Comprehensive insights into intracellular fate of WS <sub>2</sub> nanosheets for enhanced photothermal therapeutic outcomes via exocytosis inhibition
Bibliographic record
Abstract
Abstract Two‐dimensional (2D) nanosheet (NS)‐based photothermal agents (PTAs), such as transition‐metal dichalcogenides, have shown immense potential for their use in cancer photothermal therapy (PTT). However, the nano‐bio interaction study regarding these NS‐based PTAs is still in its infancy. In this study, we used WS 2 ‐PEG NS‐based PTA as an example to provide comprehensive insights into the experimental understanding of their fate in cancer cells. The data revealed that three different endocytosis pathways (macropinocytosis, clathrin‐dependent, and caveolae‐dependent endocytosis), autophagy‐mediated lysosome accumulation, and exocytosis‐induced excretion contribute to the integrated pathways of WS 2 ‐PEG NSs within cells. These pathways are consistent with our previous reports on MoS 2 ‐PEG NS‐based drug delivery platform, indicating that the composition difference of 2D NSs with PEGylation may have little influence on their intercellular fate. Moreover, by blocking the revealed exocytosis pathway‐mediated secretion of WS 2 NSs in tumor cells, an effective approach is proposed to attain enhanced photothermal therapeutic outcomes with low doses of WS 2 NSs and under a low power of a near‐infrared (NIR) laser. We expect that the exocytosis inhibition strategy may be a universal one for 2D NSs to achieve combination cancer therapy. This study may also provide more experimental basis for the future development of 2D NS’s application in biomedicine (e.g. PTT).
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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".