Targeting‐Triggered Porphysome Nanostructure Disruption for Activatable Photodynamic Therapy
Bibliographic record
Abstract
Photodynamic therapy (PDT) and photothermal therapy (PTT) possess advantages over the conventional therapies with additional treatment selectivity achieved with local laser irradiation. Comparing to PTT that ablates target tissue via thermal necrosis, PDT induces target cell death via singlet oxygen without damaging the underling connective tissue, thus preserving its biological function. Activatable photosensitizers provide an additional level of treatment selectivity via the disease-associated activation mechanism. In this study, folate-conjugated porphysomes are introduced as targeting-triggered activatable nano-sized beacons for PDT. Porphysomes are reported previously as the most stable and efficient delivery system of porphyrin, but their nanostructure converts the singlet oxygen generation mechanism to thermal ablation mechanism. By folate-receptor-mediated endocytosis, folate-porphysomes are internalized into cells rapidly and resulted in efficient disruption of nanostructures, thus switching back on the photodynamic activity of the densely packed porphyrins for effective PDT. In both in vitro and in vivo studies, folate-porphysomes can achieve folate receptor-selective PDT efficacy, which proves the robustness of targeting-triggered PDT activation of porphysome nanostructure for highly selective tumor ablation. The formulation of porphysomes can be modified with other targeting ligands as activatable photosensitizers for personalized treatment in future.
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 | 0.000 | 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".