Human pulmonary endothelial cell permeability after exposure to LPS-stimulated leukocyte supernatants derived from patients with early sepsis
Bibliographic record
Abstract
Systemic immune activation is the hallmark of sepsis, which can result in endothelial injury and the acute respiratory distress syndrome (ARDS). The aim of this study was to investigate heterogeneity in sepsis-mediated endothelial permeability using primary human pulmonary microvascular endothelial cells (HPMECs) and the electric cell-substrate impedance sensing (ECIS) platform. After plasma removal, cellular component of whole blood from 35 intensive care unit (ICU) patients with early sepsis was diluted with media and stimulated with either lipopolysaccharide (LPS) or control media. Resulting supernatants were cocultured with HPMECs seeded on ECIS plates, and resistance was continually measured. A decrease in resistance signified increased permeability. After incubation, HPMECs were detached and cell adhesion proteins were quantified using flow cytometry and immunohistochemistry, and gene expression was analyzed with quantitative PCR. Significant heterogeneity in endothelial permeability after exposure to supernatants of LPS-stimulated leukocytes was identified. ICU patients with sepsis stratified into one of the following three groups: minimal (9/35, 26%), intermediate (18/35, 51%), and maximal (8/35, 23%) permeability. Maximal permeability was associated with increased intercellular adhesion molecule-1 protein and mRNA expression and decreased vascular endothelial-cadherin mRNA expression. These findings indicate that substantial heterogeneity in pulmonary endothelial permeability is induced by supernatants of LPS-stimulated leukocytes derived from patients with early sepsis and provide insights into some of the mechanisms that induce lung vascular injury. In addition, this in vitro model of lung endothelial permeability from LPS-stimulated leukocytes may be a useful method for testing therapeutic agents that could mitigate endothelial injury in early sepsis.
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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 | 0.000 |
| 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".