Dynamic perfluorinated gas MRI reveals abnormal ventilation despite normal FEV1 in cystic fibrosis

We hypothesized that dynamic perfluorinated gas MRI would sensitively detect mild cystic fibrosis (CF) lung disease. This cross-sectional study enrolled 20 healthy volunteers and 24 stable subjects with CF, including a subgroup of subjects with normal forced expiratory volume in the first second (FEV1; >80% predicted, n = 9). Dynamic fluorine-19–enhanced MRI (19F MRI) were acquired during sequential breath holds while breathing perfluoropropane (PFP) and during gas wash-out. Outcomes included the fraction of lung without significant ventilation (ventilation defect percent, VDP) and time constants that described PFP wash-in and wash-out kinetics. VDP values (mean ± SD) of healthy controls (3.87% ± 2.7%) were statistically different from moderate CF subjects (19.5% ± 15.5%, P = 0.001) but not from mild CF subjects (10.4% ± 9.9%, P = 0.24). In contrast, the fractional lung volume with slow gas wash-out was elevated both in subjects with mild (9.61% ± 4.87%; P = 0.0066) and moderate CF (16.01% ± 5.01%; P = 0.0002) when compared with healthy controls (3.84% ± 2.16%) and distinguished mild from moderate CF (P = 0.006). 19F MRI detected significant ventilation abnormalities in subjects with CF. The ability of gas wash-out kinetics to distinguish between healthy and mild CF lung disease subjects makes 19F MRI a potentially valuable method for the characterization of early lung disease in CF

Statistics and State-istics : exclusion categories in the population census (Belgium, 1846-1930)

Peer reviewe

Maternal distress and perceptions of infant development following extracorporeal membrane oxygenation and conventional ventilation for persistent pulmonary hypertension

Neurodevelopmental outcome and concurrent maternal distress were examined for infants who suffered persistent pulmonary hypertension at birth and were treated with either extracorporeal membrane oxygenation (ECMO) ( n = 19) or conventional ventilation (CV) ( n = 15). Mothers were asked to complete inventories assessing their infant's (mean age 8.74 months) developmental growth as well as their own psychological health. Relevant sociodemographic and treatment parameters were also entered into the analysis. The results indicated that ECMO and CV infants did not differ on developmental indices and impairment rates were 15–23% respectively, similar to previous reports, in addition, ECMO and CV mothers did not differ in their reports of psychological distress. Correlational analyses revealed that length of treatment for ECMO but not CV infants significantly predicted developmental delay and maternal distress. For CV mothers, maternal distress was associated with the perception of delayed language. The results are discussed in terms of the limited morbidity associated with ECMO and CV interventions and the possible role of a ‘vulnerable child syndrome’ in understanding the maternal-infant relationship following ECMO therapy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73367/1/j.1365-2214.1995.tb00410.x.pd

Meta-analysis of type 2 Diabetes in African Americans Consortium

Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15 × 10(-94)<P<5 × 10(-8), odds ratio (OR)  = 1.09 to 1.36). Fine-mapping revealed that 88 of 158 previously identified T2D or glucose homeostasis loci demonstrated nominal to highly significant association (2.2 × 10(-23) < locus-wide P<0.05). These novel and previously identified loci yielded a sibling relative risk of 1.19, explaining 17.5% of the phenotypic variance of T2D on the liability scale in African Americans. Overall, this study identified two novel susceptibility loci for T2D in African Americans. A substantial number of previously reported loci are transferable to African Americans after accounting for linkage disequilibrium, enabling fine mapping of causal variants in trans-ethnic meta-analysis studies.Peer reviewe

On Phase Transitions in Learning Sparse Networks

In this paper [1] we study the identification of sparse interaction networks, from a given set of observations, as a machine learning problem. An example of such a network is a sparse gene-protein interaction network, for more details see [2]. Sparsity means that we are provided with a small data set and a high number of unknown components of the system, most of which are zero. Under these circumstances, a model needs to be learned that fits the underlying system, capable of generalization. This corresponds to the student-teacher setting in machine learning. In some engineering applications, the number of measurements M available for system identification and model validation is much smaller than the system order N, which represents the number of components. This substantial lack of data can give rise to an identifiability problem, in which case a larger subset of the model class is entirely consistent with the observed data so that no unique model can be proposed. Since conventional techniques for system identification are not well suited to deal with such situations, it thus becomes important to work around this by exploiting as much additional information as possible about the underlying system. In particular, we are interested i