Effective chimeric antigen receptor T cells against SARS-CoV-2
Bibliographic record
Abstract
Current therapies to treat coronavirus disease 2019 (COVID-19) involve vaccines against the spike protein S1 of SARS-CoV-2. Here, we outline an alternative approach involving chimeric antigen receptors (CARs) in T cells (CAR-Ts). CAR-T recognition of the SARS-CoV-2 receptor-binding domain (RBD) peptide induced ribosomal protein S6 phosphorylation, the increased expression of activation antigen, CD69 and effectors, interferon-γ, granzyme B, perforin, and Fas-ligand on overlapping subsets of CAR-Ts. CAR-Ts further showed potent in vitro killing of target cells loaded with RBD, S1 peptide, or expressing the S1 protein. The efficacy of killing varied with different sized hinge regions, whereas time-lapse microscopy showed CAR-T cluster formation around RBD-expressing targets. Cytolysis of targets was mediated primarily by the GZMB/perforin pathway. Lastly, we showed in vivo killing of S1-expressing cells by our SARS-CoV-2 CAR-Ts in mice. The successful generation of SARS-CoV-2 CAR-Ts represents a living vaccine approach for the treatment of COVID-19.
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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) | 0.003 | 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".