Multifunctional liposomes remodeling tumor microenvironment for enhancing Hemoporfin‐mediated photodynamic therapy
Bibliographic record
Abstract
Abstract Photodynamic therapy (PDT) utilizes light and photosensitizer (PS) to generate reactive oxygen species (ROS) to kill cancer cells, presenting a promising strategy as an anti‐cancer treatment. However, the low hydrophilicity and poor targeting of currently used PSs, as well as the abnormal tumor microenvironment (TME), limit the clinical application of PDT. Herein, non‐toxic liposomes with the ability to incorporate both hydrophilic and hydrophobic PS are used as the nanocarrier to co‐load Hemoporfin, L‐buthionine sulfoximine (BSO), and catalase (CAT) to obtain BSO/CAT@Liposomes‐Hemoporfin nanoparticles (BCHL NPs), which could be used to remodel the TME and to enhance Hemoporfin‐mediated PDT efficacy. BCHL NPs exhibit a long blood circulation time and can accumulate in the tumor. BSO can reduce the cytosolic concentration of glutathione (GSH), a natural scavenger of ROS. CAT catalyzes the endogenously overexpressed H 2 O 2 in the tumor site into H 2 O and O 2 , thus relieving tumor hypoxia and enhancing ROS generation. Upon irradiation, the synergetic effects of reduced GSH synthesis by BSO and relieved hypoxia by CAT were observed in 4T1 tumor‐bearing mouse model. Compared to the tumor treated by free Hemoporfin, BCHL NPs‐mediated PDT resulted in 1.25‐fold higher inhibition of tumor growth due to the enhanced ROS generation. The present study provides insight into the design of efficient strategies for enhanced clinic Hemoporfin‐mediated PDT efficiency.
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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".