McCarthy, C; Avetisyan, R; Carey, BC; Chalk, C; Trapnell, BC. Prevalence and healthcare burden of pulmonary alveolar proteinosis. Orphanet Journal of Rare Diseases. 2018; 13(1).
Pulmonary alveolar proteinosis (PAP) is a disease manifest by decreased surfactant clearance leading to respiratory failure for which the pathogenetic mechanism is unknown. In this study, McCarthy and colleagues examine the lipids accumulating in alveolar macrophages and surfactant to define the pathogenesis of PAP and evaluate a novel pharmacotherapeutic approach. In PAP patients, alveolar macrophages demonstrated a significant increase in cholesterol. Oral statin therapy resulted in clinical, physiological, and radiological improvement in autoimmune PAP patients.
Guo, M; Du, Y; Gokey, JJ; Ray, S; Bell, SM; Adam, M; Sudha, P; Perl, AK; Deshmukh, H; Potter, SS; Whitsett, JA; Xu, Y. Single cell RNA analysis identifies cellular heterogeneity and adaptive responses of the lung at birth. Nature Communications. 2019; 10(1):37-37.
In this study, single cell analyses were performed to identify the heterogeneity of pulmonary cell types and dynamic changes in gene expression mediating adaptation to respiration. Distinct populations of epithelial, endothelial, mesenchymal, and immune cells, each containing distinct subpopulations during adaptation to air breathing after birth were revealed at a level of resolution far exceeding previous understanding.
Li, H; Parikh, NA; Wang, J; Merhar, S; Chen, M; Parikh, M; Holland, S; He, L. Objective and Automated Detection of Diffuse White Matter Abnormality in Preterm Infants Using Deep Convolutional Neural Networks. Frontiers in Neuroscience. 2019; 13.
Despite improvements in outcomes for preterm infants, deep white matter injury and subsequent diagnoses of cerebral palsy remain a major concern. Early diagnosis is crucial to optimize long-term outcomes. He and colleagues developed an automated, highly reliable detection model based on deep learning to detect deep white matter injury on T2-weighted MRI images in a cohort of very preterm infants. The model exhibited superior performance when compared to other popular models based on machine learning. Their work is an important step forward toward making accurate diagnosis of deep white matter injury in preterm infants.
Schmidt, AF; Kannan, PS; Bridges, JP; Filuta, A; Lipps, D; Kemp, M; Miller, LA; Kallapur, SG; Xu, Y; Whitsett, JA; Jobe, AH. Dosing and formulation of antenatal corticosteroids for fetal lung maturation and gene expression in rhesus macaques. Scientific Reports. 2019; 9(1):9039.
Antenatal corticosteroids (ANS) treatment is the standard of care for lung disease due to prematurity. The most widely used treatment is two intramuscular doses of a 1:1 mixture of betamethasone-phosphate (Beta-P) and betamethasone-acetate (Beta-Ac), however, dosing has never been optimized to provide the dose both maximize therapeutic benefit and minimizing potential harmful effects. Schmidt and colleagues used a primate model to test the efficacy of the slow release Beta-Ac alone for enhancing fetal lung maturation and to reduce fetal corticosteroid exposure and potential toxic effects. Beta-Ac alone increased lung compliance and surfactant concentration in the fetal lung equivalently to the current clinical regimen with a much lower overall ANS exposure. This result suggest that a low dose ANS treatment with Beta-Ac should be assessed for efficacy in human trials.
Kirkendall, ES; Ni, Y; Lingren, T; Leonard, M; Hall, ES; Melton, K. Data Challenges With Real-Time Safety Event Detection And Clinical Decision Support. Journal of Medical Internet Research. 2019; 21(5).
The electronic health record offers substantial potential to convert data into valuable clinical interventions such as error detection/prevention. However, access to real-time patient data does not appear to be sufficient. Kirkendall and colleagues defined 8 major challenge categories that require compromise effective use of real-time data extracted from the electronic health record. Their work offers a framework to address these barriers and realize the full potential of digitized patient information.