Systems Immunology Approaches to Understanding Immune Responses in Acute Infection of Yellow Fever Patients
Bibliographic record
Abstract
In the 2018 yellow fever (YF) outbreak in Brazil, we generated new transcriptomic data and combined it with clinical and immunological data to decode the pathogenesis of YF. Analyzing 79 patients, we found distinct gene expression patterns between acute YF, other viral infections, and the milder YF-17D vaccine infection. We identified a critical role for low-density, immature neutrophils in severe outcomes, marked by the downregulation of genes essential for neutrophil migration and maturation, such as PADI4, CSF3R, and ICAM1, in deceased patients. Our study also revealed complex interactions among inflammation-related genes, including increased CXCL10 and IL1R2 expression and decreased IL-1b expression in the acute phase. The diminished expression of HLA class II genes indicates impaired antigen presentation. These findings highlight the delicate balance of immune responses in YF pathogenesis and lay the groundwork for future therapeutic and diagnostic advancements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".