Blockade of TSLP pathway alters asthma biomarkers
Bibliographic record
Abstract
Thymic stromal lymphopoietin (TSLP), an epithelial cell-derived cytokine produced in response to pro-inflammatory stimuli, drives allergic inflammatory responses mostly through its effect on dendritic and mast cells. TSLP reportedly is increased in the airways of asthmatic subjects. AMG 157 is a human anti-TSLP monoclonal antibody that blocks interaction between TSLP and its receptor. The goal of this study was to evaluate AMG 157 effects on asthma biomarkers in mild atopic asthmatic subjects after an inhaled allergen challenge. The study consisted of 31 subjects who received three doses, at monthly intervals, of 700mg AMG 157 (n=16) or placebo (n=15). Allergen challenges were performed pre-dose, and 6 and 12 weeks after initiation of dosing. Sputum eosinophil counts, blood mRNA and fractional exhaled nitric oxide (FeNO) were collected before and after allergen challenge and blood eosinophil counts and Th2/Th1 ratio were followed prior to allergen challenge through the treatment period. AMG 157 attenuated allergen-induced airway responses. Pre-allergen FeNO and blood and sputum eosinophil counts decreased significantly with AMG 157. In whole blood transcript analysis, genes highly expressed in eosinophils were elevated after allergen challenge. Pre-dose gene expression and eosinophil count were well-correlated. However, no AMG 157 effects on blood transcript profiles were detected. Treatment with AMG 157 was associated with a decreased Th2/Th1 cell ratio, driven mostly by a decrease in Th2 cells. The reduction in these markers of airway and systemic inflammation in AMG 157-treated subjects supports a role of TSLP in allergen-induced responses in patients with allergic asthma.
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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) | 0.001 | 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) | 0.001 | 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".