Photodynamic N-TiO2 Nanoparticle Treatment Induces Controlled ROS-mediated Autophagy and Terminal Differentiation of Leukemia Cells
Bibliographic record
Abstract
Abstract In this study, we used nitrogen-doped titanium dioxide (N-TiO 2 ) NPs in conjugation with visible light, and show that both reactive oxygen species (ROS) and autophagy are induced by this novel NP-based photodynamic therapy (PDT) system. While well-dispersed N-TiO 2 NPs (≤100 μg/ml) were inert, their photo-activation with visible light led to ROS-mediated autophagy in leukemia K562 cells and normal peripheral lymphocytes, and this increased in parallel with increasing NP concentrations and light doses. At a constant light energy (12 J/cm 2 ), increasing N-TiO 2 NP concentrations increased ROS levels to trigger autophagy-dependent megakaryocytic terminal differentiation in K562 cells. By contrast, an ROS challenge induced by high N-TiO 2 NP concentrations led to autophagy-associated apoptotic cell death. Using chemical autophagy inhibitors (3-methyladenine and Bafilomycin A1), we confirmed that autophagy is required for both terminal differentiation and apoptosis induced by photo-activated N-TiO 2 . Pre-incubation of leukemic cells with ROS scavengers muted the effect of N-TiO 2 NP-based PDT on cell fate, highlighting the upstream role of ROS in our system. In summary, PDT using N-TiO 2 NPs provides an effective method of priming autophagy by ROS induction. The capability of photo-activated N-TiO 2 NPs in obtaining desirable cellular outcomes represents a novel therapeutic strategy of cancer cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".