Development of a Clinically Viable Strategy for Nanoparticle‐Based Photodynamic Therapy of Colorectal Cancer
Bibliographic record
Abstract
Abstract Photodynamic therapy (PDT) is an anticancer treatment modality that is poorly adopted into clinical practice for a variety of factors. Though fundamental studies supporting its utility abound, few clinical trials are performed on its use. This may be due to failures in the translation of treatment protocols used in fundamental studies into clinically viable protocols. This study seeks to develop a clinically viable protocol for the treatment of colorectal cancer using nanoparticle‐based PDT by using three complementary animal models. Using the porphysome nanoparticle as a model photosensitizer, several theoretical and practical challenges to the clinical delivery of PDT are addressed including the required drug‐light interval (DLI), the route of administration, and the method of light irradiation delivery. This study finds that nanoparticle‐based PDT can effectively ablate colorectal cancer tumors, that PDT can be effectively delivered after a relatively short DLI, and that safety concerns related to off‐target effects can be mitigated through the peritumoral administration of the photosensitizer and the transanal intraluminal placement of light irradiation. These findings lay the framework for future clinical trials investigating the use of PDT as a component of the multi‐modal approach to the treatment of colorectal cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".