Intranasal delivery of engineered anti-SARS-CoV-2 extracellular vesicles therapeutically represses lung infection and inflammation
Bibliographic record
Abstract
Extracellular vesicles (EVs) are amenable to genetic engineering in that EVs can be endowed with surface armaments that can directly bind to target molecules or receptors. We previously developed HEK293 cell-derived EVs that contain a novel fusion tetraspanin protein, CD63, embedded within a highly conserved anti-SARS-CoV-2 nanobody, VHH72. These anti-SARS-CoV-2-enriched EVs bind SARS-CoV-2 spike protein and can functionally neutralize SARS-CoV-2 in vitro. Here, we extend our observations in vivo using EVs derived from neural stem cells (NSCs) and demonstrated the antiviral effectiveness of these direct-acting EVs in the lungs of SARS-CoV-2 infected mice when administered intranasally post-infection. Using NanoString-based immune transcriptomics we showed that these EVs exert mild anti-inflammatory effects on SARS-CoV-2 infected lungs. This is the first demonstration of the effective use of intranasally delivered EVs ladened with anti-SARS-CoV-2 nanobodies in vivo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".