This work presents the development and optimisation of an amperometric biosensor for determining aspartate aminotransferase (AST) activity in blood serum, using glutamate oxidase and platinum disc electrodes. AST is a key biomarker for diagnosing cardiovascular and liver diseases. The biosensor's bioselective membrane composition and formation protocol and the working solution (aspartate 8 mM, α-ketoglutarate 2 mM, pyridoxal-5-phosphate 100 µM) were optimised. The sensor demonstrated high selectivity, stability (70% retention over 2 months at - 18 °C), and sensitivity (2.37 nA min⁻ per 10 U L⁻), with a dynamic range of 0-500 U L⁻ and a limit of detection of 1 U L⁻. Comparative analysis showed the calibration curve method outperforms the standard addition method for AST measurement in serum samples. Additionally, a reference spectrophotometric technique was adapted for AST level determination, showing a strong correlation (r = 0.989) with the biosensor results. This research offers a fast, affordable, and accurate tool for early check-ups of liver and heart conditions. The biosensor's flexibility and ease of use make it suitable for further development into point-of-care testing and personalised healthcare techniques.
This paper develops a method for cancer classification from microRNA data using a convolutional neural network (CNN)-based model optimized by genetic algorithm. The convolutional neural network has performed well in various recognition and perception tasks. This paper contributes to the cancer classification using a union of two CNNs.
This research was devoted to optimising opportunistic tamping scheduling to present a cost-effective approach that considers both preventive and corrective tamping activities. To achieve this, we formulated the track geometry tamping scheduling problem as a mixed integer linear programming model and employed a genetic algorithm for its resolution. Key track quality indicators, including the standard deviation of the longitudinal level and single defects, were considered.
Introduction: Laparoscopic transperitoneal adrenalectomy was first described by Gagner M et al. Here, we present our experience of more than two decades of laparoscopic adrenalectomy performed in a single surgical unit at a tertiary care centre.
Patients And Methods: A prospectively collected database of patients undergoing laparoscopic adrenalectomy from December 1994 to May 2020 was analysed retrospectively.
The aim of study was to prepared and evaluated rutin-loaded solid-lipid-nanoparticles (Ru-SLNs) gel for treatment of melanoma cells. SLNs were prepared by ultrasonication method through optimisation and evaluated their mean-diameter, PDI, zeta-potential, morphology, entrapment-efficiency, drug-loading, interaction by FTIR, in vitro skin permeation, stability, antioxidant/MTT assay and fluorescence microscopic. Further developed Ru-SLNs was incorporated into gel and characterised their physicochemical properties, drug contents, in vitro diffusion, ex vivo permeation and retention studies in human cadaver skin.
Medical Image Optimisation and Perceptions Group, Discipline of Medical Imaging Science, Faculty of Medicine and Health, The University of Sydney, Australia. Electronic address:
Introduction: Radiation Dose Monitoring Software (DMS) tools have been developed to monitor doses and alert computed tomography (CT) users of high radiation exposure. However, the causal factors for alerts and the impact of DMS in dose optimisation are poorly understood.
Aim: This review aims to identify high-dose CT examinations triggering alerts and their determinants, and to assess if the alerts from DMS help to reduce CT dose levels.
