Nanotechnology Promotes Genetic and Functional Modifications of Therapeutic T Cells Against Cancer
Bibliographic record
Abstract
Growing experience with engineered chimeric antigen receptor (CAR)-T cells has revealed some of the challenges associated with developing patient-specific therapy. The promising clinical results obtained with CAR-T therapy nevertheless demonstrate the urgency of advancements to promote and expand its uses. There is indeed a need to devise novel methods to generate potent CARs, and to confer them and track their anti-tumor efficacy in CAR-T therapy. A potentially effective approach to improve the efficacy of CAR-T cell therapy would be to exploit the benefits of nanotechnology. This report highlights the current limitations of CAR-T immunotherapy and pinpoints potential opportunities and tremendous advantages of using nanotechnology to 1) introduce CAR transgene cassettes into primary T cells, 2) stimulate T cell expansion and persistence, 3) improve T cell trafficking, 4) stimulate the intrinsic T cell activity, 5) reprogram the immunosuppressive cellular and vascular microenvironments, and 6) monitor the therapeutic efficacy of CAR-T cell therapy. Therefore, genetic and functional modifications promoted by nanotechnology enable the generation of robust CAR-T cell therapy and offer precision treatments against cancer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".