The concentration of IL-6, TNF-α, s-ICAM-1, and EBV DNA load – predictive factors of hepatological complications in children with infectious mononucleosis. A pilot study
Bibliographic record
Abstract
Introduction The aim of the study was to assess the relationship between the concentration of pro-inflammatory cytokines such as interleukin 6 (IL-6), tumour necrosis factor α (TNF-α), soluble intercellular adhesion molecule 1 (s-ICAM-1), and Epstein-Barr virus (EBV) DNA viraemia in children with infectious mononucleosis and hepatitis. Material and methods The Epstein-Barr virus DNA load in the plasma of 36 immunocompetent patients aged 2.5–18 years with a symptomatic (sore throat, cervical lymph node enlargement, fatigue, and fever), antibody-confirmed EBV primary infection was assessed using a quantitative real-time polymerase chain reaction assay. The concentration of IL-6, s-ICAM-1, and TNF-α in serum was determined using enzyme-linked immunosorbent assay tests. Results In a group of patients with infectious mononucleosis caused by EBV and viraemia > 3.5 log10 copies/ml, shorter duration of symptoms, and higher serum levels of s-ICAM-1 and C-reactive protein (CRP) were confirmed. In group of children with EBV hepatitis and CRP > 5 mg/l, levels of s-ICAM-1, γ-glutamyl transpeptidase activity, count of total white blood cells and lymphocytes, and EBV DNA load were significantly higher than in patients with CRP < 5 mg/l. Among patients with EBV hepatitis the increase of s-ICAM-1 concentration correlated with the increase of IL-6 and TNF-α serum levels. Conclusions The results suggest a higher risk of cholestatic complications in children with elevated CRP level. They indicate that EBV DNA viraemia in conjunction with an s-ICAM-1, IL-6, and TNF-α assessment may be helpful in selecting patients requiring hospitalization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".