Nanodiagnostic and Nanotherapeutic Molecular Platforms for Cancer Management
Bibliographic record
Abstract
Over the last ten years rapid progress is being made regarding the incorporation of nanoparticles in cancer diagnosis and treatment. Besides the limitations that have to be addressed, there are various research studies suggesting some promising nanodiagnostic and nanotherapeutic platforms for cancer managment. Nanotherapeutic platforms are based on the localized application of nanoparticles using targeting moieties, most usually antibodies, in order to in vivo direct nanoparticles to cancer cells. Thereafter, either nanoparticles react to external stimulus, for example under radiofrequency waves nanoparticles generate thermal energy, or they are used for targeted drug-delivery platforms, which allows the augmentation of drug concentration in the cancerous site of the body and thus minimizing side effects and increasing the efficacy of the drug. Regarding nanodiagnostics, particular focus is paid on nanoparticles that can act as contrast agents in cancer imaging for in vivo nanodiagnostics and on nanobiochips and nanobiosensor, devices that incorporate the lab on a chip notion for in vitro nanodiagnostics. In this review, several advanced nanodiagnostic and nanotherapeutic platforms are discussed, on the development of more effective and targeted molecular techniques in the diagnosis and treatment of cancer.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".