Porphyrin‐lipid nanovesicles (Porphysomes) are effective photosensitizers for photodynamic therapy
Bibliographic record
Abstract
Abstract Porphysomes (PS) are liposome‐like nanoparticles comprising pyropheophorbide‐conjugated phospholipids that have demonstrated potential as multimodal theranostic agents for applications that include phototherapies, targeted drug delivery and in vivo fluorescence, photoacoustic, magnetic resonance or positron emission imaging. Previous therapeutic applications focused primarily on photothermal therapy (PTT) and suggested that PSs require target‐triggered activation for use as photodynamic therapy (PDT) sensitizers. Here, athymic nude mice bearing subcutaneous A549 human lung tumors were randomized into treatment and control groups: PS‐PDT at various doses, PS‐only and no treatment negative controls, as well as positive controls using the clinical photosensitizer Photofrin. Animals were followed for 30 days post‐treatment. PS‐PDT at all doses demonstrated a significant tumor ablative effect, with the greatest effect seen with 10 mg/kg PS at a drug‐light interval of 24 h. By comparison, negative controls (PS‐only, Photofrin‐only, and no treatment) showed uncontrolled tumor growth. PDT with Photofrin at 5 mg/kg and PS at 10 mg/kg demonstrated similar tumor growth suppression and complete tumor response rates (15 vs. 25%, p = 0.52). Hence, porphysome nanoparticles are an effective PDT agent and have the additional advantages of multimodal diagnostic and therapeutic applications arising from their intrinsic structure. Porphysomes may also be the first single all‐organic agent capable of concurrent PDT and PTT.
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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) | 0.000 | 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 | 0.000 | 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".