Photothermal therapy of papillary thyroid cancer tumor xenografts with targeted thyroid stimulating hormone receptor antibody functionalized multiwalled carbon nanotubes
Bibliographic record
Abstract
Abstract Multiwalled carbon nanotubes (MWCNTs) are being widely investigated in multiple biomedical applications including, and not limited to, drug delivery, gene therapy, imaging, biosensing, and tissue engineering. Their large surface area and aspect ratio in addition to their unique structural, optical properties, and thermal conductivity also make them potent candidates for novel hyperthermia therapy. Here we introduce thyroid hormone stimulating receptor (TSHR) antibody–conjugate–MWCNT formulation as an enhanced tumor targeting and light-absorbing device for the photoablation of xenografted BCPAP papillary thyroid cancer tumors. To ensure successful photothermal tumor ablation, we determined three key criteria that needed to be addressed: (1) predictive pre-operational modeling; (2) real-time monitoring of the tumor ablation process; and (3) post-operational follow-up to assess the efficacy and ensure complete response with minimal side effects. A COMSOL-based model of spatial temperature distributions of MWCNTs upon selected laser irradiation of the tumor was prepared to accurately predict the internal tumor temperature. This modeling ensured that 4.5W of total laser power delivered over 2 min, would cause an increase of tumor temperature above 45 ℃, and be needed to completely ablate the tumor while minimizing the damage to neighboring tissues. Experimentally, our temperature monitoring results were in line with our predictive modeling, with effective tumor photoablation leading to a significantly reduced post 5-week tumor recurrence using the TSHR-targeted MWCNTs. Ultimately, the results from this study support a utility for photosensitive biologically modified MWCNTs as a cancer therapeutic modality. Further studies will assist with the transition of photothermal therapy from preclinical studies to clinical evaluations.
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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".