SARS-CoV-2 antigen-carrying extracellular vesicles activate T cell responses in a human immunogenicity model
Bibliographic record
Abstract
Extracellular vesicles (EVs) are entering the clinical arena as novel biologics for infectious diseases, potentially serving as the immunogenic components of next generation vaccines. However, relevant human assays to evaluate the immunogenicity of EVs carrying viral antigens are lacking, contributing to challenges in translating rodent studies to human clinical trials. Here, we engineered EVs to carry SARS-CoV-2 Spike to evaluate the immunogenicity of antigen-carrying EVs using human peripheral blood mononuclear cells (PBMCs). Delivery of Spike EVs to PBMCs resulted in specific immune cell activation as assessed through T cell activation marker expression. Further, Spike EVs were taken up largely by antigen-presenting cells (monocytes, dendritic cells and B cells). Taken together, this human PBMC-based system models physiologically relevant pathways of antigen delivery, uptake and presentation. In summary, the current study highlights the suitability of using human PBMCs for evaluating the immunogenicity of EVs engineered to carry antigens for infectious disease therapeutics.
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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 | 0.000 |
| 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 | 0.001 | 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".