Nanostructured silver (Ag) and gold (Au) are widely known to be potent biocidal and cytotoxic agents as well as biocompatible nanomaterials. It has been recently reported that combining both metals in a specific chemical composition causes a significant enhancement in their antibacterial activity against antibiotic-resistant bacterial strains, as well as in their anticancer effects, while preserving cytocompatibility properties. In this work, Ag/Au bimetallic nanoparticles over a complete atomic chemical composition range were prepared at 10 at% through a green, highly reproducible, and simple approach using starch as a unique reducing and capping agent. The noble metal nanosystems were thoroughly characterized by different analytical techniques, including UV-visible and FT-IR spectroscopies, XRD, TEM/EDS, XPS and ICP-MS. Moreover, absorption spectra simulations for representative colloidal Ag/Au-NP samples were conducted using FDTD modelling. The antibacterial properties of the bimetallic nanoparticles were determined against multidrug-resistant Escherichia coli and methicillin-resistant Staphylococcus aureus, showing a clear dose-dependent inhibition even at the lowest concentration tested (5 µg/mL). Cytocompatibility assays showed a medium range of toxicity at low and intermediate concentrations (5 and 10 µg/mL), while triggering an anticancer behavior, even at the lowest concentration tested, in a process involving reactive oxygen species production per the nanoparticle Au:Ag ratio. In this manner, this study provides promising evidence that the presently fabricated Ag/Au-NPs should be further studied for a wide range of antibacterial and anticancer applications.

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Fatigue decreases performance in several professional activities. Fatigue can lead to commit technical mistakes which consequences might be lethal, such as in health area, where a surgical error due to the absence of rest can provoke the patient death. Therefore, this study aims to detect vigil and fatigue (due to lack of sleep) states in medical students through the classification of electroencephalographic (EEG) patterns. The EEG signals of 18 physician students were analyzed within theta band (4 - 8 Hz) over front-central recording sites, and alpha band (8 - 13 Hz) rhythms over temporal and parieto-occipital recording sites during the execution of laparoscopic tasks before and after their medical duties. The EEG signal processing pipeline consisted in pre-processing based on individual component analysis, absolute band power estimates, and Support Vector Machine classification. The F-score to differ between vigil and fatigue states was 90.89%, where the first class was slightly more identifiable reaching a sensitivity of 90.18%. Based on this outcome, the detection of fatigue in medical students while their laparoscopic training seems achievable and feasible to diminish technical mistakes that could be lethal in health area. For this purpose, EEG recording are provided.

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