Host biomarkers are associated with progression to dengue haemorrhagic fever: a nested case-control study
Bibliographic record
Abstract
OBJECTIVES: Dengue represents the most important arboviral infection worldwide. Onset of circulatory collapse can be unpredictable. Biomarkers that can identify individuals at risk of plasma leakage may facilitate better triage and clinical management. DESIGN: Using a nested case-control design, we randomly selected subjects from a prospective cohort study of dengue in Colombia (n=1582). Using serum collected within 96 hours of fever onset, we tested 19 biomarkers by ELISA in cases (developed dengue hemorrhagic fever or dengue shock syndrome (DHF/DSS); n=46), and controls (uncomplicated dengue fever (DF); n=65) and healthy controls (HC); n=15. RESULTS: Ang-1 levels were lower and angptl3, sKDR, sEng, sICAM-1, CRP, CXCL10/IP-10, IL-18 binding protein, CHI3L1, C5a and Factor D levels were increased in dengue compared to HC. sICAM-1, sEng and CXCL10/IP-10 were further elevated in subjects who subsequently developed DHF/DSS (p=0.008, p=0.028 and p=0.025, respectively). In a logistic regression model, age (odds ratio (OR) (95% CI): 0.95 (0.92-0.98), p=0.001), hyperesthesia/hyperalgesia (OR; 3.8 (1.4-10.4), p=0.008) and elevated sICAM-1 (>298ng/mL: OR; 6.3 (1.5-25.7), p=0.011) at presentation were independently associated with progression to DHF/DSS. CONCLUSIONS: These results suggest that inflammation and endothelial activation are important pathways in the pathogenesis of dengue and sICAM-1 levels may identify individuals at risk of plasma leakage.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".