key: cord-0797388-civwyvzh
authors: Apostolou, Theofylaktos; Kyritsi, Maria; Vontas, Alexandros; Loizou, Konstantinos; Hadjilouka, Agni; Speletas, Mathaios; Mouchtouri, Varvara; Hadjichristodoulou, Christos
title: Development and performance characteristics evaluation of a new Bioelectric Recognition Assay (BERA) method for rapid Sars-CoV-2 detection in clinical samples
date: 2021-04-16
journal: J Virol Methods
DOI: 10.1016/j.jviromet.2021.114166
sha: 55710e56ea7a5e561144637eaa433621bf2d64bb
doc_id: 797388
cord_uid: civwyvzh

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.

In early January 2020, a hitherto unknown coronavirus -now called Severe Acute Respiratory Syndrome Coronavirus -2 (SARS-CoV-2), was identified as the leading cause of a group of suspected pneumonia cases in Wuhan, China [1] . Due to the rapid spread of the virus, by the end of January 2020, the World Health Organization (WHO) declared a public health emergency as an international concern [2] .

Until presently, the gold standard method for COVID-19 diagnosis is the detection of SARS-CoV-2 genetic material with real-time PCR (RT-PCR) [3] , and although the amplification process can be completed in a relatively short timeframe, the stages of extraction, sample processing and data management (including reporting) can be time consuming and lead to a turnaround result time 24 to 48 hours. In addition, special equipment and trained personnel is required in order to perform the analysis resulting in high cost of the RT-PCR test.

As the third wave of the pandemic is in progress the development of fast and cost-effective approaches in order to control the COVID-19 pandemic is essential.

Furthermore, since the clinical manifestations at the onset of COVID-19 resemble to other respiratory infections such as influenza, it is vital to develop methods for rapidly confirming or clearing suspected cases during global outbreak scenarios.

To date the point of care (POC) tests that have been developed for rapid diagnosis of SARS-CoV-2 infection, belong in three main categories; molecularbased, antigen and biosensor technologies. In recent years, automated, single-step RT-PCR methods as well as other nucleic acid amplification methods, such as isothermal amplification that do not require the sophisticated thermo-cycling involved in RT-PCR, have been developed [4] . These technological advances have allowed J o u r n a l P r e -p r o o f molecular technologies to be developed that are suitable for use in a point-of-care context [5] . In a recent evaluation of Dinnes et al., published at the Cochrane database the average sensitivity of rapid molecular-based POC tests was 95.2% (95%CI: 86.7-98.0) and specificity 98.9% (95%CI 97.3-99.5) [6] . These methods have the potential to reduce the time to produce test results after extraction and sample processing to minutes, but the time for the whole process may still be significant and the cost of the analysis is usually higher than RT-PCR.

Rapid antigen tests are mainly based on lateral flow immunoassays. Viral antigen is captured by specific antibodies and detected by a secondary virus-specific antibody that is labelled with an enzyme, fluorophore or colloidal gold. Their use is simple, they are cheap but as reported in the previous study their sensitivity varied considerably across studies (from 0% to 94%): the average sensitivity was 56.2% (95%CI: 29.5-79.8) and average specificity was 99.5% (95%CI: 98.1-99.9) [6] .

On the other hand, biosensors are considered fast, cost-effective, portable, and sensitive detectors that could be a promising diagnostic method.. The principle of operation of biosensors is based on their ability to detect the target and turn this recognition into a detectable signal [7, 8] . At the same time, due to their high sensitivity, low manufacturing costs (approximately 9 euros with the potential of reducing to 2 euros after mass production), and size, they are excellent candidates for the development of portable biosensors [9] . The reagents usually used for the detection are enzymes, antibodies, or whole cells. Immunosensors, unlike enzyme biosensors that assess overall toxicity, have the ability to be specific for a molecule. This is achieved due to the high affinity of the antibodies (Ab) or antigens (Ag) that are immobilized on the transducer surface relative to the target analysers that are Ag or Ab respectively [10] . J o u r n a l P r e -p r o o f Live, cell-based biosensors have been shown to have high selectivity, sensitivity, and fast response times. Such detection systems, like the Bioelectric Recognition Assay (BERA), have been used in environmental, chemical and medical applications [11] which have shown unique and measurable changes on the electrical properties of the bio-recognition elements [12, 13, 14] when the target molecules bind to electro-inserted antibodies.

The aim of the present study was to develop and evaluate the performance characteristics (sensitivity-specificity) of a new BERA -membrane biosensor method regarding Sars-CoV-2 detection in naso-and oro-pharyngeal swabs and its potential to be used as a reliable POC test.

