Respiratory distress syndrome (RDS), which is induced by insufficient production of surfactant, is the leading cause of mortality in preterm babies. Although several transcription factors are known to be involved in surfactant protein expression, the molecular mechanisms and signaling pathways upstream of these transcription factors have remained elusive. Here, using mammalian Hippo kinases (Mst1/2, mammalian sterile 20-like kinase 1/2) conditional knockout mice, we demonstrate that Mst1/2 kinases are critical for orchestration of transcription factors involved in surfactant protein homeostasis and prevention of RDS. Mice lacking Mst1/2 in the respiratory epithelium exhibited perinatal mortality with respiratory failure and their lungs contained fewer type I pneumocytes and more immature type II pneumocytes lacking microvilli, lamellar bodies, and surfactant protein expression, pointing to peripheral lung immaturity and RDS. In contrast to previous findings of YAP (Yes-associated protein)-mediated canonical Hippo signaling in the liver and intestine, loss of Mst1/2 kinases induced the defects in pneumocyte differentiation independently of YAP hyperactivity. We instead found that Mst1/2 kinases stabilized and phosphorylated the transcription factor Foxa2 (forkhead box A2), which regulates pneumocyte maturation and surfactant protein expression. Taken together, our results suggest that the mammalian Hippo kinases play crucial roles in surfactant homeostasis and coordination of peripheral lung differentiation through regulation of Foxa2 rather than of YAP.non-canonical Hippo pathway | lung development P eripheral lung immaturity and deficiency of pulmonary surfactant cause many intractable pulmonary diseases. Deficit of surfactant because of preterm birth or genetic disorders of surfactant homeostasis induce respiratory distress syndrome (RDS) in the newborn period (1). Dysregulation of these genes also underlies the pathogenesis of many chronic lung diseases that have been considered idiopathic, such as interstitial lung disease, pulmonary alveolar proteinosis, and others (2). Elucidating the mechanism underlying regulation of surfactant would be very helpful for understanding the molecular basis of refractory lung diseases of both infants and adults.Alveoli are composed of type I and type II pneumocytes. The type II pneumocyte is the progenitor cell of the type I pneumocyte and plays a central role in physiological pulmonary functions, especially surfactant production (3). Type I pneumocytes contact alveolar capillaries and participate in gas exchange. Lung morphogenesis in mice is a dynamic process that can be divided into embryonic [embryonic day (E) 9-11.5], pseudoglandular (E11.5-16.5), canalicular (E16.5-17.5), saccular [E17.5 to postnatal (PN) day 5], and alveolar (PN5-28) stages. During canalicular and saccular stages in late gestation, peripheral lungs containing type I and II pneumocytes fully differentiate in preparation for postnatal respiration (4).Many transcription factors, including Foxa2 (forkhead...
Auscultation has been essential part of the physical examination; this is non-invasive, real-time, and very informative. Detection of abnormal respiratory sounds with a stethoscope is important in diagnosing respiratory diseases and providing first aid. However, accurate interpretation of respiratory sounds requires clinician’s considerable expertise, so trainees such as interns and residents sometimes misidentify respiratory sounds. To overcome such limitations, we tried to develop an automated classification of breath sounds. We utilized deep learning convolutional neural network (CNN) to categorize 1918 respiratory sounds (normal, crackles, wheezes, rhonchi) recorded in the clinical setting. We developed the predictive model for respiratory sound classification combining pretrained image feature extractor of series, respiratory sound, and CNN classifier. It detected abnormal sounds with an accuracy of 86.5% and the area under the ROC curve (AUC) of 0.93. It further classified abnormal lung sounds into crackles, wheezes, or rhonchi with an overall accuracy of 85.7% and a mean AUC of 0.92. On the other hand, as a result of respiratory sound classification by different groups showed varying degree in terms of accuracy; the overall accuracies were 60.3% for medical students, 53.4% for interns, 68.8% for residents, and 80.1% for fellows. Our deep learning-based classification would be able to complement the inaccuracies of clinicians' auscultation, and it may aid in the rapid diagnosis and appropriate treatment of respiratory diseases.
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