Inflammatory Cytokine Expression Is Associated with Chikungunya Virus Resolution and Symptom Severity
Bibliographic record
Abstract
The Chikungunya virus infection zones have now quickly spread from Africa to parts of Asia, North America and Europe. Originally thought to trigger a disease of only mild symptoms, recently Chikungunya virus caused large-scale fatalities and widespread economic loss that was linked to recent virus genetic mutation and evolution. Due to the paucity of information on Chikungunya immunological progression, we investigated the serum levels of 13 cytokines/chemokines during the acute phase of Chikungunya disease and 6- and 12-month post-infection follow-up from patients of the Italian outbreak. We found that CXCL9/MIG, CCL2/MCP-1, IL-6 and CXCL10/IP-10 were significantly raised in the acute phase compared to follow-up samples. Furthermore, IL-1β, TNF-α, Il-12, IL-10, IFN-γ and IL-5 had low initial acute phase levels that significantly increased at later time points. Analysis of symptom severity showed association with CXCL9/MIG, CXCL10/IP-10 and IgG levels. These data give insight into Chikungunya disease establishment and subsequent convalescence, which is imperative to the treatment and containment of this quickly evolving and frequently re-emerging disease.
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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 | 0.001 |
| 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".