Poor virus-specific T-cell responses early after tick-borne encephalitis virus infection correlate with disease severity
Bibliographic record
Abstract
Tick-borne encephalitis virus (TBEV) infection may cause acute central nervous system inflammation varying in clinical manifestations and severity. A possible correlation of TBEV-specific antibody and cell-mediated immune responses, shortly after infection, with clinical manifestations, severity and long-term outcome has been poorly investigated. In a cohort of thirty early tick-borne encephalitis (TBE) patients, we assessed the magnitude, specificity and functional properties of TBEV-specific T-cell and antibody responses. These responses early during disease were assessed in view of clinical manifestations, severity and long-term outcome. TBEV-specific T-cell responses to C, E, NS1, and NS5 proteins were significantly lower in patients with severe acute illness than in patients with mild TBE. Lower T-cell responses to E, NS1, and NS5 proteins also correlated with the development of meningoencephalomyelitis. Virus-specific antibody titres early after infection did not correlate with disease severity, clinical manifestations, or long-term outcome in this study, possibly due to the small number of patients of which matching serum and peripheral blood mononuclear cells were available. The findings suggest that virus-specific T cells afford a certain degree of protection against the development of severe TBEV-induced 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 | 0.000 |
| 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".