Porphysome nanoparticles for enhanced photothermal therapy in a patient-derived orthotopic pancreas xenograft cancer model: a pilot study
Bibliographic record
Abstract
Local disease control is a major challenge in pancreatic cancer treatment, because surgical resection of the primary tumor is only possible in a minority of patients and radiotherapy cannot be delivered in curative doses. Despite the promise of photothermal therapy (PTT) for focal ablation of pancreatic tumors, this approach remains underinvestigated. Using photothermal sensitizers in combination with laser light irradiation for PTT can result in more efficient conversion of light energy to heat and improved spatial confinement of thermal destruction to the tumor. Porphysomes are self-assembled nanoparticles composed mainly of pyropheophorbide-conjugated phospholipids, enabling the packing of ∼80,000 porphyrin photosensitizers per particle. The high-density porphyrin loading imparts enhanced photonic properties and enables high-payload tumor delivery. A patient-derived orthotopic pancreas xenograft model was used to evaluate the feasibility of porphysome-enhanced PTT for pancreatic cancer. Biodistribution and tumor accumulation were evaluated using fluorescence intensity measurements from homogenized tissues and imaging of excised organs. Tumor surface temperature was recorded using IR optical imaging during light irradiation to monitor treatment progress. Histological analyses were conducted to determine the extent of PTT thermal damage. These studies may provide insight into the influence of heat-sink effect on thermal therapy dosimetry for well-perfused pancreatic 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) | 0.001 | 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 | 0.001 |
| 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".