PATHPOD - A loop-mediated isothermal amplification (LAMP)-based point-of-care system for rapid clinical detection of SARS-CoV-2 in hospitals in Denmark.

Sensitive and rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a vital goal in the ongoing COVID-19 pandemic. We present in this comprehensive work, for the first time, detailed fabrication and clinical validation of a point of care (PoC) device for rapid, onsite detection of SARS-CoV-2 using a real-time reverse-transcription loop-mediated isothermal amplification (RT-LAMP) reaction on a polymer cartridge. The PoC system, namely PATHPOD, consisting of a standalone device (weight less than 1.2 kg) and a cartridge, can perform the detection of 10 different samples and two controls in less than 50 min, which is much more rapid than the golden standard real-time reverse-transcription Polymerase Chain Reaction (RT-PCR), typically taking 16-48 h. The novel total internal reflection (TIR) scheme and the reactions inside the cartridge in the PoC device allow monitoring of the diagnostic results in real-time and onsite. The analytical sensitivity and specificity of the PoC test are comparable with the current RT-PCR, with a limit of detection (LOD) down to 30-50 viral genome copies. The robustness of the PATHPOD PoC system has been confirmed by analyzing 398 clinical samples initially examined in two hospitals in Denmark. The clinical sensitivity and specificity of these tests are discussed.

Emerging Methods in Biosensing of Immunoglobin G-A Review.

Human antibodies are produced due to the activation of immune system components upon exposure to an external agent or antigen. Human antibody G, or immunoglobin G (IgG), accounts for 75% of total serum antibody content. IgG controls several infections by eradicating disease-causing pathogens from the body through complementary interactions with toxins. Additionally, IgG is an important diagnostic tool for certain pathological conditions, such as autoimmune hepatitis, hepatitis B virus (HBV), chickenpox and MMR (measles, mumps, and rubella), and coronavirus-induced disease 19 (COVID-19). As an important biomarker, IgG has sparked interest in conducting research to produce robust, sensitive, selective, and economical biosensors for its detection. To date, researchers have used different strategies and explored various materials from macro- to nanoscale to be used in IgG biosensing. In this review, emerging biosensors for IgG detection have been reviewed along with their detection limits, especially electrochemical biosensors that, when coupled with nanomaterials, can help to achieve the characteristics of a reliable IgG biosensor. Furthermore, this review can assist scientists in developing strategies for future research not only for IgG biosensors but also for the development of other biosensing systems for diverse targets.

Machine Learning-Based Fragment Selection Improves the Performance of Qualitative PRM Assays.

Targeted mass spectrometry-based platforms have become a valuable tool for the sensitive and specific detection of protein biomarkers in clinical and research settings. Traditionally, developing a targeted assay for peptide quantification has involved manually preselecting several fragment ions and establishing a limit of detection (LOD) and a lower limit of quantitation (LLOQ) for confident detection of the target. Established thresholds such as LOD and LLOQ, however, inherently sacrifice sensitivity to afford specificity. Here, we demonstrate that machine learning can be applied to qualitative PRM assays to discriminate positive from negative samples more effectively than a traditional approach utilizing conventional methods. To demonstrate the utility of this method, we trained an ensemble machine learning model using 282 SARS-CoV-2 positive and 994 SARS-CoV-2 negative nasopharyngeal swabs (NP swab) analyzed using a targeted PRM method. This model was then validated using an independent set of 200 positive and 150 negative samples and achieved a sensitivity of 92% relative to results obtained by RT-PCR, which was superior to a traditional approach that resulted in 86.5% sensitivity when analyzing the same data. These results demonstrate that machine learning can be applied to qualitative PRM assays and results in superior performance relative to traditional methods.

Design of Gold Nanoparticle Vertical Flow Assays for Point-of-Care Testing.

Vertical flow assays (VFAs) or flow-through assays have emerged as an alternate type of paper-based assay due to their faster detection time, larger sample volume capacity, and significantly higher multiplexing capabilities. They have been successfully employed to detect several different targets (polysaccharides, protein, and nucleic acids), although in a limited number of samples (serum, whole blood, plasma) compared to the more commonly known lateral flow assays (LFAs). The operation of a VFA relies mainly on gravity, coupled with capillary action or external force to help the sample flow through layers of stacked pads. With recent developments in this field, multiple layers of pads and signal readers have been optimized for more user-friendly operation, and VFAs have achieved a lower limit of detection for various analytes than the gold-standard methods. Thus, compared to the more widely used LFA, the VFA demonstrates certain advantages and is becoming an increasingly popular platform for obtaining qualitative and quantitative results in low-resource settings. Considering the wide application of gold nanoparticles (GNPs) in VFAs, we will mostly discuss (1) the design of GNP-based VFA along with its associated advantages/disadvantages, (2) fabrication and optimization of GNP-based VFAs for applications, and (3) the future outlook of flow-based assays for point-of-care testing (POCT) diagnostics.