A PEGylation-Free Biomimetic Porphyrin Nanoplatform for Personalized Cancer Theranostics
Bibliographic record
Abstract
PEGylation (PEG) is the most commonly adopted strategy to prolong nanoparticles' vascular circulation by mitigating the reticuloendothelial system uptake. However, there remain many concerns in regards to its immunogenicity, targeting efficiency, etc., which inspires pursuit of alternate, non-PEGylated systems. We introduced here a PEG-free, porphyrin-based ultrasmall nanostructure mimicking nature lipoproteins, termed PLP, that integrates multiple imaging and therapeutic functionalities, including positron emission tomography (PET) imaging, near-infrared (NIR) fluorescence imaging and photodynamic therapy (PDT). With an engineered lipoprotein-mimicking structure, PLP is highly stable in the blood circulation, resulting in favorable pharmacokinetics and biodistribution without the need of PEG. The prompt tumor intracellular trafficking of PLP allows for rapid nanostructure dissociation upon tumor accumulation to release monomeric porphyrins to efficiently generate fluorescence and photodynamic reactivity, which are highly silenced in intact PLP, thus providing an activatable mechanism for low-background NIR fluorescence imaging and tumor-selective PDT. Its intrinsic copper-64 labeling feature allows for noninvasive PET imaging of PLP delivery and quantitative assessment of drug distribution. Using a clinically relevant glioblastoma multiforme model, we demonstrated that PLP enabled accurate delineation of tumor from surrounding healthy brain at size less than 1 mm, exhibiting the potential for intraoperative fluorescence-guided surgery and tumor-selective PDT. Furthermore, we demonstrated the general applicability of PLP for sensitive and accurate detection of primary and metastatic tumors in other clinically relevant animal models. Therefore, PLP offers a biomimetic theranostic nanoplatform for pretreatment stratification using PET and NIR fluorescence imaging and for further customized cancer management via imaging-guided surgery, PDT, or/and potential chemotherapy.
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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".