Rapid synthesis of bovine serum albumin-conjugated gold nanoparticles using pulsed laser ablation and their anticancer activity on hela cells
Bibliographic record
Abstract
Nanoscience research aims to produce nanoparticles without adverse effects for medical applications. The pulsed laser ablation (PLA) technique was utilized in this study to synthesize gold nanoparticles (AuNPs) using bovine serum albumin (BSA) in simulated body fluid (SBF) at the fundamental wavelength of the Nd: YAG laser (1064 nm). BSA acted as a stabilizer, reducing and capping agent to produce spherically shaped AuNPs (diameter 3–10 nm). The successful synthesis of AuNPs was confirmed through color changes and UV–vis spectroscopy. The agglomeration and precipitation of AuNPs are attributed to the presence of BSA in the solution, and electrostatic repulsion interactions between BSA and Au nanoclusters. The effect of salt concentration of SBF on BSA stability as well as the interaction of BSA conjugated AuNPs to form complexes was studied using molecular dynamic simulations. Our results show that the stability of AuNPs-BSA conjugates increase with the salt concentration of BSA. Moreover, the synthesized AuNPs exhibit low toxicity and high biocompatibility, supporting their application in drug delivery. Investigation of the cytotoxic effect of the synthesized AuNPs show that normal fibroblast cells (L929) remain intact after treatment whereas a dose-dependent inhibition effect on the growth of cervix cancer cells (HeLa) is observed. In general, this study presents an effective, environmentally-friendly, and facile approach to the synthesis of multifunctional AuNPs using the PLA technique, as a promising efficacious therapeutic treatment of cervical 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 | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".