Severe sepsis cytomix elicits inflammation in cerebrovascular endothelial cells and polymorphonuclear (PMN) leukocytes in vitro.
Bibliographic record
Abstract
The mechanisms of severe sepsis induced activation/dysfunction of the cerebrovascular endothelium are poorly understood. Our findings indicate that severe sepsis in humans results in up‐regulation of 8 (p<0.01; out of 41 measured) inflammation‐relevant analytes in the blood plasma. We employed Severe sepsis Cytomix (SS‐CM) consisting of 8 cytokines/chemokines (at the levels detected in Severe sepsis‐plasma) to assess activation/dysfunction of human‐derived cerebrovascular endothelial cells (hCMEC/D3; provided by Dr. P.O. Couraud, INSERM) and neutrophilic leukocytes (PMN). Various inflammation‐relevant endpoints were assessed. The obtained results indicate that stimulation of hCMEC/D3 with SS‐CM failed to induce ROS production, activation of NF‐κB, and increase in hCMEC/D3 permeability; however ICAM‐1 gene expression was up‐regulated in hCMEC/D3. The latter was accompanied by increased PMN adhesion to hCMEC/D3 under conditions of “flow” (0.7 dyn/cm2 shear stress). The most potent increase in PMN adhesion, however, occurred when both PMN and hCMEC/D3 were stimulated with SS‐CM. PMN adhesions was prevented by interfering (neutralizing MAb) with β‐2‐integrin function. Taken together our findings indicate that increased adhesive interaction between circulating PMN and cerebrovascular endothelium may contribute to BBB dysfunction in severe sepsis via β‐2‐integrin. HSFO NA‐6914, PSI 11–01)
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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) | 0.001 | 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".