Red blood cells induce lung inflammation in hypoxia
Bibliographic record
Abstract
Red blood cells (RBCs) determine systemic vascular tone by releasing vasoactive factors. To determine RBC‐induced lung responses, we inflated isolated, blood‐perfused lungs of rat or mouse with normoxic or hypoxic gas. Corresponding blood pO2 were 150 and 33 mmHg. Using our reported methods (J. Clin. Invest. 2003. 111: –699), we quantified fluorescence in single endothelial cells (ECs) of lung capillaries by real‐time fluorescence imaging. Using the dichloroluorescin (DCF) and fura 2 methods respectively, we detected EC ROS and EC cytosolic Ca2+ (Ca2+cyt) levels. In the presence of RBC‐containing perfusion, hypoxic inflation increased both ROS and Ca2+cyt above baseline (P<0.05). However, both increases were blocked during RBC‐free perfusion, or by inclusion of the H2O2 hydrolyzing agent, catalase in RBC‐containing perfusion. The wild type hypoxic response caused leukocyte recruitment that was inhibited by separate infusions of RBC‐free plasma, catalase, or of a blocking mAb against the leukocyte adhesion receptor, P‐selectin (P<0.05). Perfusion with RBC from BERK‐trait mice, in which hypoxia enhances RBC superoxide production, enhanced both hypoxia‐induced EC ROS and the induced leukocyte recruitment (P<0.05). We conclude that in hypoxia, RBCs generate H2O2 that induces Ca2+‐dependent EC P‐selectin expression, leading to leukocyte recruitment, hence initiation of inflammation (HL69514).
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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 | 0.000 |
| 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) | 0.002 | 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".