Self-Assembled Porphyrin Nanodiscs with Structure-Dependent Activation for Phototherapy and Photodiagnostic Applications
Bibliographic record
Abstract
The abilities to deliver and subsequently activate a therapeutic at the intended site of action are two important challenges in the synthesis of novel nanoparticles. Poor tumor permeability as a result of a dense microenvironment can impede the delivery of nanoparticles to the site of action. The design of a sub-40 nm activatable porphyrin nanodisc, based on protein-induced lipid constriction, is described. The biophotonic nanoparticle, self-assembled from aggregated porphyrin-lipid, is stabilized by an amphipathic alpha helical protein and becomes photoactive when its structure is perturbed. Enzymatic cleavage of the constricting protein leads to conversion of the particle from a disc- to a vesicle-shaped structure and provides further evidence that the apolipoprotein serves a functional role on the nanodisc. Fluorescence measurements of these nanodiscs in a detergent show that fluorescence is over 99% quenched in the intact state with a 12-fold increase in singlet oxygen generation upon disruption. Cellular fluorescence unquenching and dose-dependent phototoxicity demonstrate that these nanodiscs can be internalized and unquenched intracellularly. Finally, nanodiscs were found to display a 5-fold increase in diffusion coefficient when compared with the protein-free control ((3.5±0.1)×10(-7) vs (0.7±0.03)×10(-7) cm2 s(-1)). The ability to incorporate large amounts of photosensitizer drugs into its compact structure allows for phototherapeutic action, fluorescence diagnostic applications, and the potential to effectively deliver photosensitizers deep into poorly permeable 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) | 0.000 | 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 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".