Prostate-specific membrane antigen–directed nanoparticle targeting for extreme nearfield ablation of prostate cancer cells
Bibliographic record
Abstract
Almost all biological therapeutic interventions cannot overcome neoplastic heterogeneity. Physical ablation therapy is immune to tumor heterogeneity, but nearby tissue damage is the limiting factor in delivering lethal doses. Multi-walled carbon nanotubes offer a number of unique properties: chemical stability, photonic properties including efficient light absorption, thermal conductivity, and extensive surface area availability for covalent chemical ligation. When combined together with a targeting moiety such as an antibody or small molecule, one can deliver highly localized temperature increases and cause extensive cellular damage. We have functionalized multi-walled carbon nanotubes by conjugating an antibody against prostate-specific membrane antigen. In our in vitro studies using prostate-specific membrane antigen-positive LNCaP prostate cancer cells, we have effectively demonstrated cell ablation of >80% with a single 30-s exposure to a 2.7-W, 532-nm laser for the first time without bulk heating. We also confirmed the specificity and selectivity of prostate-specific membrane antigen targeting by assessing prostate-specific membrane antigen-null PC3 cell lines under the same conditions (<10% cell ablation). This suggests that we can achieve an extreme nearfield cell ablation effect, thus restricting potential tissue damage when transferred to in vivo clinical applications. Developing this new platform will introduce novel approaches toward current therapeutic modalities and will usher in a new age of effective cancer treatment squarely addressing tumoral heterogeneity.
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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".