Photodynamic and Photothermal Therapies: Synergy Opportunities for Nanomedicine
Bibliographic record
Abstract
Tumoricidal photodynamic (PDT) and photothermal (PTT) therapies harness light to eliminate cancer cells with spatiotemporal precision by either generating reactive oxygen species or increasing temperature. Great strides have been made in understanding biological effects of PDT and PTT at the cellular, vascular and tumor microenvironmental levels, as well as translating both modalities in the clinic. Emerging evidence suggests that PDT and PTT may synergize due to their different mechanisms of action, and their nonoverlapping toxicity profiles make such combination potentially efficacious. Moreover, PDT/PTT combinations have gained momentum in recent years due to the development of multimodal nanoplatforms that simultaneously incorporate photodynamically- and photothermally active agents. In this review, we discuss how combining PDT and PTT can address the limitations of each modality alone and enhance treatment safety and efficacy. We provide an overview of recent literature featuring dual PDT/PTT nanoparticles and analyze the strengths and limitations of various nanoparticle design strategies. We also detail how treatment sequence and dose may affect cellular states, tumor pathophysiology and drug delivery, ultimately shaping the treatment response. Lastly, we analyze common experimental design pitfalls that complicate preclinical assessment of PDT/PTT combinations and propose rational guidelines to elucidate the mechanisms underlying PDT/PTT interactions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".