th subset is obscure. Herein, we demonstrate the cellular basis of phenotypic and functional alterations associated with Th subsets following AZM treatment in vitro. Using well-characterized Th subset specific chemokine receptors, we report significant suppression of T cell receptor (TCR)-stimulated hyperactivated CCR4+CXCR3+ (Th0) expansion compared to CCR4-CXCR3+ (Th1-like) and CCR4+CXCR3- (Th2-like) cells. Interestingly, this effect was associated with diminished cell proliferation. Furthermore, AZM significantly inhibited the inflammatory cytokines IFN- and IL-4 production, CCR4 and CXCR3 receptor expression, and viability of Th0, Th1-like and Th2-like subsets. Our findings suggest that AZM differentially affects TCR-activated Th subsets phenotype and function, and CCR4 and CXCR3 downregulation and suppressed Th0 subset expansion could potentially influence their trafficking and differentiation into cytokine-producing effector 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".