All tests were performed using a custom portable hardware device (Bio In addition, the system can be connected via Bluetooth 4.0 to a smartphone, thus allowing the end user to be informed immediately of any analysis result [14] . The device is shown in Figure 1 . Eppendorf AG, Germany) cuvettes (4mm) and electroinserted by applying two square electrical pulses at 1800 V / cm. The mixture was then transferred to a Petri dish (60 x 15 mm 2 ) containing 3 mL of medium and incubated at 37 o C and 5% CO2 for 24 hours. The medium was then discarded from the Petri dish and the Vero / anti-S1 cells were mechanically removed and collected with the medium in Eppendorf tubes.

In order to define the LOD of the system we used a positive sample with a viral concentration of 4x10 11 The PCR conditions were set according to the manufacturer's indications.

The BERA method offers the opportunity for virus detection in minutes, without the use of any extraction protocol. To achieve this, the matrix effect of various VTM should be addressed [15] . After testing a series of VTM, the different responses that were observed on their potential difference (millivolts) were analyzed.

A specific algorithms were then developed for each VTM to assess the detection of SARS-CoV-2.

Data were uploaded on the online database using Google Firestore and Google Cloud Functions to run analytics. A specific algorithm which was developed according to a previously described procedure [16] was used to produce/calculate the final results.

There are four different stages prior to result analysis: Training/test data set: The data set was divided into training and test data sets. The training data set was used to determine the algorithm limits and the test data set was used to evaluate the algorithm.

Editing / Exporting features: The data set was processed in a two-step process. In the first step, the background noise was subtracted to normalize and calibrate the signal and in the second step, the purified data was used as input for the development of an algorithm capable of detecting positive and negative samples. From the experiments, we observed a biosensor response due to the enhanced matrix effect of the Viral Transport Media (VTM). Consequently, further research to identify and eradicate possible impediments due to the matrix effect is required for the optimization of the system. On the other hand, utilizing the receiver operating characteristic (ROC) analysis was possible to define the optimal model for increasing the specificity of the system, having in mind that this will decrease the sensitivity of the developed system.

A ROC curve was designed (not shown) in order to illustrate the diagnostic ability of the system as its discrimination threshold was varied. To draw a ROC curve, only the true-positive rate (TPR) and the false-positive rate (FPR) were needed. A ROC space was defined by FPR and TPR as x and y axes, respectively, which depicted relative trade-offs between true positive and false positive.

The biosensor, based on Vero/anti-S1 cells membrane-engineered was able to give a positive signal when a positive sample with a viral concentration of approximately 4 gc/μL or 4x10 3 gc/mL (Ct value=37) was analyzed. The biosensor measurements at each dilution, as shown in Figure 3 , were distinct and significantly different from the control solution (i.e. the solution where the dilutions were made) as well as from the dilutions 10 -9 and 10 -10 which were not detected with RT-PCR.

One hundred and two (102) out of one hundred ten (110) 

As the COVID-19 pandemic continues, smart sensors for rapid and reliable SARS-CoV-2 detection would better facilitate pandemic management and impact analysis.

In the present study we developed a robust cell-based biosensor technology known as the Bioelectric Recognition Assay (BERA) for the POC detection of SARS-CoV-2 S1 spike protein in naso-or oro-pharyngeal samples. The method was performed using a membrane-engineered procedure. The response was measured The technology was then evaluated and the LOD, sensitivity and specificity were defined. The method proved to be extremely fast (up to 180s) and with a low limit for virus detection (4 gc/μL).

The results in this study support the BERA method for detection of SARS-CoV-2 virus and are in accordance with a previous study conducted by Mavrikou et al. [22] . The method was able to detect low viral loads and moreover proved to be a In contrast, other promising sensing systems that have been developed for rapid Sars-CoV-2 detection as the CRISPR-based test [17] , the dual functional SARS-CoV-2 plasma gene sensor [18] , a field effect transistor (FET) -sensitizer [19] , and a lateral flow immunoassay (LFIA) [20] , lack supportive studies and need further validation before their application as potential COVID-19 diagnostic tools.

Recently, many studies have been conducted in order to evaluate the performance characteristics of various commercially available POC antigen tests. and AAZ tests showed a relatively satisfactory total sensitivity (55-62%) that reached 87-96% for Ct values <25 [23] . The EMBIO BERA test demonstrated superior sensitivity, from the tests mentioned above, that is 98.9% for Ct values <35, and 100% for Ct values <25 (Table 1) .

Concerning specificity, CORIS, ABBOTT, NG BIOTECH and AAZ reached 100%, BIOSENSOR and BIOSYNEX 93.2% and 98.5% respectively [23] , while the sensitivity of EMBIO BERA test was 97.8%. Even though our method demonstrated a slightly lower specificity, it is worth mentioning that the Negative Predictive Value of the method, when Ct values were <20-30, was 100% (for prevalence rates both 1% and 5%). The advantages/disadvantages of the BERA method as well as the comparison to commercially available POC assays are summarized at Table 2 .

