A Paper-Based Multiplexed Serological Test to Monitor Immunity against SARS-COV-2 Using Machine Learning.

The rapid spread of SARS-CoV-2 caused the COVID-19 pandemic and accelerated vaccine development to prevent the spread of the virus and control the disease. Given the sustained high infectivity and evolution of SARS-CoV-2, there is an ongoing interest in developing COVID-19 serology tests to monitor population-level immunity. To address this critical need, we designed a paper-based multiplexed vertical flow assay (xVFA) using five structural proteins of SARS-CoV-2, detecting IgG and IgM antibodies to monitor changes in COVID-19 immunity levels. Our platform not only tracked longitudinal immunity levels but also categorized COVID-19 immunity into three groups: protected, unprotected, and infected, based on the levels of IgG and IgM antibodies. We operated two xVFAs in parallel to detect IgG and IgM antibodies using a total of 40 µL of human serum sample in <20 min per test. After the assay, images of the paper-based sensor panel were captured using a mobile phone-based custom-designed optical reader and then processed by a neural network-based serodiagnostic algorithm. The serodiagnostic algorithm was trained with 120 measurements/tests and 30 serum samples from 7 randomly selected individuals and was blindly tested with 31 serum samples from 8 different individuals, collected before vaccination as well as after vaccination or infection, achieving an accuracy of 89.5%. The competitive performance of the xVFA, along with its portability, cost-effectiveness, and rapid operation, makes it a promising computational point-of-care (POC) serology test for monitoring COVID-19 immunity, aiding in timely decisions on the administration of booster vaccines and general public health policies to protect vulnerable populations.

Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning.

A plaque assay-the gold-standard method for measuring the concentration of replication-competent lytic virions-requires staining and usually more than 48 h of runtime. Here we show that lens-free holographic imaging and deep learning can be combined to expedite and automate the assay. The compact imaging device captures phase information label-free at a rate of approximately 0.32 gigapixels per hour per well, covers an area of about 30 × 30 mm2 and a 10-fold larger dynamic range of virus concentration than standard assays, and quantifies the infected area and the number of plaque-forming units. For the vesicular stomatitis virus, the automated plaque assay detected the first cell-lysing events caused by viral replication as early as 5 h after incubation, and in less than 20 h it detected plaque-forming units at rates higher than 90% at 100% specificity. Furthermore, it reduced the incubation time of the herpes simplex virus type 1 by about 48 h and that of the encephalomyocarditis virus by about 20 h. The stain-free assay should be amenable for use in virology research, vaccine development and clinical diagnosis.

Nanoparticle-assisted pyrrolidonyl arylamidase assay for a culture-free Group A Streptococcus pyogenes detection with image analysis.

Existing techniques for the detection of Group A Streptococcus pyogenes (GAS) have drawbacks in rapidness, accuracy or in high-cost. Considering the clinical importance of GAS, we have developed a culture-free detection method based on pyrrolidonyl arylamidase (PYR) activity with the aid of magnetic gold nanoparticles (AuNPs). GAS is the reason for pharyngitis and sampling starts from the throat with cotton swabs. After swab sampling, the target was collected with antibody modified magnetic AuNPs and transferred into 500 µL of PYR-broth without any antigen extraction or pure colony isolation. Then, the assay was finished by adding 25 µL of 4-(dimethylamino)-cinnamaldehyde (DMACA) reagent after 4-h incubation. A red color formation was evaluated as the presence of GAS comparing to blank, however, image analysis was employed for the interpretation of color changes clearly. For this purpose, a formula related to image data was proposed and analytical validation parameters were defined. Thus, the correlation was found to be linear with the R2 of 0.9685 between the log of bacteria concentration and the image data with the limit of detection of 3.3 × 102 CFU/mL of GAS. In addition, the assay worked efficiently in the abundance interference of Enterococcus faecalis. The results represent a new feature to nanoparticles eliminating the selective growth media for a bacteria and this study provided a detection with intact cells of bacteria without any antigen or DNA/RNA extraction. The proposed work has been the most similar to the gold standard but a faster method in this field.

SERS-based rapid assay for sensitive detection of Group A Streptococcus by evaluation of the swab sampling technique.

