American journal of respiratory and critical care medicine
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Am. J. Respir. Crit. Care Med. · Dec 2013
Challenges in Identifying Asthma Subgroups Using Unsupervised Statistical Learning Techniques.
Unsupervised statistical learning techniques, such as exploratory factor analysis (EFA) and hierarchical clustering (HC), have been used to identify asthma phenotypes, with partly consistent results. Some of the inconsistency is caused by the variable selection and demographic and clinical differences among study populations. ⋯ The use of different unsupervised statistical learning methods and different variable sets and encodings can lead to multiple and inconsistent subgroupings of asthma, not necessarily correlated with severity. The search for asthma phenotypes needs more careful selection of markers, consistent across different study populations, and more cautious interpretation of results from unsupervised learning.
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Am. J. Respir. Crit. Care Med. · Dec 2013
Cigarette Smoke Induces Systemic Defects in Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) Function.
Several extrapulmonary disorders have been linked to cigarette smoking. Smoking is reported to cause cystic fibrosis transmembrane conductance regulator (CFTR) dysfunction in the airway, and is also associated with pancreatitis, male infertility, and cachexia, features characteristic of cystic fibrosis and suggestive of an etiological role for CFTR. ⋯ Smoking causes systemic CFTR dysfunction. Acrolein present in cigarette smoke mediates CFTR defects in extrapulmonary tissues in smokers.