Cysteinyl leukotriene 2 receptor‐mediated vascular permeability <i>via</i> transendothelial vesicle transport
Bibliographic record
Abstract
Cysteinyl leukotrienes (CysLTs) are potent mediators of inflammation synthesized by the concerted actions of 5-lipoxygenase (5-LO), 5-LO-activating protein (FLAP), leukotriene C(4) synthase, and additional downstream enzymes, starting with arachidonic acid substrate. CysLTs produced by macrophages, eosinophils, mast cells, and other inflammatory cells activate 3 different high-affinity CysLT receptors: CysLT(1)R, CysLT(2)R, and GPR 17. We sought to investigate vascular sites of CysLT(2)R expression and the role and mechanism of this receptor in mediating vascular permeability events. Vascular expression of CysLT(2)R was investigated by reporter gene expression in a novel CysLT(2)R deficient-LacZ mouse model. CysLT(2)R was expressed in small, but not large, vessels in mouse brain, bladder, skin, and cremaster muscle. Intravital, in addition to confocal and electron, microscopy investigations using FITC-labeled albumin in cremaster postcapillary venule preparations indicated rapid CysLT-mediated permeability, which was blocked by application of BAY-u9773, a dual CysLT(1)R/CysLT(2)R antagonist or by CysLT(2)R deficiency. Endothelial human CysLT(2)R overexpression in mice exacerbated vascular leakage even in the absence of exogenous ligand. The enhanced vascular permeability mediated by CysLT(2)R takes place via a transendothelial vesicle transport mechanism as opposed to a paracellular route and is controlled via Ca(2+) signaling. Our results reveal that CysLT(2)R can mediate inflammatory reactions in a vascular bed-specific manner by altering transendothelial vesicle transport-based vascular permeability.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".