Wastewater Surveillance for Identifying SARS-CoV-2 Infections in Long-Term Care Facilities, Kentucky, USA, 2021-2022.

Persons living in long-term care facilities (LTCFs) were disproportionately affected by COVID-19. We used wastewater surveillance to detect SARS-CoV-2 infection in this setting by collecting and testing 24-hour composite wastewater samples 2-4 times weekly at 6 LTCFs in Kentucky, USA, during March 2021-February 2022. The LTCFs routinely tested staff and symptomatic and exposed residents for SARS-CoV-2 using rapid antigen tests. Of 780 wastewater samples analyzed, 22% (n = 173) had detectable SARS-CoV-2 RNA. The LTCFs reported 161 positive (of 16,905) SARS-CoV-2 clinical tests. The wastewater SARS-CoV-2 signal showed variable correlation with clinical test data; we observed the strongest correlations in the LTCFs with the most positive clinical tests (n = 45 and n = 58). Wastewater surveillance was 48% sensitive and 80% specific in identifying SARS-CoV-2 infections found on clinical testing, which was limited by frequency, coverage, and rapid antigen test performance.

Simple SARS-CoV-2 concentration methods for wastewater surveillance in low resource settings.

Wastewater-based epidemiology (WBE) measures pathogens in wastewater to monitor infectious disease prevalence in communities. Due to the high dilution of pathogens in sewage, a concentration method is often required to achieve reliable biomarker signals. However, most of the current concentration methods rely on expensive equipment and labor-intensive processes, which limits the application of WBE in low-resource settings. Here, we compared the performance of four inexpensive and simple concentration methods to detect SARS-CoV-2 in wastewater samples: Solid Fraction, Porcine Gastric Mucin-conjugated Magnetic Beads, Calcium Flocculation-Citrate Dissolution (CFCD), and Nanotrap® Magnetic Beads (NMBs). The NMBs and CFCD methods yielded the highest concentration performance for SARS-CoV-2 (∼16-fold concentration and âˆ¼ 41 % recovery) and require <45 min processing time. CFCD has a relatively low consumable cost (<$2 per four sample replicates). All methods can be performed with basic laboratory equipment and minimal electricity usage which enables further application of WBE in remote areas and low resource settings.

Stabilization of SARS-CoV-2 RNA in wastewater via rapid RNA extraction.

Wastewater-based Epidemiology (WBE) has contributed to surveillance of SARS-CoV-2 in communities across the world. Both symptomatic and asymptomatic patients with COVID-19 can shed the virus through the gastrointestinal tract, enabling the quantification of the virus in stool and ultimately in wastewater (WW). Unfortunately, instability of SARS-CoV-2 RNA in wastewater limits the utility of WBE programs, particularly in remote/rural regions where reliable cold storage and/or rapid shipping may be unavailable. This study examined whether rapid SARS-CoV-2 RNA extraction on the day of sample collection could minimize degradation. Importantly, the extraction technology used in these experiments, termed exclusion-based sample preparation (ESP), is lightweight, portable, and electricity-free, making it suitable for implementation in remote settings. We demonstrated that immediate RNA extraction followed by ambient storage significantly increased the RNA half-life compared to raw wastewater samples stored at both 4 °C or ambient temperature. Given that RNA degradation negatively impacts both the sensitivity and precision of WBE measurements, efforts must be made to mitigate degradation in order to maximize the potential impact of WBE on public health.

Reproducibility of Systolic Strain in Mice Using Cardiac Magnetic Resonance Feature Tracking of Black-Blood Cine Images.

