Biogenic Silver Nanoparticles: Assessment of Their Cytotoxicity, Genotoxicity and Study of Capping Proteins.

The development of nanotechnology in the last two decades has led to the use of silver nanoparticles (AgNPs) in various biomedical applications, including antimicrobial, anti-inflammatory, and anticancer therapies. However, the potential of the medical application of AgNPs depends on the safety of their use. In this work, we assessed the in vitro cytotoxicity and genotoxicity of silver nanoparticles and identified biomolecules covering AgNPs synthesized from actinobacterial strain SH11. The cytotoxicity of AgNPs against MCF-7 human breast cancer cell line and murine macrophage cell line RAW 264.7 was studied by MTT assay, cell LDH (lactate dehydrogenase) release, and the measurement of ROS (reactive oxygen species) level while genotoxicity in Salmonella typhimurium cells was testing using the Ames test. The in vitro analysis showed that the tested nanoparticles demonstrated dose-dependent cytotoxicity against RAW 264.6 macrophages and MCF-7 breast cancer cells. Moreover, biosynthesized AgNPs did not show a mutagenic effect of S. typhimurium. The analyses and identification of biomolecules present on the surface of silver nanoparticles showed that they were associated with proteins. The SDS-PAGE (sodium dodecyl sulfate-polyacrylamide gel electrophoresis) analysis revealed the presence of 34 and 43 kDa protein bands. The identification of proteins performed by using LC-MS/MS (liquid chromatography with tandem mass spectrometry) demonstrated their highest homology to bacterial porins. Capping biomolecules of natural origin may be involved in the synthesis process of AgNPs or may be responsible for their stabilization. Moreover, the presence of natural proteins on the surface of bionanoparticles eliminates the postproduction steps of capping which is necessary for chemical synthesis to obtain the stable nanostructures required for application in medicine.

Artificial intelligence outperforms human students in conducting neurosurgical audits.

OBJECTIVES: Neurosurgical audits are an important part of improving the safety, efficiency and quality of care but require considerable resources, time, and funding. To that end, the advent of the Artificial Intelligence-based algorithms offered a novel, more economically viable solution. The aim of the study was to evaluate whether the algorithm can indeed outperform humans in that task. PATIENTS & METHODS: Forty-six human students were invited to inspect the clinical notes of 45 medical outliers on a neurosurgical ward. The aim of the task was to produce a report containing a quantitative analysis of the scale of the problem (e.g. time to discharge) and a qualitative list of suggestions on how to improve the patient flow, quality of care, and healthcare costs. The Artificial Intelligence-based Frideswide algorithm (FwA) was used to analyse the same dataset. RESULTS: The FwA produced 44 recommendations whilst human students reported an average of 3.89. The mean time to deliver the final report was 5.80 s for the FwA and 10.21 days for humans. The mean relative error for factual inaccuracy for humans was 14.75 % for total waiting times and 81.06 % for times between investigations. The report produced by the FwA was entirely factually correct. 13 out of 46 students submitted an unfinished audit, 3 out of 46 made an overdue submission. Thematic analysis revealed numerous internal contradictions of the recommendations given by human students. CONCLUSION: The AI-based algorithm can produce significantly more recommendations in shorter time. The audits conducted by the AI are more factually accurate (0 % error rate) and logically consistent (no thematic contradictions). This study shows that the algorithm can produce reliable neurosurgical audits for a fraction of the resources required to conduct it by human means.