Cysteinyl leukotriene receptor 2 (CysLT2R)‐mediated vascular permeability and ischemia/reperfusion injury
Bibliographic record
Abstract
Cysteinyl leukotrienes (CysLTs) are arachidonate‐derived lipid mediators of inflammation that act via CysLT receptors. Previously, we demonstrated that transgenic overexpression of the human CysLT2R in endothelium (hEC) exacerbates vascular permeability and inflammatory response activation. However, we could not delineate the relative contributions of endogenous mouse CysLT2R and the transgene‐derived receptor. Thus, we created a novel mouse strain (hECxKO) by crossing hEC mice with CysLT2R knockout mice in order to examine the specific roles of CysLT2R tissue‐localized expression niches in inflammation and tissue injury. Surprisingly, in contrast to our previous findings where myocardial ischemia/reperfusion injury was markedly enhanced in hEC mice compared to wildtype (WT) mice (47.3 ± 2.0% to 25.2 ± 3.5%), this was not the case with hECxKO mice, where injury was comparable to WT mice (24.0 ± 3.0%). Furthermore, in contrast to hEC and WT mice, hECxKO mice displayed altered leukotriene‐mediated vascular permeability responses, measured as FITC‐albumin leakage, in a cremaster muscle venule intravital imaging system. The data indicate that endothelial‐expressed CysLT2R mediates only part of the post‐injury inflammatory response to leukotrienes and that CysLT2R expressed in other uncharacterized sites is important. This work was supported by the CIHR and a Career Investigator award from the HSFO.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".