PURPOSE: Mouse models are widely utilized to enhance our understanding of cardiac disease. The goal of this study is to investigate the reproducibility of strain parameters that were measured in mice using cardiac magnetic resonance (CMR) feature-tracking (CMR42, Canada). METHODS: We retrospectively analyzed black-blood CMR datasets from thirteen C57BL/6 B6.SJL-CD45.1 mice (N = 10 female, N = 3 male) that were imaged previously. The circumferential, longitudinal, and radial (Ecc, Ell, and Err, respectively) parameters of strain were measured in the mid-ventricular region of the left ventricle. Intraobserver and interobserver reproducibility were assessed for both the end-systolic (ES) and peak strain. RESULTS: The ES strain had larger intraclass correlation coefficient (ICC) values when compared to peak strain, for both the intraobserver and interobserver reproducibility studies. Specifically, the intraobserver study showed excellent reproducibility for all three ES strain parameters, namely, Ecc (ICC 0.95, 95% CI 0.83-0.98), Ell (ICC 0.90, 95% CI 0.59-0.97), and Err (ICC 0.92, 95% CI 0.73-0.97). This was also the case for the interobserver study, namely, Ecc (ICC 0.92, 95% CI 0.60-0.98), Ell (ICC 0.76, 95% CI 0.33-0.93), and Err (ICC 0.93, 95% CI 0.68-0.98). Additionally, the coefficient of variation values were all < 10%. CONCLUSION: The results of this preliminary study showed excellent reproducibility for all ES strain parameters, with good to excellent reproducibility for the peak strain parameters. Moreover, all ES strain parameters had larger ICC values than the peak strain. In general, these results imply that feature-tracking with CMR42 software and black-blood cine images can be reliably used to assess strain patterns in mice.

Development and Validation of a Simplified Method for Analysis of SARS-CoV-2 RNA in University Dormitories.

Over the course of the COVID-19 pandemic, wastewater surveillance has become a useful tool for describing SARS-CoV-2 prevalence in populations of varying size, from individual facilities (e.g., university residence halls, nursing homes, prisons) to entire municipalities. Wastewater analysis for SARS-CoV-2 RNA requires specialized equipment, expensive consumables, and expert staff, limiting its feasibility and scalability. Further, the extremely labile nature of viral RNA complicates sample transportation, especially in regions with limited access to reliable cold chains. Here, we present a new method for wastewater analysis, termed exclusion-based sample preparation (ESP), that substantially simplifies workflow (at least 70% decrease in time; 40% decrease in consumable usage compared with traditional techniques) by targeting the labor-intensive processing steps of RNA purification and concentration. To optimize and validate this method, we analyzed wastewater samples from residence halls at the University of Kentucky, of which 34% (44/129) contained detectible SARS-CoV-2 RNA. Although concurrent clinical testing was not comprehensive, student infections were identified in the 7 days following a positive wastewater detection in 68% of samples. This pilot study among university residence halls validated the performance and utility of the ESP method, laying the foundation for future studies in regions of the world where wastewater testing is not currently feasible.

Multiscale simulations of left ventricular growth and remodeling.

Cardiomyocytes can adapt their size, shape, and orientation in response to altered biomechanical or biochemical stimuli. The process by which the heart undergoes structural changes-affecting both geometry and material properties-in response to altered ventricular loading, altered hormonal levels, or mutant sarcomeric proteins is broadly known as cardiac growth and remodeling (G&R). Although it is likely that cardiac G&R initially occurs as an adaptive response of the heart to the underlying stimuli, prolonged pathological changes can lead to increased risk of atrial fibrillation, heart failure, and sudden death. During the past few decades, computational models have been extensively used to investigate the mechanisms of cardiac G&R, as a complement to experimental measurements. These models have provided an opportunity to quantitatively study the relationships between the underlying stimuli (primarily mechanical) and the adverse outcomes of cardiac G&R, i.e., alterations in ventricular size and function. State-of-the-art computational models have shown promise in predicting the progression of cardiac G&R. However, there are still limitations that need to be addressed in future works to advance the field. In this review, we first outline the current state of computational models of cardiac growth and myofiber remodeling. Then, we discuss the potential limitations of current models of cardiac G&R that need to be addressed before they can be utilized in clinical care. Finally, we briefly discuss the next feasible steps and future directions that could advance the field of cardiac G&R.