Mast cell mediators in relation to dengue severity: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Degranulation of mast cells (MCs) releases several mediators such as vascular endothelial growth factor (VEGF), chymase, tryptase, histamine, and cytokines, which all have important roles in the severity of dengue infection. We aimed to investigate the role of MCs in severity of dengue. METHODS: We searched for relevant studies in 10 databases on 15 August 2016. Meta-analysis (MA) was conducted by R version 3.5.0. RESULTS: We included 24 studies. in vivo and in vitro studies showed higher MC products released from infected mice/cells with dengue virus. In addition, when administering MC stabilizers or antihistaminic drugs, there was a decrease in vascular/capillary permeability. In human and at early stages, studies revealed an insignificant difference in VEGF levels in dengue fever (DF) versus dengue hemorrhagic fever (DHF) (standardized mean difference [SMD] 0.145; 95% confidence interval [CI], -0.348-0.638). Meanwhile, at acute stages and compared with healthy controls, high heterogeneity with an inconclusive difference in VEGF levels were noted in DF and DHF. However, pooled serum and plasma levels of VEGF were increased significantly in dengue shock syndrome (DSS) versus healthy controls (SMD 0.65; 95% CI, 0.3-0.95). There were also significantly higher chymase levels in DHF patients compared with DF during the acute phase (MD -6.531; 95% CI, -12.2 to -0.9). CONCLUSION: VEGF and chymase levels are mediators in dengue pathogenesis. However, limited data were available to support their role in severe dengue cases. Further studies are needed to evaluate the function of other mediators in dengue severity.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".