Self-Sensing Porphysomes for Fluorescence-Guided Photothermal Therapy
Bibliographic record
Abstract
Porphysomes are highly quenched unilamellar porphyrin-lipid nanovesicles with structurally dependent photothermal properties. The high packing density of porphyrin molecules in the lipid bilayer enables their application in photothermal therapy, whereas the partial disruption of the porphysome structure over time restores the porphyrin fluorescence and enables the fluorescence-guided photothermal ablation. This conversion is a time-dependent process and cannot be easily followed using existing analytical techniques. Here we present the design of a novel self-sensing porphysome (FRETysomes) capable of fluorescently broadcasting its structural state through Förster resonance energy transfer. By doping in a near-infrared emitting fluorophore, it is possible to divert a small fraction of the absorbed energy toward fluorescence emission which provides information on whether the vesicle is intact or disrupted. Addition of bacteriopheophorbide-lipid into the vesicle bilayer as a fluorescence acceptor (0.5-7.5 mol %) yields a large separation of 100 nm between the absorption and fluorescence bands of the nanoparticle. Furthermore, a progressive increase in FRET efficiency (14.6-72.7%) is observed. Photothermal heating and serum stability in FRETysomes is comparable with the undoped porphysomes. The fluorescence arising from the energy transfer between the donor and acceptor dyes can be clearly visualized in vivo through hyperspectral imaging. By calculating the ratio between the acceptor and donor fluorescence, it is possible to determine the structural fate of the nanovesicles. We observe using this technique that tumor accumulation of structurally intact porphyrin-lipid nanovesicles persists at 24 and 48 h postinjection. The development of FRETysomes offers a unique and critical imaging tool for planning porphysome-enabled fluorescence-guided photothermal treatment, which maximizes light-induced thermal toxicity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".