Synergistic Enhancement of Photodynamic Cancer Therapy with Mesenchymal Stem Cells and Theranostic Nanoparticles
Bibliographic record
Abstract
Nanoparticles engineered to combat cancer and other life-threatening diseases may significantly improve patient outcomes. However, inefficient nanoparticle delivery to tumors limits their use and necessitates the development of complex delivery approaches. Here, we examine this issue by harnessing the tumor-homing abilities of human mesenchymal stem cells (MSCs) to deliver a decoupled theranostic complex of rare earth-doped nanoparticles (dNPs) and photosensitizer chlorin e6 (Ce6) to tumors. We show that both bone-marrow- and skin-derived MSCs can transport the dNP-Ce6 complex inside tumor spheroids, which is challenging to accomplish by passive delivery alone. MSCs deliver the dNP-Ce6 complex across the tumor spheroid, facilitating more effective photodynamic damage and tumor destruction than passively accumulated dNP-Ce6. The dNP-Ce6 complex also provides the built-in ability to monitor the MSC migration without causing undesired phototoxicity, which is essential for maximal and side-effect-free delivery of nanoparticles. Our results demonstrate how MSCs can be used as delivery vehicles for the transportation of the dNP-Ce6 complex, addressing the limitations of passive nanoparticle delivery and providing light-based theranostics.
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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".