Analysis of cytokine profiles in pediatric fulminant myocarditis and acute myocarditis multicenter study
Bibliographic record
Abstract
Abstract Introduction: Acute myocarditis (AM) is an inflammatory disease of the heart muscle that can progress to fulminant myocarditis (FM), a severe and life-threatening condition. The cytokine profile of myocarditis in children, especially in relation to fulminant myocarditis, is not well understood. This study aims to evaluate the cytokine profiles of acute and fulminant myocarditis in children. Method: Pediatric patients diagnosed with myocarditis were included in the study. Cytokine levels were measured using a multiplexed fluorescent bead-based immunoassay. Statistical analysis was performed to compare patient characteristics and cytokine levels between FM, AM, and healthy control (HC) groups. Principal component analysis (PCA) was applied to cytokine groups that were independent among the FM, AM, and HC groups. Result: The study included twenty-two patients with FM and fourteen with AM patients. We identified 4 cytokines that were significantly higher in the FM group compared to the AM group: IL1-RA (p=0.002), IL-8 (p=0.005), IL-10 (p=0.011), and IL-15 (p=0.005). IL-4 was significantly higher in the AM group compared to FM and HC groups (p=0.006, and 0.0015). PDGF-AA, and VEGF-A were significantly lower in the FM group than in the AM group (p=0.013, and <0.001). Similar results were obtained in PCA. Conclusion: Cytokine profiles might be used to differentiate pediatric FM from AM, stratify severity, and predict prognosis. The targeted therapy that works individual cytokines might provide a potential treatment for reducing the onset of the FM and calming the condition, and further studies are needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".