Recent advances in NIR-II fluorescence based theranostic approaches for glioma
Bibliographic record
Abstract
Gliomas are among the most common malignant tumors in the central nervous system and lead to poor life expectancy. However, the effective treatment of gliomas remains a considerable challenge. The recent development of near infrared (NIR) II (1000–1700 nm) theranostic agents has led to powerful strategies in diagnosis, targeted delivery of drugs, and accurate therapy. Because of the high capacity of NIR-II light in deep tissue penetration, improved spatiotemporal resolution can be achieved to facilitate the in vivo detection of gliomas via fluorescence imaging, and high contrast fluorescence imaging guided surgery can be realized. In addition to the precise imaging of tumors, drug delivery nano-platforms with NIR-II agents also allow the delivery process to be monitored in real-time. In addition, the combination of targeted drug delivery, photodynamic therapy, and photothermal therapy in the NIR region significantly improves the therapeutic effect against gliomas. Thus, this mini-review summarizes the recent developments in NIR-II fluorescence-based theranostic agents for glioma treatment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".