Concerning the issue of the results, our approach of a SARS-CoV-2 detection test connected to a smartphone, is in accordance with the study of Song et al. [21] .

In this study, the Internet of Things (IoT) -based combinatorial approaches to sensor participation, IT sharing, AI and dynamic networks were very useful for healthcare professionals in assessing COVID-19 full-spectrum perception, reliable transmission and smart processing.

The present study has some limitations. First of all, no samples of patients infected with other coronaviruses were used in order to check for cross reactivity.

However, in the study of Mavrikou et al. [22] using antibodies, enzymes and other receptor-like molecules against distinct domains within the S1 subunit, the novel biosensor assay could detect different coronaviruses or even other SARS-CoV-2 variants.

At the time the experiments were performed in Greece, the burden of the disease was low so the number of positive samples tested was also relatively low.

Further experiments with more samples are required and will be performed in the near future.

The issue of cell viability remains of crucial importance. Various approaches have been proposed to overcome this limitation (microfluidic/organ-on-chip circuits in biosensing platforms or specific cell types), but so far none of these approaches have proven to be suitable and cost efficient for routine mass-screening applications [15] . The production rate of the assay consumables and reagents for mass-scale testing remains a challenge. 

The work described here is an original submission that has not been published before and that is not currently under consideration for publication elsewhere. Corresponding author is Prof. 

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Table 1 . Specificity and sensitivity of the method according to Ct values and different prevalence rates of the disease.

Sensitivity, that is the probability that a test result will be positive when the disease is present (true positive rate) was calculated for each group according to the following type: a / (a+b)  Specificity, that is the probability that a test result will be negative when the disease is not present (true negative rate) was calculated according to the following type: d / (c+d)  Positive predictive value, that is the probability that the disease is present when the test is positive was calculated for each group according to the following type:

 Negative predictive value, that is the probability that the disease is not present when the test is negative was calculated for each group according to the following type: 

China Novel Coronavirus Investigating and Research Team. A novel coronavirus from patients with pneumonia in China

China coronavirus: WHO declares international emergency as death toll exceeds 200

Real-Time PCR in Clinical Microbiology: Applications for Routine Laboratory Testing

Assay Techniques and Test J o u r n a l P r e -p r o o f Development for COVID-19 Diagnosis

Point-of-Care Testing for Infectious Diseases: Past, Present, and Future

Rapid, point-of-care antigen and molecular-based tests for diagnosis of SARS-CoV-2 infection

Biosensors as useful tools for environmental analysis and monitoring

Biosensors for effective environmental and agrifood protection and commercialization: from research to market

Electrochemical biosensors

Immunoanalytical Techniques for Analyzing J o u r n a l P r e -p r o o f Pesticides in the Environment

Cell-based sensors in clinical chemistry

Development of a cellular biosensor for the detection of aflatoxin B1, based on the interaction of membrane engineered Vero cells with anti-AFB1 antibodies on the surface of gold nanoparticle screen printed electrodes

Application of "membrane-engineering" to bioelectric recognition cell sensors for the detection of picomole concentrations of superoxide radical: a novel biosensor principle

Newly developed system for acetamiprid residue screening in the lettuce samples based on a bioelectric cell biosensor

Matrix Effect Study and Immunoassay Detection Using Electrolyte-Gated Graphene Biosensor. Micromachines

Newly Developed System for the Robust Detection of Listeria monocytogenes Based on a

CRISPR− Cas12-Based Detection of SARS-CoV-2

Dual-Functional Plasmonic Photothermal Biosensors for Highly Accurate Severe Acute Respiratory Syndrome Coronavirus 2 Detection

Rapid Detection of COVID-19 Causative Virus (SARS-CoV-2) in Human Nasopharyngeal Swab Specimens Using Field-Effect Transistor-Based Biosensor

Rapid and Sensitive Detection of AntiSARS-CoV-2 IgG, Using Lanthanide-Doped Nanoparticles-Based Lateral Flow Immunoassay

Prospect and Application of Internet of Things Technology for Prevention of SARIs

Development of a Portable, Ultra-Rapid and Ultra-Sensitive Cell-Based Biosensor for the Direct Detection of the SARS-CoV-2 S1 Spike Protein Antigen

Evaluation de la performance diagnostique des tests rapides d'orientation diagnostique antigeniques COVID-19. Laboratoire de Virologie et le service de santé publique des Hôpitaux Universitaires Henri-Mondor

Newly Developed System for the Robust Detection of Listeria monocytogenes Based on a

Concentration-dependent biosensor responses against the SARS-CoV-2 spike S1 protein. A concentration-dependent response was observed during the analysis of increasing concentrations of isolated SARS-CoV-2 virus with the new biosensor