Beta-hemolytic, Group A Streptococcus pyogenes (GAS) is a life-threating pathogen and the reason for prominent disease, pharyngitis. The conventional analysis of GAS, gold standard, takes 48 hours and the related rapid tests lack in accuracy and sensitivity. In this study, firstly, the efficiency of swab sampling, which is a must in the GAS detection, was discussed with the proposed surface-enhanced Raman spectroscopy (SERS)-based batch assay and each step was controlled by the plate-counting method. Secondly, SERS-based lateral flow immunoassay (LFIA) test strips were constructed and the variation in the SERS intensity of 5,5'-dithiobis (2-nitrobenzoic acid) (DTNB) was observed. Thus, a linear correlation was found with a R2 value of 0.9926 and the LOD was calculated to be 0.2 CFU mL-1 of GAS which could be counted as one cell. The combination of the gold standard with the LFIA-SERS technique enabled the fast and accurate pathogen detection. In addition, GAS was quantified with paper-based test strips up to 100 CFU ml-1 level of bacteria for the first time without any interference. Besides, this study was featured with the discussion of the whole cell and pretreated cell detection of pathogens with LFIAs. Therefore, this work enlightens the points that have never been discussed on pathogen detection with paper-based platforms.

Quantification and spatial distribution of salicylic acid in film tablets using FT-Raman mapping with multivariate curve resolution.

In this study, we proposed a rapid and sensitive method for quantification and spatial distribution of salicylic acid in film tablets using FT-Raman spectroscopy with multivariate curve resolution (MCR). For this purpose, the constituents of film tablets were identified by using FT-Raman spectroscopy, and then eight different concentrations of salicylic acid tablets were visualized by Raman mapping. MCR was applied to mapping data to expose the active pharmaceutical ingredients in the presence of other excipients by monitoring distribution maps and combination of FT-Raman mapping with MCR enabled the determination of lower salicylic acid concentrations. In addition, the distribution of major excipient, lactose, was examined in the tablet form. A calibration curve was obtained by plotting the intensity of the Raman signal at 1635 cm-1 versus the concentration of salicylic acid and the correlation was found to be linear within the range of 0.5%-3.9% with a correlation coefficient of 0.99. The limit of detection for the technique was determined 0.35%. The ability of the technique to quantify salicylic acid in tablet test samples was also investigated.

Surface-enhanced Raman probe for rapid nanoextraction and detection of erythropoietin in urine.

We present a surface-enhanced Raman probe (SERS) platform for the determination of a prohibited substance, recombinant erythropoietin (rEPO), in urine matrix, using nanoparticles as substrate. Rod-shaped gold nanoparticles (GNR) were modified with a Raman label and an antibody as SERS probe. We developed two SERS-based immunoassays for detection and quantification of rEPO in urine. In the first assay, rEPO was determined by a sandwich assay with gold surfaces and GNR. In the second assay, rEPO was extracted by using core shell-structured magnetic iron oxide gold nanoparticles, and again sandwich assay was performed by using GNR. We also demonstrated the ability of the proposed method to discriminate rEPO and urinary erythropoietin (uEPO). A good linear correlation was obtained between logarithms of rEPO concentrations in urine and Raman intensities within the range of 10-1-103 pg mL-1 rEPO concentrations. Detection limits which are smaller than 0.1 pg mL-1 levels were achieved owing to the high extractive performance of the nanoextraction techniques. Graphical Abstract Schematic represantation of surface-enhanced Raman probe for rapid nanoextraction and detection of erythropoietin.

A novel glucose biosensor platform based on Ag@AuNPs modified graphene oxide nanocomposite and SERS application.

This study represents a novel template demonstration of a glucose biosensor based on mercaptophenyl boronic acid (MBA) terminated Ag@AuNPs/graphene oxide (Ag@AuNPs-GO) nanomaterials. The nanocomposites were characterized by transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD) method. The TEM image shows that Ag@AuNPs in the nanocomposite is in the range of diameters of 10-20 nm. The nanocomposite was used for the determination of glucose through the complexation between boronic acid and diol groups of glucose. Thus, a novel glucose biosensor was further fabricated by immobilizing glucose oxidase (GOD) into MBA terminated Ag@AuNPs-GO nanocomposite film (MBA-Ag@AuNPs-GO). The linearity range of glucose was obtained as 2-6mM with detection limit of 0.33 mM. The developed biosensor was also applied successfully for the determination of glucose in blood samples. The concentration value of glucose in blood samples was calculated to be 1.97±0.002 mM from measurements repeated for six times.