Recent advances in near infrared light responsive multi-functional nanostructures for phototheranostic applications
Bibliographic record
Abstract
Light-based theranostics have become indispensable tools in the field of cancer nanomedicine. Specifically, near infrared (NIR) light mediated imaging and therapy of deeply seated tumors using a single multi-functional nanoplatform have gained significant attention. To this end, several multi-functional nanomaterials have been utilized to tackle cancer and thereby achieve significant outcomes. The present review mainly focuses on the recent advances in the development of NIR light activatable multi-functional materials such as small molecules, quantum dots, and metallic nanostructures for the diagnosis and treatment of deeply seated tumors. The need for improved disease detection and enhanced treatment options, together with realistic considerations for clinically translatable nanomaterials will be the key driving factors for theranostic agent research in the near future. NIR-light mediated cancer imaging and therapeutic approaches offer several advantages in terms of minimal invasiveness, deeper tissue penetration, spatiotemporal resolution, and molecular specificities. Herein, we have reviewed the recent developments in NIR light responsive multi-functional nanostructures for phototheranostic applications in 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".