Visual diagnosis of COVID-19 disease based on serum metabolites using a paper-based electronic tongue

Electronic Tongues and Noses: A General Overview

As technology advances, electronic tongues and noses are becoming increasingly important in various industries. These devices can accurately detect and identify different substances and gases based on their chemical composition. This can be incredibly useful in fields such as environmental monitoring and industrial food applications, where the quality and safety of products or ecosystems should be ensured through a precise analysis. Traditionally, this task is performed by an expert panel or by using laboratory tests but sometimes becomes a bottleneck because of time and other human factors that can be solved with technologies such as the provided by electronic tongue and nose devices. Additionally, these devices can be used in medical diagnosis, quality monitoring, and even in the automotive industry to detect gas leaks. The possibilities are endless, and as these technologies continue to improve, they will undoubtedly play an increasingly important role in improving our lives and ensuring our safety. Because of the multiple applications and developments in this field in the last years, this work will present an overview of the electronic tongues and noses from the point of view of the approaches developed and the methodologies used in the data analysis and steps to this aim. In the same manner, this work shows some of the applications that can be found in the use of these devices and ends with some conclusions about the current state of these technologies.

A review on machine learning-powered fluorescent and colorimetric sensor arrays for bacteria identification

Biosensors have been widely used for bacteria determination with great success. However, the "lock-and-key" methodology used by biosensors to identify bacteria has a significant limitation: it can only detect one species of bacteria. In recent years, optical (fluorescent and colorimetric) sensor arrays are gradually gaining attention from researchers as a new type of biosensor. They can acquire multiple features of a target simultaneously, form a feature pattern, and determine the bacteria species with the help of pattern recognition/machine learning algorithms. Previous reviews in this area have focused on the interaction between the sensor array and bacteria or the materials used to make the sensors. This review, on the other hand, will provide researchers with a better understanding of the field by discussing fluorescent and colorimetric sensor arrays based on the mechanism of optical signal generation. These sensor arrays will be compared based on the identified species. Finally, we will discuss the limitations of these sensor arrays and explore possible solutions.

Monitoring saliva compositions for non-invasive detection of diabetes using a colorimetric-based multiple sensor

The increasing population of diabetic patients, especially in developing countries, has posed a serious risk to the health sector, so that the lack of timely diagnosis and treatment process of diabetes can lead to threatening complications for the human lifestyle. Here, a multiple sensor was fabricated on a paper substrate for rapid detection and controlling the progress of the diabetes disease. The proposed sensor utilized the sensing ability of porphyrazines, pH-sensitive dyes and silver nanoparticles in order to detect the differences in saliva composition of diabetic and non-diabetic patients. A unique color map (sensor response) was obtained for each studied group, which can be monitored by a scanner. Moreover, a good correlation was observed between the colorimetric response resulting from the analysis of salivary composition and the fasting blood glucose (FBG) value measured by standard laboratory instruments. It was also possible to classify participants into two groups, including patients caused by diabetes and those were non-diabetic persons with a total accuracy of 88.9%. Statistical evaluations show that the multiple sensor can be employed as an effective and non-invasive device for continuous monitoring of diabetes, substantially in the elderly.

Synergistically functionalized molybdenum disulfide-reduced graphene oxide nanohybrid based ultrasensitive electrochemical immunosensor for real sample analysis of COVID-19

Herein, we have proposed a straightforward and label-free electrochemical immunosensing strategy supported on a glassy carbon electrode (GCE) modified with a biocompatible and conducting biopolymer functionalized molybdenum disulfide-reduced graphene oxide (CS-MoS₂/rGO) nanohybrid to investigate the SARS-CoV-2 virus. CS-MoS₂/rGO nanohybrid-based immunosensor employs recombinant SARS-CoV-2 Spike RBD protein (rSP) that specifically identifies antibodies against the SARS-CoV-2 virus via differential pulse voltammetry (DPV). The antigen-antibody interaction diminishes the current responses of the immunosensor. The obtained results indicate that the fabricated immunosensor is extraordinarily capable of highly sensitive and specific detection of the corresponding SARS-CoV-2 antibodies with a LOD of 2.38 zg mL^-1 in phosphate buffer saline (PBS) samples over a broad linear range between 10 zg mL^-1-100 ng mL^-1. In addition, the proposed immunosensor can detect attomolar concentrations in spiked human serum samples. The performance of this immunosensor is assessed using actual serum samples from COVID-19-infected patients. The proposed immunosensor can accurately and substantially differentiate between (+) positive and (-) negative samples. As a result, the nanohybrid can provide insight into the conception of Point-of-Care Testing (POCT) platforms for cutting-edge infectious disease diagnostic methods.

