Inorganic Nanoplatforms for Simultaneous Cancer Imaging and Therapy: Status and Challenges
Bibliographic record
Abstract
Functional nanomaterials have inspired revolutionary methods for cancer early diagnosis, treatment, and prevention. For instance, the imaging property of nanomaterials with high resolution and sensitivity can be used for noninvasive detection of cancer and visualization of drug transport. Meanwhile, the therapeutic property of nanomaterials with controllable fashion will increase therapy efficacy and decrease adverse side effect. Thus, compared to traditional treatment approaches, the nanomaterials which combines imaging and therapeutic functionalities, will be more suitable for cancer theranostics. This review introduces several types of inorganic nanoparticles, including silica nanoparticles, upconversion nanoparticles, iron oxide nanoparticles and gold nanoparticles, which can been explored as theranostic nanoplatforms for simultaneous cancer imaging and therapy. We also cover the ongoing challenges of these nanoparticles in clinical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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 teacher head, 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".