Recent Advances in Functionalized Nanoparticles in Cancer Theranostics
Bibliographic record
Abstract
Cancer theranostics is the combination of diagnosis and therapeutic approaches for cancer, which is essential in personalized cancer treatment. The aims of the theranostics application of nanoparticles in cancer detection and therapy are to reduce delays in treatment and hence improve patient care. Recently, it has been found that the functionalization of nanoparticles can improve the efficiency, performance, specificity and sensitivity of the structure, and increase stability in the body and acidic environment. Moreover, functionalized nanoparticles have been found to possess a remarkable theranostic ability and have revolutionized cancer treatment. Each cancer treatment modality, such as MRI-guided gene therapy, MRI-guided thermal therapy, magnetic hyperthermia treatment, MRI-guided chemotherapy, immunotherapy, photothermal and photodynamic therapy, has its strengths and weaknesses, and combining modalities allows for a better platform for improved cancer control. This is why cancer theranostics have been investigated thoroughly in recent years and enabled by functionalized nanoparticles. In this topical review, we look at the recent advances in cancer theranostics using functionalized nanoparticles. Through understanding and updating the development of nanoparticle-based cancer theranostics, we find out the future challenges and perspectives in this novel type of cancer 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".