Inflammatory response is elicited in human cerebrovascular endothelial cells stimulated with blood plasma obtained from Severe Sepsis patients
Bibliographic record
Abstract
Mechanisms of sepsis‐associated encephalopathy (SAE) are largely un‐investigated, however, impaired function of cerebrovascular endothelium has been suggested to play a key role. Our recent findings indicate that Severe Sepsis in humans results in up‐regulation of 13 out of 42 pro‐inflammatory cytokines/chemokines analyzed in blood plasma (e.g. IL‐6, MCP‐1, MIP‐1, IL‐8, IP‐10). In this study we used immortalized human‐derived cerebrovascular endothelial cells (hCVEC) to assess activation/dysfunction of hCVEC in response to stimulation with plasma (20% vol/vol) obtained from either patients with Severe Sepsis (hSSP) or healthy controls. Stimulation of hCVEC with hSSP for 1hr resulted in hCVEC activation as evidenced by increased ROS production (DHR123 oxidation). Subsequently, stimulation of hCVEC with hSSP resulted in increased PMN adhesion to hCVEC under conditions of flow (shear stress 1dyn/cm 2 ) and increased hCVEC permeability as assessed by decreased transendothelial electric resistance. Interestingly, hSSP failed to induce activation of inflammation‐relevant transcription factor, NF‐κB (ELISA) following 1hr stimulation. In summary, our data indicate that pro‐inflammatory substance(s) present in human Severe Sepsis plasma induce activation/dysfunction of cerebrovascular endothelial cells, thus may contribute to the development of SAE (IRF 08‐10)
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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".