Gold Nanoparticles Enhance DNA Damage Induced by Anti-cancer Drugs and Radiation
Bibliographic record
Abstract
The chemotherapeutic agent cisplatin was chemically linked to pGEM-3Zf(-) plasmid DNA to produce a cisplatin-DNA complex, Gold nanoparticles, which bind electrostatically to pure DNA, could also be added to this complex. Dry films of pure plasmid DNA and DNA-cisplatin, DNA-gold nanoparticles and DNA-cisplatin-gold nanoparticles complexes were bombarded by 60 keV electrons. The yields of single- and double-strand breaks were measured as a function of exposure by electrophoresis. From a comparison of such yields from the different type of films, we found that the binding of only one gold nanoparticle to a plasmid-cisplatin complex containing 3197 base pairs increases by a factor of 3 the efficiency of the chemotherapeutic agent cisplatin to produce double-strand breaks in irradiated DNA. Furthermore, adding two cisplatin molecules and one gold nanoparticle to DNA enhances radiation-induced DSBs by a factor of 7.5. A number of phenomena could contribute to this huge enhancement, including the higher density of low-energy electrons and reactive species around the gold nanoparticles and the weakening of bonds adjacent to cisplatin in the DNA backbone. The addition of gold nanoparticles to cisplatin and other platinum agents may therefore provide interesting avenues of research to improve the treatment of cancer by concomitant chemoradiation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".