Emancipating Target‐Functionalized Carbon Dots from Autophagy Vesicles for a Novel Visualized Tumor Therapy
Bibliographic record
Abstract
Abstract Killing the tumor cells by a visualized targeting system is a promising strategy with which to achieve high efficiency, low side effects, and a high survival rate for tumor therapy. Here, an autophagy regulation strategy is reported by emancipating target‐functionalized carbon dots from autophagy vesicles for the efficient visualized tumor therapy. The folic acid modified N‐doped carbon dots (FN‐CDs) are selectively endocytosed (specific cellular uptake rate >93.40%) and stably existed in autophagic vacuoles in tumor cells. Next, the autophagic vacuoles are “opened” by the autophagy inhibitors. Released FN‐CDs activate both the intrinsic and extrinsic apoptotic signaling pathway and kill tumor cells efficiently. This method achieves therapeutic effects with high performance in 26 types of tumor cell lines. Animal experiments show that the 30 d survival rate of this therapeutic strategy is much higher than that with traditional drug treatment. Real‐time imaging/monitoring and its effects on the intelligent tumor therapy are also demonstrated based on the stable, strong, green emission from FN‐CDs.
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) | 0.001 | 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".