Antigen Presenting Cell Mimetic Lipid Nanoparticles for Rapid mRNA CAR T Cell Cancer Immunotherapy
Bibliographic record
Abstract
Chimeric antigen receptor (CAR) T cell therapy has achieved remarkable clinical success in the treatment of hematological malignancies. However, producing these bespoke cancer-killing cells is a complicated ex vivo process involving leukapheresis, artificial T cell activation, and CAR construct introduction. The activation step requires the engagement of CD3/TCR and CD28 and is vital for T cell transfection and differentiation. Though antigen-presenting cells (APCs) facilitate activation in vivo, ex vivo activation relies on antibodies against CD3 and CD28 conjugated to magnetic beads. While effective, this artificial activation adds to the complexity of CAR T cell production as the beads must be removed prior to clinical implementation. To overcome this challenge, this work develops activating lipid nanoparticles (aLNPs) that mimic APCs to combine the activation of magnetic beads and the transfection capabilities of LNPs. It is shown that aLNPs enable one-step activation and transfection of primary human T cells with the resulting mRNA CAR T cells reducing tumor burden in a murine xenograft model, validating aLNPs as a promising platform for the rapid production of mRNA CAR T cells.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".