Mediators Released from LPS‐challenged Lungs into Circulation Induce the Inflammatory Response in Liver Vascular Endothelial Cells
Bibliographic record
Abstract
Acute respiratory distress syndrome (ARDS) contributes significantly to the development of systemic inflammation and subsequent multiple organ dysfunction syndrome (MODS). However, the role of lung‐derived inflammatory mediators on modulation of the inflammatory response in remote organs is poorly understood. In this study, lung inflammation in mice was induced by intranasal LPS (1mg/kg) administration. Four hours later, the isolated‐perfused mouse lung approach was employed to collect mediator(s) secreted by the inflamed lung into systemic circulation (pulmonary circulation perfusate). Perfusate was used to investigate the effects of lung mediators on induction of the inflammatory response in the liver. To this end, primary mouse liver vascular endothelial cells (LVEC) in vitro were stimulated with perfusate and assessed for expression of the pro‐inflammatory phenotype. Exposure of LVEC to perfusate from LPS‐challenged lungs for 4 h resulted in: 1) increased ROS production (Lucigenin assay), 2) activation of NF¿B (Immunocytochemistry), 3) up‐regulation of pro‐adhesive phenotype (E‐selectin, ICAM‐1 and VCAM‐1 expression; RT‐PCR, cell ELISA) and 4) increased 51 Cr‐neutrophil adhesion to LVEC. These data provide with insight into the molecular mechanisms associated to lung‐induced remote organ (liver) injury during ARDS/MODS (HSFO NA6171, MOP 11666).
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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".