Spiky Gold Nanoparticles, a Nanoscale Approach to Enhanced Ex Vivo T-Cell Activation
Bibliographic record
Abstract
While existing synthetic technologies for ex vivo T-cell activation face challenges like suboptimal expansion rates and low effectiveness, artificial antigen-presenting cells (aAPCs) hold great promise for enhanced T-cell based therapies. In particular, gold nanoparticles (AuNPs), known for their biocompatibility, ease of synthesis, and versatile surface chemistry, are strong candidates for use as nanoscale aAPCs. In this study, we developed spiky AuNPs with branched geometries to present activating ligands to primary human T-cells. The special structure of spiky AuNPs enhances biomolecule loading capacity and significantly improves T-cell activation through multivalent binding of costimulatory ligands and receptors. Our spiky AuNPs outperform existing systems including Dynabeads and soluble activators by promoting greater polyclonal expansion of T-cells, boosting sustained cytokine production, and generating highly functional T-cells with reduced exhaustion. In addition, spiky AuNPs effectively activate and expand CD19 CAR-T cells while demonstrating increased in vitro cytotoxicity against target cells using fewer effector cells than Dynabeads. This study underscores the potential of spiky AuNPs as a powerful tool, bringing new opportunities to adoptive cell therapy applications.
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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) | 0.000 | 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 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".