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The unstoppable spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has severely threatened public health over the past 2 years. The current ubiquitously accepted method for its diagnosis provides sensitive detection of the virus; however, it is relatively time-consuming and costly, not to mention the need for highly skilled personnel. There is a clear need to develop novel computer-based diagnostic tools to provide rapid, cost-efficient, and time-saving detection in places where massive traditional testing is not practical. Here, we develop an electrochemiluminescence (ECL)-based detection system whose results are quantified as reverse transcriptase polymerase chain reaction (RT-PCR) cyclic threshold (CT) values. A concentration-dependent signal is generated upon the introduction of the virus to the electrode and is recorded with a smartphone camera. The ECL images are used to train machine learning algorithms, and a model using artificial neural networks (ANNs) for 45 samples was developed. The model demonstrated more than 90% accuracy in the diagnosis of 50 unknown real samples, detecting up to a CT value of 32 and a limit of detection (LOD) of 10

Coronavirus disease (COVID-19) is a new and highly contagious disease posing a threat to global public health and wreaking havoc around the world. It's caused by the Coronavirus that causes severe acute respiratory syndrome (SARS-CoV-2). In the current pandemic situation, rapid and accurate SARS-CoV-2 diagnosis on a large scale is critical for early-stage diagnosis. Early detection and monitoring of viral infections can aid in controlling and preventing infection in large groups of people. Accordingly, we developed a sensitive and high-throughput sandwich electrochemiluminescence immunosensor based on antigen detection for COVID-19 diagnosis (the spike protein of SARS-CoV-2). For the spike protein of SARS-CoV-2, the ECL biosensor had a linear range of 10 ng mL

Researchers are constantly looking to find new techniques of virus detection that are sensitive, cost-effective, and accurate. Additionally, they can be used as a point-of-care (POC) tool due to the fact that the populace is growing at a quick tempo, and epidemics are materializing greater often than ever. Electrochemiluminescence-based (ECL) biosensors for the detection of viruses have become one of the most quickly developing sensors in this field. Thus, we here focus on recent trends and developments of these sensors with regard to virus detection. Also, quantitative analysis of various viruses (e.g., Influenza virus, SARS-CoV-2, HIV, HPV, Hepatitis virus, and Zika virus) with a specific interest in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was introduced from the perspective of the biomarker and the biological receptor immobilized on the ECL-based sensors, such as nucleic acids-based, immunosensors, and other affinity ECL biosensors.

/-methylphenyl)-2-mercaptothiazole (MPMT) was synthesized from a compound of thiocarbamate family using condensation reaction. The spectroscopic properties of this structure were studied using 1H-NMR, 13C-NMR, FT-IR, Mass Spectroscopy, and elemental analysis methods. Subsequently, its thermodynamic parameters were examined by performing density functional theory (DFT) calculation at B3LYP, B3PW91, and mPW1PW91 levels of theory and 6-311G (d, p) basis set. After studying the values of total energy, entropy, vibrational frequencies, ZPVE, HOMO and LUMO dependant parameters such as Chemical potential, Chemical hardness, Electrophilicity, and maximum amount of electronic charge transfer were also considered. However, chemical shift value of different nuclei were theoretically determined. The results indicated high conformity between the theoretical and experimental values. Finally, the electrostatic potential values of this structure were studied. The results showed variation in the value of this parameter at different part of the structure.