The use of nanomaterials in advancing photodynamic therapy (PDT) for deep-seated tumors and synergy with radiotherapy
Bibliographic record
Abstract
Photodynamic therapy (PDT) has been under development for at least 40 years. Multiple studies have demonstrated significant anti-tumor efficacy with limited toxicity concerns. PDT was expected to become a major new therapeutic option in treating localized cancer. However, despite a shifting focus in oncology to aggressive local therapies, PDT has not to date gained widespread acceptance as a standard-of-care option. A major factor is the technical challenge of treating deep-seated and large tumors, due to the limited penetration and variability of the activating light in tissue. Poor tumor selectivity of PDT sensitizers has been problematic for many applications. Attempts to mitigate these limitations with the use of multiple interstitial fiberoptic catheters to deliver the light, new generations of photosensitizer with longer-wavelength activation, oxygen independence and better tumor specificity, as well as improved dosimetry and treatment planning are starting to show encouraging results. Nanomaterials used either as photosensitizers per se or to improve delivery of molecular photosensitizers is an emerging area of research. PDT can also benefit radiotherapy patients due to its complementary and potentially synergistic mechanisms-of-action, ability to treat radioresistant tumors and upregulation of anti-tumoral immune effects. Furthermore, recent advances may allow ionizing radiation energy, including high-energy X-rays, to replace external light sources, opening a novel therapeutic strategy (radioPDT), which is facilitated by novel nanomaterials. This may provide the best of both worlds by combining the precise targeting and treatment depth/volume capabilities of radiation therapy with the high therapeutic index and biological advantages of PDT, without increasing toxicities. Achieving this, however, will require novel agents, primarily developed with nanomaterials. This is under active investigation by many research groups using different approaches.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".