Ablation of Hypoxic Tumors with Dose-Equivalent Photothermal, but Not Photodynamic, Therapy Using a Nanostructured Porphyrin Assembly
Bibliographic record
Abstract
Tumor hypoxia is increasingly being recognized as a characteristic feature of solid tumors and significantly complicates many treatments based on radio-, chemo-, and phototherapies. While photodynamic therapy (PDT) is based on photosensitizer interactions with diffused oxygen, photothermal therapy (PTT) has emerged as a new phototherapy that is predicted to be independent of oxygen levels within tumors. It has been challenging to meaningfully compare these two modalities due to differences in contrast agents and irradiation parameters, and no comparative in vivo studies have been performed until now. Here, by making use of recently developed nanostructured self-quenched porphysome nanoparticles, we were able to directly compare PDT and PTT using matched light doses and matched porphyrin photosensitizer doses (with the photosensitizer being effective for either PTT or PDT based on the existence of nanostructure or not). Therefore, we demonstrated the nanostructure-driven conversion from the PDT singlet oxygen generating mechanism of porphyrin to a completely thermal mechanism, ideal for PTT enhancement. Using a novel hypoxia tumor model, we determined that nanostructured porphyrin PTT enhancers are advantageous to overcome hypoxic conditions to achieve effective ablation of solid tumors.
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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".