Reduced T‐bet in addition to enhanced <scp>STAT</scp>6 and <scp>GATA</scp>3 expressing T cells contribute to human allergen‐induced late responses
Bibliographic record
Abstract
BACKGROUND: T-bet and GATA-3 are transcriptional factors involved in Th1 and Th2 cell differentiation, although their concomitant roles at protein levels in target organs during human allergic disease have not been assessed. OBJECTIVES: We investigated the expression of T-bet and GATA-3 in nasal and cutaneous models of Th2 (grass-pollen allergen) and a cutaneous model of Th1 (PPD) responses in man. METHODS: Nasal biopsies were obtained at 8 h and skin biopsies at 8 and 48 h after allergen and PPD challenges, respectively, from 10 allergic rhinitics and 6 non-atopic controls. T cells were assessed using immunofluorescence microscopy. RESULTS: There were increases in CD3(+)STAT6(+)cells (P = 0.01 for nose and skin) and CD3(+)GATA3(+)cells (P = 0.03 for skin) in response to allergen compared with diluent in allergics. When compared with non-atopics after allergen challenge the difference between the two groups was also significant for CD3(+)STAT6(+) (P = 0.001 and 0.03) and for CD3(+)GATA3(+)cells (P = 0.04 and 0.001) for nose and skin respectively. Following PPD challenge CD3(+)STAT4(+)cells and CD3(+)T-bet(+)cells increased in both groups compared with diluent (P = 0.02 and 0.03 for both TFs), whereas only CD3(+)T-bet(+) cells were significantly greater in non-atopics compared with allergics (P = 0.04). The ratio of GATA3(+):T-bet(+) T cells in allergen-induced responses was significantly greater in the allergics (P = 0.008 and 0.01 nose and skin respectively), whereas the ratio of T-bet:GATA3(+)T cells was significantly higher in the non-atopics during PPD-induced responses (P = 0.003). CONCLUSIONS AND CLINICAL RELEVANCE: Dysregulation of Th1 transcription may contribute to heightened expression of STAT6 and GATA3 leading to exaggerated Th2-driven manifestations of allergic disease.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".