Linear Chimeric Triblock Molecules Self‐Assembled Micelles with Controllably Transformable Property to Enhance Tumor Retention for Chemo‐Photodynamic Therapy of Breast Cancer
Bibliographic record
Abstract
Abstract Although nanoparticles are expected to revolutionize cancer treatment, their low efficacy remains the greatest limiting factor. Recent investigations found that nanoparticles' golden principle, the enhanced permeability and retention (EPR) effect, is limited by the complicated tumor microenvironment. Herein, novel transformable nanomaterials are designed to utilize the EPR effect more effectively. By tandem conjugation of the hydrophobic head (chlorin e6 (Ce6) or bilirubin (BR)), peptide to form hydrogen bond (Phe‐Phe‐Val‐Leu‐Lys (FFVLK)), and hydrophilic tail (polyethylene glycol (PEG)), chimeric molecules that can form micelles (Ce6/BR‐FFVLK‐PEG) in aqueous solution are synthesized. Notably, the spherical micelles retain shape transformability. After circulation and distribution, they respond to 650 nm laser irradiation, and morphologically change into nanofibers so as to facilitate their retention markedly inside the tumor. Upon loading a reactive oxygen species‐responsive paclitaxel dimer with thioketal linker (PTX2‐TK), the resultant PTX2‐TK@Ce6/BR‐FFVLK‐PEG nanomedicine serves as a potent chemo‐photodynamic therapeutic for cancer treatment. Evaluations at both cell level and animal level reveal that PTX2‐TK@Ce6/BR‐FFVLK‐PEG exhibits superior biocompatibility and biodistribution, and suppresses 82.6% of in vitro cell growth and 61.8% of in vivo tumor growth at a common dose of intravenous injection (10 mg kg−1 PTX and 3.3 mg kg−1 Ce6), becoming a novel nanomedicine with extraordinary potential in cancer therapy.
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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) | 0.001 | 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".