Self‐Disassembling and Oxygen‐Generating Porphyrin‐Lipoprotein Nanoparticle for Targeted Glioblastoma Resection and Enhanced Photodynamic Therapy
Bibliographic record
Abstract
Abstract The dismal prognosis for glioblastoma multiform (GBM) patients is primarily attributed to the highly invasive tumor residual that remained after surgical intervention. The development of precise intraoperative imaging and postoperative residual removal techniques will facilitate the gross total elimination of GBM. Here, a self‐disassembling porphyrin lipoprotein‐coated calcium peroxide nanoparticles (PLCNP) is developed to target GBM via macropinocytosis, allowing for fluorescence‐guided surgery of GBM and improving photodynamic treatment (PDT) of GBM residual by alleviating hypoxia. By reducing self‐quenching and enhancing lysosome escape efficiency, the incorporation of calcium peroxide (CaO 2 ) cores in PLCNP amplifies the fluorescence intensity of porphyrin‐lipid. Furthermore, the CaO 2 core has diminished tumor hypoxia and improves the PDT efficacy of PLCNP, enabling low‐dose PDT and reversing tumor progression induced by hypoxia aggravation following PDT. Taken together, this self‐disassembling and oxygen‐generating porphyrin‐lipoprotein nanoparticle may serve as a promising all‐in‐one nanotheranostic platform for guiding precise GBM excision and empowering post‐operative PDT, providing a clinically applicable strategy to combat GBM in a safe and effective manner.
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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".