Targeting Strategies in Photodynamic Therapy for Cancer Treatment
Bibliographic record
Abstract
The selective, targeted delivery of photosensitizers to diseased cells is one of the major problems in photodynamic therapy (PDT) and is still a challenge to take up. One area of importance is the elaboration of targeted photosensitizers. Targeted therapy is a promising new therapeutic strategy, created to overcome growing problems of contemporary medicine, such as drug toxicity and drug resistance. An emerging modality of this approach is targeted PDT (TPDT) with the main aim of improving delivery of the photosensitizer to cancer tissue and, at the same time, enhancing specificity and efficiency of PDT. Depending on the mechanism of targeting, we can suggest dividing the strategies of TPDT into “passive,” “active,” and “activatable”; in the latter case, the photosensitizer is activated only in the targeted tissue. In this review, contemporary strategies of TPDT are described, including innovative new concepts, such as targeting assisted by peptides and aptamers, multifunctional nanoplatforms with navigation by magnetic field, or “photodynamic molecular beacons” activatable by enzymes and nucleic acid. The imperative of introducing a new paradigm of PDT, focused on the concepts of heterogeneity and dynamic state of tumor, is also called for.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".