Phototheranostic Porphyrin Nanoparticles Enable Visualization and Targeted Treatment of Head and Neck Cancer in Clinically Relevant Models
Bibliographic record
Abstract
Head and neck cancer is the fifth most common type of cancer worldwide and remains challenging for effective treatment due to the proximity to critical anatomical structures in the head and neck region, which increases the probability of toxicity from surgery and radiotherapy, and therefore emphasizes the importance of maximizing the targeted ablation. We have assessed the effectiveness of porphysome nanoparticles to enhance fluorescence and photoacoustic imaging of head and neck tumors in rabbit and hamster models. In addition, we evaluated the effectiveness of this agent for localized photothermal ablative therapy of head and neck tumors. We have demonstrated that porphysomes not only enabled fluorescence and photoacoustic imaging of buccal and tongue carcinomas, but also allowed for complete targeted ablation of these tumors. The supremacy of porphysome-enabled photothermal therapy over surgery to completely eradicate primary tumors and metastatic regional lymph node while sparing the adjacent critical structures' function has been demonstrated for the first time. This study represents a novel breakthrough that has the potential to revolutionize our approach to tumor diagnosis and treatment in head and neck cancer and beyond.
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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".