Cysteinyl‐leukotrienes and their receptors in asthma and other inflammatory diseases: Critical update and emerging trends
Bibliographic record
Abstract
Cysteinyl-leukotrienes (cysteinyl-LTs), that is, LTC4, LTD4, and LTE4, trigger contractile and inflammatory responses through the specific interaction with G protein-coupled receptors (GPCRs) belonging to the purine receptor cluster of the rhodopsin family, and identified as CysLT receptors (CysLTRs). Cysteinyl-LTs have a clear role in pathophysiological conditions such as asthma and allergic rhinitis (AR), and have been implicated in other inflammatory conditions including cardiovascular diseases, cancer, atopic dermatitis, and urticaria. Molecular cloning of human CysLT1R and CysLT2R subtypes has confirmed most of the previous pharmacological characterization and identified distinct expression patterns only partially overlapping. Interestingly, recent data provide evidence for the immunomodulation of CysLTR expression, the existence of additional receptor subtypes, and of an intracellular pool of CysLTRs that may have roles different from those of plasma membrane receptors. Furthermore, genetic variants have been identified for the CysLTRs that may interact to confer risk for atopy. Finally, a crosstalk between the cysteinyl-LT and the purine systems is being delineated. This review will summarize and attempt to integrate recent data derived from studies on the molecular pharmacology and pharmacogenetics of CysLTRs, and will consider the therapeutic opportunities arising from the new roles suggested for cysteinyl-LTs and their receptors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".