Intracellular Mechanistic Understanding of 2D MoS<sub>2</sub> Nanosheets for Anti-Exocytosis-Enhanced Synergistic Cancer Therapy
Bibliographic record
Abstract
Emerging two-dimensional (2D) nanomaterials, such as transition-metal dichalcogenide (TMD) nanosheets (NSs), have shown tremendous potential for use in a wide variety of fields including cancer nanomedicine. The interaction of nanomaterials with biosystems is of critical importance for their safe and efficient application. However, a cellular-level understanding of the nano-bio interactions of these emerging 2D nanomaterials ( i . e ., intracellular mechanisms) remains elusive. Here we chose molybdenum disulfide (MoS 2 ) NSs as representative 2D nanomaterials to gain a better understanding of their intracellular mechanisms of action in cancer cells, which play a significant role in both their fate and efficacy. MoS 2 NSs were found to be internalized through three pathways: clathrin → early endosomes → lysosomes, caveolae → early endosomes → lysosomes, and macropinocytosis → late endosomes → lysosomes. We also observed autophagy-mediated accumulation in the lysosomes and exocytosis-induced efflux of MoS 2 NSs. Based on these findings, we developed a strategy to achieve effective and synergistic in vivo cancer therapy with MoS 2 NSs loaded with low doses of drug through inhibiting exocytosis pathway-induced loss. To the best of our knowledge, this is the first systematic experimental report on the nano-bio interaction of 2D nanomaterials in cells and their application for anti-exocytosis-enhanced synergistic 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".