Vascular endothelial growth factor is an important determinant of sepsis morbidity and mortality
Bibliographic record
Abstract
Sepsis, the systemic inflammatory response to infection, is a leading cause of morbidity and mortality. The mechanisms of sepsis pathophysiology remain obscure but are likely to involve a complex interplay between mediators of the inflammatory and coagulation pathways. An improved understanding of these mechanisms should provide an important foundation for developing novel therapies. In this study, we show that sepsis is associated with a time-dependent increase in circulating levels of vascular endothelial growth factor (VEGF) and placental growth factor (PlGF) in animal and human models of sepsis. Adenovirus-mediated overexpression of soluble Flt-1 (sFlt-1) in a mouse model of endotoxemia attenuated the rise in VEGF and PlGF levels and blocked the effect of endotoxemia on cardiac function, vascular permeability, and mortality. Similarly, in a cecal ligation puncture (CLP) model, adenovirus-sFlt-1 protected against cardiac dysfunction and mortality. When administered in a therapeutic regimen beginning 1 h after the onset of endotoxemia or CLP, sFlt peptide resulted in marked improvement in cardiac physiology and survival. Systemic administration of antibodies against the transmembrane receptor Flk-1 but not Flt-1 protected against sepsis mortality. Adenovirus-mediated overexpression of VEGF but not PlGF exacerbated the lipopolysaccharide-mediated toxic effects. Together, these data support a pathophysiological role for VEGF in mediating the sepsis phenotype.
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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 | 0.001 |
| 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".