Thymic stromal lymphopoietin: its role and potential as a therapeutic target in asthma
Bibliographic record
Abstract
INTRODUCTION: Thymic stromal lymphopoietin (TSLP), an epithelial cytokine (alarmin), is a central regulator of the immune response to inhaled environmental insults such as allergens, viruses and pollutants, initiating a cascade of downstream inflammation. There is compelling evidence that TSLP plays a major role in the pathology of asthma, and therapies that aim to block its activity are in development. AREAS COVERED: We review studies conducted in humans and human cells, largely published in PubMed January 2010-October 2019, that investigated the innate and adaptive immune mechanisms of TSLP in asthma relevant to type 2-driven (eosinophilic/allergic) inflammation and non-type 2-driven (non-eosinophilic/non-allergic) inflammation, and the role of TSLP as a mediator between immune cells and structural cells in the airway. Clinical data from studies evaluating TSLP blockade are also discussed. EXPERT OPINION: The position of TSLP at the top of the inflammatory cascade makes it a promising therapeutic target in asthma. Systemic anti-TSLP monoclonal antibody therapy with tezepelumab has yielded positive results in clinical trials to date, reducing exacerbations and biomarkers of inflammation in patients across the spectrum of inflammatory endotypes. Inhaled anti-TSLP is an alternative route currently under evaluation. The long-term safety and efficacy of TSLP blockade need to be evaluated.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".