A Comprehensive Review of Gold Nanoparticles in Clinical Trials: Efficacy, Safety and Future Directions
Bibliographic record
Abstract
The rapid advancement of nanoscience in the 21st century has propelled gold nanoparticles (GNPs) to the forefront of nanomedicine research. Despite decades of intensive investigation, the clinical translation of GNPs has been hindered by concerns regarding their long-term toxicity. However, recent studies demonstrate that GNPs exhibit high biocompatibility, and emerging clinical trial data suggest that GNP-based therapies are approaching practical medical application. Interest and activity in this field have surged, and more clinical trial data on GNPs are now available than ever before. This review synthesizes findings from 33 peer-reviewed clinical studies involving 918 patients, covering diverse applications in oncology, cardiology, dermatology, nuclear imaging, oral health, vaccine delivery, and neurology. We provide a comprehensive overview of the current clinical landscape of GNPs, critically evaluating efficacy and safety outcomes, and highlighting key trends and future challenges facing the clinical adoption of GNPs.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".