Biomarkers of endothelial activation/dysfunction in infectious diseases
Bibliographic record
Abstract
Endothelial dysfunction contributes to the pathogenesis of a variety of potentially serious infectious diseases and syndromes, including sepsis and septic shock, hemolytic-uremic syndrome, severe malaria, and dengue hemorrhagic fever. Because endothelial activation often precedes overt endothelial dysfunction, biomarkers of the activated endothelium in serum and/or plasma may be detectable before classically recognized markers of disease, and therefore, may be clinically useful as biomarkers of disease severity or prognosis in systemic infectious diseases. In this review, the current status of mediators of endothelial cell function (angiopoietins-1 and -2), components of the coagulation pathway (von Willebrand Factor, ADAMTS13, and thrombomodulin), soluble cell-surface adhesion molecules (soluble E-selectin, sICAM-1, and sVCAM-1), and regulators of vascular tone and permeability (VEGF and sFlt-1) as biomarkers in severe infectious diseases is discussed in the context of sepsis, E. coli O157:H7 infection, malaria, and dengue virus infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".