ABSTRACT
Methods based on nucleic acid detection are currently the most commonly used technique in COVID-19 diagnostics. Although generally considered adequate, these methods are characterised by quite a long time-to-result and the necessity to prepare the material taken from the examined person-RNA isolation. For this reason, new detection methods are being sought, especially those characterised by the high speed of the analysis process from the moment of sampling to the result. Currently, serological methods of detecting antibodies against the virus in the patient's blood plasma have attracted much attention. Although they are less precise in determining the current infection, such methods shorten the analysis time to several minutes, making it possible to consider them a promising method for screening tests in people with suspected infection. The described study investigated the feasibility of a surface plasmon resonance (SPR)-based detection system for on-site COVID-19 diagnostics. A simple-to-use portable device was proposed for the fast detection of anti-SARS-CoV-2 antibodies in human plasma. SARS-CoV-2-positive and -negative patient blood plasma samples were investigated and compared with the ELISA test. The receptor-binding domain (RBD) of spike protein from SARS-CoV-2 was selected as a binding molecule for the study. Then, the process of antibody detection using this peptide was examined under laboratory conditions on a commercially available SPR device. The portable device was prepared and tested on plasma samples from humans. The results were compared with those obtained in the same patients using the reference diagnostic method. The detection system is effective in the detection of anti-SARS-CoV-2 with the detection limit of 40 ng/mL. It was shown that it is a portable device that can correctly examine human plasma samples within a 10 min timeframe.
Subject(s)
COVID-19 , Humans , COVID-19/diagnosis , SARS-CoV-2 , Surface Plasmon Resonance , COVID-19 Testing , Antibodies, ViralABSTRACT
In the recent COVID-19 outbreak, chest X-rays were the main tool for diagnosing and monitoring the pathology. To prevent further spread of this disease, special circuits had to be implemented in the healthcare services. For this reason, these chest X-rays were captured with portable X-ray devices that compensate its lower quality and limitations with more deployment flexibility. However, most of the proposed computer-aided diagnosis methodologies were designed to work with traditional fixed X-ray machines and their performance is diminished when faced with these portable images. Additionally, given that the equipment needed to properly treat the disease (such as for life support and monitoring of vital signs) most of these systems learnt to identify these artifacts in the images instead of real clinically-significant variables. In this work, we present the first methodology forced to extract features exclusively from the pulmonary region of interest that is specially designed to work with these difficult portable images. Additionally, we generate a class activation map so the methodology also provides explainability to the results returned to the clinician. To ensure the robustness of our proposal, we tested the methodology with chest radiographs from patients diagnosed with COVID-19, pathologies similar to COVID-19 (such as other types of viral pneumonias) and healthy patients in different combinations with three convolutional networks from the state of the art (for a total of 9 studied scenarios). The experimentation confirms that our proposal is able to separate COVID-19 cases, reaching a 94.7% ± 1.34% of accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Corona virus disease 2019 (COVID-19) is a serious contagious disease that arises from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease is transmitted primarily through droplets transmission, which does not exclude the possibility of transmission through aerosols and digestive tract and other means. The majority of COVID-19 cases present as common and mild, while a few cases progress rapidly to the severe and critical stage, making it more difficult to save the disease. Current laboratory diagnostic approaches for COVID-19 are on the basis of pathogenic tests for viral nucleic acids, serological tests by specific antibody detection, and general tests. For now, Real-time quantitative PCR (qRT-PCR) is the gold standard for the detection of COVID-19, but the method is complicated and may have false-negative results. Therefore, in order to diagnose and prevention COVID-19 more effectively, it is needful to develop highly sensitive diagnostic methods with portable instruments and visualized results. This mini-review provides an overview of the current portable and visual assays of detection of COVID-19, in order to achieve early effective and more accurate diagnosis of COVID-19.
ABSTRACT
Antarctica is a space-analog ICE (isolated, cold, and extreme) environment. Cardiovascular and heart autonomic adjustments are key-adaptive physiological responses to Antarctica, both in summer camps and in research stations winter-over. Research fieldwork in ICE environments imposes limitations such as energy restriction, the need for portable and easy-to-handle resources, and resistance of materials to cold and snow/water. Herein, we present the methods we use for cardiac monitoring in the Antarctic field, the limitations of the equipment currently available, and the specific demands for smart wearables to physiological and health tracking in ICE environments, including the increased remote monitoring demand due to COVID-19 restrictions.
Subject(s)
Extreme Environments , Wearable Electronic Devices , Antarctic Regions , HumansABSTRACT
The COVID-19 pandemic has devastated the world, despite all efforts in infection control and treatment/vaccine development. Hospitals are currently overcrowded, with health statuses of patients often being hard to gauge. Therefore, methods for determining infection severity need to be developed so that high-risk patients can be prioritized, resources can be efficiently distributed, and fatalities can be prevented. Electrochemical prognostic biosensing of various biomarkers may hold promise in solving these problems as they are low-cost and provide timely results. Therefore, we have reviewed the literature and extracted the most promising biomarkers along with their most favourable electrochemical sensors. The biomarkers discussed in this paper are C-reactive protein (CRP), interleukins (ILs), tumour necrosis factor alpha (TNFα), interferons (IFNs), glutamate, breath pH, lymphocytes, platelets, neutrophils and D-dimer. Metabolic syndrome is also discussed as comorbidity for COVID-19 patients, as it increases infection severity and raises chances of becoming infected. Cannabinoids, especially cannabidiol (CBD), are discussed as a potential adjunct therapy for COVID-19 as their medicinal properties may be desirable in minimizing the neurodegenerative or severe inflammatory damage caused by severe COVID-19 infection. Currently, hospitals are struggling to provide adequate care; thus, point-of-care electrochemical sensor development needs to be prioritized to provide an approximate prognosis for hospital patients. During and following the immediate aftermath of the pandemic, electrochemical sensors can also be integrated into wearable and portable devices to help patients monitor recovery while returning to their daily lives. Beyond the COVID-19 pandemic, these sensors will also prove useful for monitoring inflammation-based diseases such as cancer and cardiovascular disease.