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Enzyme-linked immunosorbent assays (ELISAs) are widely employed for the detection of protein targets due to their ease of use, sensitivity, and potential for high-throughput analyses. However, the use of ELISAs to detect non-protein targets such as lipids and amphiphiles is complicated by the physical properties of these molecules, which affects their association with functional surfaces and recognition ligands. Here, we developed a unique lipoprotein capture ELISA in which the natural association between lipoproteins and amphiphilic molecules facilitates detection of the target biomarker in a physiologically relevant conformation. An assay to detect the glycolipid lipoarabinomannan (LAM), a cell membrane component and virulence factor associated with Mycobacterial infections, was developed as a proof of concept. [Display omitted] • Enzyme-linked immunosorbent assays (ELISAs) are rarely used for the detection of non-protein targets due to association with functional surfaces and recognition ligands. • We developed a lipoprotein capture ELISA method to detect lipid and amphiphilic molecules using their natural biochemical association with lipoproteins as capture ligands. • We show a proof of concept assay to detect the glycolipid lipoarabinomannan, a cell membrane component and virulence factor associated with Mycobacterium tuberculosis. [ABSTRACT FROM AUTHOR]

The many respiratory viruses that cause influenza-like illness (ILI) are reported and tracked as one entity, defined by the CDC as a group of symptoms that include a fever of 100 degrees Fahrenheit, a cough, and/or a sore throat. In the United States alone, ILI impacts 9–49 million people every year. While tracking ILI as a single clinical syndrome is informative in many respects, the underlying viruses differ in parameters and outbreak properties. Most existing models treat either a single respiratory virus or ILI as a whole. However, there is a need for models capable of comparing several individual viruses that cause respiratory illness, including ILI. To address this need, here we present a flexible model and simulations of epidemics for influenza, RSV, rhinovirus, seasonal coronavirus, adenovirus, and SARS/MERS, parameterized by a systematic literature review and accompanied by a global sensitivity analysis. We find that for these biological causes of ILI, their parameter values, timing, prevalence, and proportional contributions differ substantially. These results demonstrate that distinguishing the viruses that cause ILI will be an important aspect of future work on diagnostics, mitigation, modeling, and preparation for future pandemics. [ABSTRACT FROM AUTHOR]