Dose-dependent T-cell Dynamics and Cytokine Cascade Following rVSV-ZEBOV Immunization
Bibliographic record
Abstract
BACKGROUND: The recent West African Ebola epidemic led to accelerated efforts to test Ebola vaccine candidates. As part of the World Health Organisation-led VSV Ebola Consortium (VEBCON), we performed a phase I clinical trial investigating rVSV-ZEBOV (a recombinant vesicular stomatitis virus-vectored Ebola vaccine), which has recently demonstrated protection from Ebola virus disease (EVD) in phase III clinical trials and is currently in advanced stages of licensing. So far, correlates of immune protection are incompletely understood and the role of cell-mediated immune responses has not been comprehensively investigated to date. METHODS: PFU) (ClinicalTrials.gov; NCT02283099). Main study objectives were safety and immunogenicity, while exploratory objectives included lymphocyte dynamics, cell-mediated immunity and cytokine networks, which were assessed using flow cytometry, ELISpot and LUMINEX assay. FINDINGS: Immunization with rVSV-ZEBOV was well tolerated without serious vaccine-related adverse events. Ebola virus-specific neutralizing antibodies were induced in nearly all individuals. Additionally, vaccinees, particularly within the highest dose cohort, generated Ebola glycoprotein (GP)-specific T cells and initiated a cascade of signaling molecules following stimulation of peripheral blood mononuclear cells with Ebola GP peptides. INTERPRETATION: PFU elicited higher cellular immune responses and stronger interlocked cytokine networks compared to lower dose groups. To our knowledge these data represent the first detailed cell-mediated immuneprofile of a clinical trial testing rVSV-ZEBOV, which is of particular interest in light of its potential upcoming licensure as the first Ebola vaccine. VEBCON trial Hamburg, Germany (NCT02283099).
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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 | 0.000 |
| 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) | 0.002 | 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".