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The pathology in Duchenne muscular dystrophy (DMD) is characterized by degenerating muscle fibers, inflammation, fibro-fatty infiltrate, and edema, and these pathological processes replace normal healthy muscle tissue. Themdxmouse model is one of the most commonly used preclinical models to study DMD. Mounting evidence has emerged illustrating that muscle disease progression varies considerably inmdxmice, with inter-animal differences as well as intra-muscular differences in pathology in individualmdxmice. This variation is important to consider when conducting assessments of drug efficacy and in longitudinal studies. Magnetic resonance imaging (MRI) is a non-invasive method that can be used qualitatively or quantitatively to measure muscle disease progression in the clinic and in preclinical models. Although MR imaging is highly sensitive, image acquisition and analysis can be time intensive. The purpose of this study was to develop a semi-automated muscle segmentation and quantitation pipeline that can quickly and accurately estimate muscle disease severity in mice. Herein, we show that the newly developed segmentation tool accurately divides muscle. We show that measures of skew and interdecile range based on segmentation sufficiently estimate muscle disease severity in healthy wildtype and diseasedmdxmice. Moreover, the semi-automated pipeline reduced analysis time by nearly 10-fold. Use of this rapid, non-invasive, semi-automated MR imaging and analysis pipeline has the potential to transform preclinical studies, allowing for pre-screening of dystrophic mice prior to study enrollment to ensure more uniform muscle disease pathology across treatment groups, improving study outcomes.

The pathology in Duchenne muscular dystrophy (DMD) is characterized by degenerating muscle fibers, inflammation, fibro-fatty infiltrate, and edema, and these pathological processes replace normal healthy muscle tissue. The mdx mouse model is one of the most commonly used preclinical models to study DMD. Mounting evidence has emerged illustrating that muscle disease progression varies considerably in mdx mice, with inter-animal differences as well as intra-muscular differences in pathology in individual mdx mice. This variation is important to consider when conducting assessments of drug efficacy and in longitudinal studies. Magnetic resonance imaging (MRI) is a non-invasive method that can be used qualitatively or quantitatively to measure muscle disease progression in the clinic and in preclinical models. Although MR imaging is highly sensitive, image acquisition and analysis can be time intensive. The purpose of this study was to develop a semi-automated muscle segmentation and quantitation pipeline that can quickly and accurately estimate muscle disease severity in mice. Herein, we show that the newly developed segmentation tool accurately divides muscle. We show that measures of skew and interdecile range based on segmentation sufficiently estimate muscle disease severity in healthy wildtype and diseased mdx mice. Moreover, the semi-automated pipeline reduced analysis time by nearly 10-fold. Use of this rapid, non-invasive, semi-automated MR imaging and analysis pipeline has the potential to transform preclinical studies, allowing for pre-screening of dystrophic mice prior to study enrollment to ensure more uniform muscle disease pathology across treatment groups, improving study outcomes., Competing Interests: CONFLICT OF INTEREST EMM has consulted for Amgen, AstraZeneca, Cytokinetics, Pfizer, Tenaya Therapeutics and is the founder of Ikaika Therapeutics. ARD is the Chief Scientific Officer at Ikaika Therapeutics. These activities are unrelated to the content of this manuscript. The other authors have no conflicts of interest to declare.

Background: Estimates of seroprevalence to SARS-CoV-2 vary widely and may influence vaccination response. We ascertained IgG levels across a single US metropolitan site, Chicago, from June 2020 through December 2020., Methods: Participants (n=7935) were recruited through electronic advertising and received materials for a self-sampled dried blood spot assay through the mail or a minimal contact in person method. IgG to the receptor binding domain of SARS-CoV-2 was measured using an established highly sensitive and highly specific assay., Results: Overall seroprevalence was 17.9%, with no significant difference between method of contact. Only 2.5% of participants reported having had a diagnosis of COVID-19 based on virus detection, consistent with a 7-fold greater exposure to SARS-CoV-2 measured by serology than detected by viral testing. The range of IgG level observed in seropositive participants from this community survey overlapped with the range of IgG levels associated with COVID-19 cases having a documented positive PCR positive test. From a subset of those who participated in repeat testing, half of seropositive individuals retained detectable antibodies for 3-4 months., Conclusions: Quantitative IgG measurements with a highly specific and sensitive assay indicate more widespread exposure to SARS-CoV-2 than observed by viral testing. The range of IgG concentration produced from these asymptomatic exposures is similar to IgG levels occurring after documented non-hospitalized COVID-19, which is considerably lower than that produced from hospitalized COVID-19 cases. The differing ranges of IgG response, coupled with the rate of decay of antibodies, may influence response to subsequent viral exposure and vaccine., Competing Interests: Conflicts of Interest: Thomas McDade has a financial interest in EnMed Microanalytics, a company that specializes in laboratory testing of dried blood spot samples. All other authors declare no conflicts of interest.

Magnitude of SARS-CoV-2 virus exposure may contribute to symptom severity. In a sample of seropositive adults (n=1101), we found that individuals who lived with a known COVID-19 case exhibited greater symptom severity and IgG concentrations compared to individuals who were seropositive but did not live with a known case (P<0.0001).