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To assess the impact of COVID‐19 restrictions on cystic fibrosis (CF) pulmonary exacerbations (PEx) we performed a retrospective review of PEx events at our CF Center and compared the rate of PEx in 2019 versus 2020. Restrictions on social interaction due to the COVID‐19 pandemic were associated with a lower number of PEx events at our pediatric CF Center, suggesting that these restrictions also reduced exposure to other respiratory viral infection in children with CF.

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Background.Procalcitonin (PCT) is a prohormone that rises in bacterial pneumonia and has promise in reducing antibiotic use. Despite these attributes, there are inconclusive data on its use for clinical prognostication. We hypothesize that serial PCT measurements can predict mortality, intensive care unit (ICU) admission, and bacteremia.Methods.A prospective cohort study of inpatients diagnosed with pneumonia was performed at a large tertiary care center in Boston, Massachusetts. Procalcitonin was measured on days 1 through 4. The primary endpoint was a composite adverse outcome defined as all-cause mortality, ICU admission, and bacteremia. Regression models were calculated with area under the receiver operating characteristic curve (AUC) as a measure of discrimination.Results.Of 505 patients, 317 patients had a final diagnosis of community-acquired pneumonia (CAP) or healthcare-associated pneumonia (HCAP). Procalcitonin was significantly higher for CAP and HCAP patients meeting the composite primary endpoint, bacteremia, and ICU admission, but not mortality. Incorporation of serial PCT levels into a statistical model including the Pneumonia Severity Index (PSI) improved the prognostic performance of the PSI with respect to the primary composite endpoint (AUC from 0.61 to 0.66), bacteremia (AUC from 0.67 to 0.85), and need for ICU-level care (AUC from 0.58 to 0.64). For patients in the highest risk class PSI >130, PCT was capable of further risk stratification for prediction of adverse outcomes.Conclusion.Serial PCT measurement in patients with pneumonia shows promise for predicting adverse clinical outcomes, including in those at highest mortality risk.

Large-scale population testing is a key tool to mitigate the spread of respiratory pathogens, such as the current COVID-19 pandemic, where swabs are used to collect samples in the upper airways (e.g., nasopharyngeal and midturbinate nasal cavities) for diagnostics. However, the high volume of supplies required to achieve large-scale population testing has posed unprecedented challenges for swab manufacturing and distribution, resulting in a global shortage that has heavily impacted testing capacity worldwide and prompted the development of new swabs suitable for large-scale production. Newly designed swabs require rigorous preclinical and clinical validation studies that are costly and time-consuming (i.e., months to years long); reducing the risks associated with swab validation is therefore paramount for their rapid deployment. To address these shortages, we developed a 3D-printed tissue model that mimics the nasopharyngeal and midturbinate nasal cavities, and we validated its use as a new tool to rapidly test swab performance. In addition to the nasal architecture, the tissue model mimics the soft nasal tissue with a silk-based sponge lining, and the physiological nasal fluid with asymptomatic and symptomatic viscosities of synthetic mucus. We performed several assays comparing standard flocked and injection-molded swabs. We quantified the swab pickup and release and determined the effect of viral load and mucus viscosity on swab efficacy by spiking the synthetic mucus with heat-inactivated SARS-CoV-2 virus. By molecular assay, we found that injected molded swabs performed similarly or superiorly in comparison to standard flocked swabs, and we underscored a viscosity-dependent difference in cycle threshold values between the asymptomatic and symptomatic mucuses for both swabs. To conclude, we developed an in vitro nasal tissue model that corroborated previous swab performance data from clinical studies; this model will provide to researchers a clinically relevant, reproducible, safe, and cost-effective validation tool for the rapid development of newly designed swabs.