Secondary dengue virus infection is associated with endothelial activation and hypotension in an outpatient cohort from the Philippines
Bibliographic record
Abstract
Background Secondary dengue virus (DENV) infection is a known risk factor for severe clinical manifestations. Antibody-dependent enhancement of viral pathogenesis explains this phenomenon; however, the underlying mechanisms remain incompletely defined.Objectives To compare the frequency of hypotension, endothelial activation, systemic inflammation, and thrombocytopenia in patients with primary and secondary DENV infection.Methods This was a cross-sectional study among children and young adults aged 1–26 years conducted at an outpatient clinic in the Philippines. Secondary infection was defined by the presence of detectable anti-DENV IgG antibodies at presentation. Clinical data and haematologic parameters were recorded. Plasma concentration of circulating markers of endothelial activation and inflammation were quantified by Luminex® assay.Results Among 244 patients (median age 9 years, 40% female), 93 (38%) were IgG positive. Secondary infection was associated with a 2.2-fold increased odds (95% CI, 1.1–4.1) of hypotension compared to primary infection. Endothelial activation, quantified using a composite index of six endothelial markers (Ang1, Ang2, sTie2, sFlt1, sICAM1, and sEndoglin), was significantly higher in secondary infection (p < 0.001). Platelet counts were lower in secondary infection (170 × 109/L vs 230 × 109/L, p < 0.0001). IL-10 levels were elevated in secondary infection (76 pg/mL vs 33 pg/mL, p < 0.001). Systemic inflammation, quantified using a composite index of four plasma markers (TNF, CXCL8/IL-8, CXCL10/IP-10, PCT), correlated with endothelial activation (τ = 0.39, p < 0.001) and IL-10 (τ = 0.32, p < 0.0001).Conclusion Endothelial activation, IL-10-mediated immune dysregulation, and platelet depletion are associated with transient vascular hyperpermeability in secondary DENV infection.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".