Improving the Delivery of Drugs and Nucleic Acids to T Cells Using Nanotechnology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
T cells play several roles in antitumor immunity, including mediating cytotoxicity, generating immune memory, and promoting humoral immunity. Given these critical roles, T cells are the therapeutic target of immunotherapies that have achieved clinical success, notably immune checkpoint inhibitors and chimeric antigen receptor T‐cell therapy. However, a fraction of patients benefits from these treatments due to intolerable toxicities and limited efficacy. These issues stem in part from inefficient and nonselective drug delivery to T cells. Nanotechnology may help resolve these delivery issues, as nanoparticles can serve as modular drug delivery vehicles with targeting abilities that can be applied for ex vivo and in vivo delivery. Herein, applications of nanotechnology in improving extracellular delivery of cytokines and small molecule drugs and intracellular delivery of siRNA to T cells are described. An overview of nanoparticle‐mediated delivery of nucleic acids for chimeric antigen receptor T‐cell therapy and CRISPR/Cas9 genome editing is provided. Finally, an outlook on the challenges and opportunities for the advancement of nanoparticle‐mediated drug delivery to T cells is shared.
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.
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 | 0.000 | 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) | 0.000 | 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 it