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The continuing mutability of the SARS-CoV-2 virus can result in failures of diagnostic assays. To address this, we describe a generalizable bioinformatics-to-biology pipeline developed for the calibration and quality assurance of inactivated SARS-CoV-2 variant panels provided to Radical Acceleration of Diagnostics programs (RADx)-radical program awardees. A heuristic genetic analysis based on variant-defining mutations demonstrated the lowest genetic variance in the Nucleocapsid protein (Np)-C-terminal domain (CTD) across all SARS-CoV-2 variants. We then employed the Shannon entropy method on (Np) sequences collected from the major variants, verifying the CTD with lower entropy (less prone to mutations) than other Np regions. Polyclonal and monoclonal antibodies were raised against this target CTD antigen and used to develop an Enzyme-linked immunoassay (ELISA) test for SARS-CoV-2. Blinded Viral Quality Assurance (VQA) panels comprised of UV-inactivated SARS-CoV-2 variants (XBB.1.5, BF.7, BA.1, B.1.617.2, and WA1) and distractor respiratory viruses (CoV 229E, CoV OC43, RSV A2, RSV B, IAV H1N1, and IBV) were assembled by the RADx-rad Diagnostics core and tested using the ELISA described here. The assay tested positive for all variants with high sensitivity (limit of detection: 1.72-8.78 ng/mL) and negative for the distractor virus panel. Epitope mapping for the monoclonal antibodies identified a 20 amino acid antigenic peptide on the Np-CTD that an in-silico program also predicted for the highest antigenicity. This work provides a template for a bioinformatics pipeline to select genetic regions with a low propensity for mutation (low Shannon entropy) to develop robust 'pan-variant' antigen-based assays for viruses prone to high mutational rates.

E-cigarette use has become widespread, and its effects on airway inflammation and disease are not fully delineated. E-cigarette vapor extract (EVE) profoundly affects neutrophil function. We hypothesized that EVE also alters eosinophil function and thus could impact allergic airways disease. We employed RNA-sequencing to measure the ex vivo effect of EVE components on human eosinophil transcription. Blood eosinophils from 9 non-vaping subjects without asthma were isolated by negative selection. Cells were incubated for 48 hours with EVE consisting of glycerin, propylene glycol and nicotine (EVE+), EVE without nicotine ("EVE-"), air-exposed media termed Extract Buffer (EB), or untreated media. Bulk RNA-sequencing was performed. Transcriptomic analysis revealed that the EB, EVE-, and EVE+ conditions showed highly variable gene expression with respect to No Treatment and each other. Differential gene expression analysis comparing a combination of EVE+, EVE-, and EB revealed 3,030 differentially expressed genes (DEG) with adjusted p value < 0.05 and log2 fold change >0.5 or <0.5. There were 645 DEG between EB and EVE-, 1,713 between EB and EVE+, and 404 between EVE- and EVE+. Gene set enrichment analysis demonstrated that DEG between both EVE+ and EVE- and the EB control were positively enriched for heme metabolism and apoptosis and negatively enriched TNFα signaling, IFNγ signaling, and inflammatory response. Thus, EVE significantly alters eosinophil metabolic and inflammatory pathways, mediated by propylene glycol and glycerin with both enhancing and unique effects of nicotine. This study motivates further research into the pathogenic effects of vaping on airway eosinophils and allergic airways disease., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for Leukocyte Biology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Diagnostic approaches that combine the high sensitivity and specificity of laboratory-based digital detection with the ease of use and affordability of point-of-care (POC) technologies could revolutionize disease diagnostics. This is especially true in infectious disease diagnostics, where rapid and accurate pathogen detection is critical to curbing the spread of disease. We have pioneered an innovative label-free digital detection platform that utilizes Interferometric Reflectance Imaging Sensor (IRIS) technology. IRIS leverages light interference from an optically transparent thin film, eliminating the need for complex optical resonances to enhance the signal by harnessing light interference and the power of signal averaging in shot-noise-limited operation to achieve virtually unlimited sensitivity. In our latest work, we have further improved our previous 'Single-Particle' IRIS (SP-IRIS) technology by allowing the construction of the optical signature of target nanoparticles (whole virus) from a single image. This new platform, 'Pixel-Diversity' IRIS (PD-IRIS), eliminated the need for z-scan acquisition, required in SP-IRIS, a time-consuming and expensive process, and made our technology more applicable to POC settings. Using PD-IRIS, we quantitatively detected the Monkeypox virus (MPXV), the etiological agent for Monkeypox (Mpox) infection. MPXV was captured by anti-A29 monoclonal antibody (mAb 69-126-3) on Protein G spots on the sensor chips and were detected at a limit-of-detection (LOD) - of 200 PFU/ml (~3.3 attomolar). PD-IRIS was superior to the laboratory-based ELISA (LOD - 1800 PFU/mL) used as a comparator. The specificity of PD-IRIS in MPXV detection was demonstrated using Herpes simplex virus, type 1 (HSV-1), and Cowpox virus (CPXV). This work establishes the effectiveness of PD-IRIS and opens possibilities for its advancement in clinical diagnostics of Mpox at POC. Moreover, PD-IRIS is a modular technology that can be adapted for the multiplex detection of pathogens for which high-affinity ligands are available that can bind their surface antigens to capture them on the sensor surface.