Neural Stem Cell-Mediated Intratumoral Delivery of Gold Nanorods Improves Photothermal Therapy
Bibliographic record
Abstract
Plasmonic photothermal therapy utilizes biologically inert gold nanorods (AuNRs) as tumor-localized antennas that convert light into heat capable of eliminating cancerous tissue. This approach has lower morbidity than surgical resection and can potentially synergize with other treatment modalities including chemotherapy and immunotherapy. Despite these advantages, it is still challenging to obtain heating of the entire tumor mass while avoiding unnecessary collateral damage to surrounding healthy tissue. It is therefore critical to identify innovative methods to distribute an effective concentration of AuNRs throughout tumors without depositing them in surrounding healthy tissue. Here we demonstrate that AuNR-loaded, tumor-tropic neural stem cells (NSCs) can be used to improve the intratumoral distribution of AuNRs. A simple UV-vis technique for measuring AuNR loading within NSCs was established. It was then confirmed that NSC viability is unimpaired following AuNR loading and that NSCs retain AuNRs long enough to migrate throughout tumors. We then demonstrate that intratumoral injections of AuNR-loaded NSCs are more efficacious than free AuNR injections, as evidenced by reduced recurrence rates of triple-negative breast cancer (MDA-MB-231) xenografts following NIR exposure. Finally, we demonstrate that the distribution of AuNRs throughout the tumors is improved when transported by NSCs, likely resulting in the improved efficacy of AuNR-loaded NSCs as compared to free AuNRs. These findings highlight the advantage of combining cellular therapies and nanotechnology to generate more effective cancer treatments.
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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 | 0.000 |
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".