Silver and gold nanoparticles as an integral part of nanooncology: current state of the problem
Bibliographic record
Abstract
Gold and silver nanoparticles are easily synthesized and the most effective nanostructures in clinical practice. Their optical properties, ease of synthesis, colloidal stability and the ability to form any surface for easier conjugation with biological particles, the ability to individual multiple use, make them especially important in oncology. Gold and silver nanoparticles are capable of providing targeted drug delivery, which depends on the photothermal, photodynamic, and antiangiogenic properties of the metal. Due to their exceptional properties, these nanoparticles are considered as a potential tool for the diagnosis of various types of cancer and drug delivery. The non-toxic and non-immunogenic nature of gold and silver nanoparticles, as well as their high permeability and retention effect, provide additional advantages, allowing drugs to easily penetrate and accumulate at tumor sites. The effectiveness of precious metal nanoparticles as radiosensitizers, dose enhancers and contrast agents is gaining increasing recognition. An important problem associated with the use of biogenic metal nanoparticles is the study of their genotoxicity, since they play an important role in the initiation and progression of abnormalities, including genetic ones.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".