Alternative platform for COVID-19 diagnosis based on AuNP-modified lab-on-paper

COVID-19 has caused global health problems, and so rapid diagnosis is crucial to slow spread of the disease. Herein, a novel lab-on-paper screening method for SARS-CoV-2 Omicron BA.2 variant was developed using a gold nanoparticle-based colorimetric biosensor along with sensitive detection of SARS-CoV-2 antigen using laser desorption ionization-mass spectrometry (LDI-MS). As a result of antigen-antibody interaction, in the presence of SARS-CoV-2 antigen the gold nanoparticles undergo aggregation and change color from red to light purple, allowing for rapid determination of SARS-CoV-2 antigen with the naked eye. Furthermore, the lab-on-paper method can be directly applied as a substrate for sensitive quantitation of SARS-CoV-2 antigen in saliva using LDI-MS without the use of a conventional organic matrix and sample preparation. LDI-MS offers early diagnosis with high sensitivity, rapidity without sample preparation and lower cost per test compared with reverse transcriptase-PCR, which is crucial for preventing mortality in patients with underlying conditions. This method showed linearity over 0.01-1 μg mL^-1 covering the cut-off value of 0.048 μg mL^-1 for COVID-19 detection in human saliva. Moreover, a colorimetric sensor for urea was also fabricated in-parallel, for prediction of COVID-19 severity in patients with chronic kidney disease. The color change upon increasing urea concentration directly reflected kidney damage, which is related to increasing risk of mortality among patients with COVID-19. Hence, this platform might be a potential device for non-invasive diagnosis of SARS-CoV-2 Omicron BA.2 variant, which is the variant of most concern because it is transmitted more rapidly than the original SARS-CoV-2 virus and the Delta variant.

Molecular test for COVID-19 diagnosis based on a colorimetric genomagnetic assay

The world is in a long pandemic period caused by the SARS-CoV-2 virus and massive diagnostic tests to assist efforts to control the spread of the disease and also to avoid new coronavirus variants are still needed. Herein, we propose a simple and accurate saliva-based colorimetric test for the diagnosis of COVID-19. Magnetic beads (MBs) modified with a sequence of single-strand DNA (ssDNA) complementary to the N gene of the SARS-CoV-2 RNA were developed and used for magnetic capture and separation from a complex saliva sample. A second biotinylated ssDNA sequence was applied, and the colorimetric detection was carried out by adding streptavidin-horseradish peroxidase conjugate, H₂O_2, and tetramethylbenzidine (TMB) as chromogenic substrate. The test does not require viral RNA isolation, transcription, or amplification steps and can be performed at room temperature. The molecular assay test can be run using 96-well microplates, allowing the diagnosis of a large number of samples in 90 min. A simple support for magnets was designed and constructed using a 3D printer that allows the magnetic separations directly in the 96-well microplate. The colorimetric test showed an excellent ability to discriminate between healthy individuals and patients infected with SARS-CoV-2, with 92% and 100% of clinical sensitivity and specificity, respectively. This performance was similar to that achieved using the gold standard RT-PCR technique. The proposed genomagnetic assay offers an opportunity to greatly increase population testing, contribute to controlling the spread of the virus, and improve health equity in testing for COVID-19.

Plasma-induced nanoparticle aggregation for stratifying COVID-19 patients according to disease severity

Stratifying patients according to disease severity has been a major hurdle during the COVID-19 pandemic. This usually requires evaluating the levels of several biomarkers, which may be cumbersome when rapid decisions are required. In this manuscript we show that a single nanoparticle aggregation test can be used to distinguish patients that require intensive care from those that have already been discharged from the intensive care unit (ICU). It consists of diluting a platelet-free plasma sample and then adding gold nanoparticles. The nanoparticles aggregate to a larger extent when the samples are obtained from a patient in the ICU. This changes the color of the colloidal suspension, which can be evaluated by measuring the pixel intensity of a photograph. Although the exact factor or combination of factors behind the different aggregation behavior is unknown, control experiments demonstrate that the presence of proteins in the samples is crucial for the test to work. Principal component analysis demonstrates that the test result is highly correlated to biomarkers of prognosis and inflammation that are commonly used to evaluate the severity of COVID-19 patients. The results shown here pave the way to develop nanoparticle aggregation assays that classify COVID-19 patients according to disease severity, which could be useful to de-escalate care safely and make a better use of hospital resources.