No abstract

Population growth and increased production demands on fruit and vegetables have driven agricultural production to new heights. Nevertheless, agriculture remains one of the least optimized industries, with laboratory tests that take days to provide a clear result on the chemical level of produce. To address this problem, we developed a tailor-made solution for the industry that can allow multiple field tests on key pesticides, based on a bioelectric cell biosensor and the measurement of the cell membrane potential changes, according to the principle of the Bioelectric Recognition Assay (BERA). We developed a fully functional system that operates using a newly developed hardware for multiple data sources and an Android application to provide results within 3 min. The presence of acetamiprid residues caused a cell membrane hyperpolarization, which was distinguishable from the control samples. A database that classified samples Below or Above Maximum Residue Levels (MRL) was then created, based on a newly developed algorithm. Additionally, lettuce samples were analyzed with the conventional and the newly developed method, in parallel, revealing a high correlation on sample classification. Thus, it was demonstrated that the novel biosensor system could be used in the food supply chain to increase the number of tested products before they reach the market.

Water-level sensors are indispensable for monitoring the level of water in storage tanks, which are used in drinking water distribution networks. In this paper, a long-range capacitive-type water-level sensor is presented. The proposed sensor is constructed using widely-available multilayer tubes, which are used for building drinking water systems. Thus, both the manufacturing cost of the sensor and the cost of the associated electronic circuits, which are used for interfacing the sensor to a digital data-acquisition unit, are low. The performance of the proposed sensor has been evaluated in a water storage tank of a city-scale water distribution network. The experimental results indicate that the accuracy of the proposed experimental prototype sensor is equivalent to that of a commercially available ultrasound water level sensor, while, additionally, its manufacturing cost is significantly lower.

Introduction As the second wave of COVID-19 pandemic is in progress the development of fast and cost-effective approaches for diagnosis is essential. The aim of the present study was to develop and evaluate the performance characteristics of a new Bioelectric Recognition Assay (BERA) regarding Sars-CoV-2 detection in clinical samples and its potential to be used as a point of care test. Materials and methods All tests were performed using a custom portable hardware device developed by EMBIO DIAGNOSTICS (EMBIO DIAGNOSTICS Ltd, Cyprus). 110 positive and 136 negative samples tested by RT-PCR were used in order to define the lower limit of detection (L.O.D.) of the system, as well as the sensitivity and the specificity of the method. Results The system was able to detect a viral concentration of 4 genome copies/μL. The method displayed total sensitivity of 92.7 % (95 %CI: 86.2–96.8) and 97.8 % specificity (95 %CI: 93.7–99.5). When samples were grouped according to the recorded Ct values the BERA biosensor displayed 100.00 % sensitivity (95 %CI: 84.6–100.0) for Ct values <20−30. For the aforementioned Ct values the Positive Predictive Value (PPV) of the method was estimated at 31.4 % for COVID-19 prevalence of 1% and at 70.5 % for 5% prevalence. At the same time the Negative Predictive Value (NPV) of the BERA biosensor was at 100.0 % for both prevalence rates. Conclusions EMBIO DIAGNOSTICS BERA for the detection of SARS-CoV-2 infection has the potential to allow rapid and cost-effective detection and subsequent isolation of confirmed cases, and therefore reduce household and community transmissions.

Human food-borne diseases caused by pathogenic bacteria have been significantly increased in the last few decades causing numerous deaths worldwide. The standard analyses used for their detection have significant limitations regarding cost, special facilities and equipment, highly trained staff, and a long procedural time that can be crucial for foodborne pathogens with high hospitalization and mortality rates, such as Listeria monocytogenes. This study aimed to develop a biosensor that could detect L. monocytogenes rapidly and robustly. For this purpose, a cell-based biosensor technology based on the Bioelectric Recognition Assay (BERA) and a portable device developed by EMBIO Diagnostics, called B.EL.D (Bio Electric Diagnostics), were used. Membrane engineering was performed by electroinsertion of Listeria monocytogenes homologous antibodies into the membrane of African green monkey kidney (Vero) cells. The newly developed biosensor was able to detect the pathogen’s presence rapidly (3 min) at concentrations as low as 102 CFU mL−1, demonstrating a higher sensitivity than most existing biosensor-based methods. In addition, lack of cross-reactivity with other Listeria species, as well as with Escherichia coli, was shown, thus, indicating biosensor’s significant specificity against L. monocytogenes.