High incidence of acute lung injury in children with Down syndrome

OBJECTIVE: Acute respiratory tract infection is a common reason for hospitalization in children with Down syndrome (CDS) and is characterized by a high morbidity. The severe course of disease in CDS may be related to a higher incidence of acute lung injury (ALI). This study evaluated the incidence of ALI and acute respiratory distress syndrome (ARDS) in mechanically ventilated CDS. DESIGN AND SETTING: Retrospective cohort study in a pediatric ICU. PATIENTS AND PARTICIPANTS: Cases were all mechanically ventilated CDS admitted to our unit between January 1998 and July 2005. All mechanically ventilated patients without Down syndrome from January 1998 to January 2001 served as controls. Postoperative patients (cases and controls) and those with a cardiac left to right shunt were excluded. MEASUREMENTS AND RESULTS: The main outcome measure was the incidence of ALI and ARDS. The criteria for ALI were met in 14 of 24 CDS (58.3%) in 41 of 317 of controls (12.9%; OR 9.4, 95% CI 3.9-22.6). The criteria for ARDS were met in 11 of 24 CDS (46%) and in 21 of 317 of controls (7%; OR 11.9, 95% CI 4.8-29.8). None of the CDS with ALI died; in the control group ten patients with ALI died. CONCLUSIONS: CDS had a significantly higher incidence of ALI and ARDS than children without Down syndrome. The explanation for this remains to be elucidated; further study is necessary before clinical implications become clea

Discovery of molecular subtypes in leiomyosarcoma through integrative molecular profiling.

Leiomyosarcoma (LMS) is a soft tissue tumor with a significant degree of morphologic and molecular heterogeneity. We used integrative molecular profiling to discover and characterize molecular subtypes of LMS. Gene expression profiling was performed on 51 LMS samples. Unsupervised clustering showed three reproducible LMS clusters. Array comparative genomic hybridization (aCGH) was performed on 20 LMS samples and showed that the molecular subtypes defined by gene expression showed distinct genomic changes. Tumors from the muscle-enriched cluster showed significantly increased copy number changes (P=0.04). A majority of the muscle-enriched cases showed loss at 16q24, which contains Fanconi anemia, complementation group A, known to have an important role in DNA repair, and loss at 1p36, which contains PRDM16, of which loss promotes muscle differentiation. Immunohistochemistry (IHC) was performed on LMS tissue microarrays (n=377) for five markers with high levels of messenger RNA in the muscle-enriched cluster (ACTG2, CASQ2, SLMAP, CFL2 and MYLK) and showed significantly correlated expression of the five proteins (all pairwise P<0.005). Expression of the five markers was associated with improved disease-specific survival in a multivariate Cox regression analysis (P<0.04). In this analysis that combined gene expression profiling, aCGH and IHC, we characterized distinct molecular LMS subtypes, provided insight into their pathogenesis, and identified prognostic biomarkers

Gibbs sampling with people

A core problem in cognitive science and machine learning is to understand how humans derive semantic representations from perceptual objects, such as color from an apple, pleasantness from a musical chord, or seriousness from a face. Markov Chain Monte Carlo with People (MCMCP) is a prominent method for studying such representations, in which participants are presented with binary choice trials constructed such that the decisions follow a Markov Chain Monte Carlo acceptance rule. However, while MCMCP has strong asymptotic properties, its binary choice paradigm generates relatively little information per trial, and its local proposal function makes it slow to explore the parameter space and find the modes of the distribution. Here we therefore generalize MCMCP to a continuous-sampling paradigm, where in each iteration the participant uses a slider to continuously manipulate a single stimulus dimension to optimize a given criterion such as 'pleasantness'. We formulate both methods from a utility-theory perspective, and show that the new method can be interpreted as 'Gibbs Sampling with People' (GSP). Further, we introduce an aggregation parameter to the transition step, and show that this parameter can be manipulated to flexibly shift between Gibbs sampling and deterministic optimization. In an initial study, we show GSP clearly outperforming MCMCP; we then show that GSP provides novel and interpretable results in three other domains, namely musical chords, vocal emotions, and faces. We validate these results through large-scale perceptual rating experiments. The final experiments use GSP to navigate the latent space of a state-of-the-art image synthesis network (StyleGAN), a promising approach for applying GSP to high-dimensional perceptual spaces. We conclude by discussing future cognitive applications and ethical implications

Gibbs sampling with people

A core problem in cognitive science and machine learning is to understand how humans derive semantic representations from perceptual objects, such as color from an apple, pleasantness from a musical chord, or seriousness from a face. Markov Chain Monte Carlo with People (MCMCP) is a prominent method for studying such representations, in which participants are presented with binary choice trials constructed such that the decisions follow a Markov Chain Monte Carlo acceptance rule. However, while MCMCP has strong asymptotic properties, its binary choice paradigm generates relatively little information per trial, and its local proposal function makes it slow to explore the parameter space and find the modes of the distribution. Here we therefore generalize MCMCP to a continuous-sampling paradigm, where in each iteration the participant uses a slider to continuously manipulate a single stimulus dimension to optimize a given criterion such as 'pleasantness'. We formulate both methods from a utility-theory perspective, and show that the new method can be interpreted as 'Gibbs Sampling with People' (GSP). Further, we introduce an aggregation parameter to the transition step, and show that this parameter can be manipulated to flexibly shift between Gibbs sampling and deterministic optimization. In an initial study, we show GSP clearly outperforming MCMCP; we then show that GSP provides novel and interpretable results in three other domains, namely musical chords, vocal emotions, and faces. We validate these results through large-scale perceptual rating experiments. The final experiments use GSP to navigate the latent space of a state-of-the-art image synthesis network (StyleGAN), a promising approach for applying GSP to high-dimensional perceptual spaces. We conclude by discussing future cognitive applications and ethical implications

Osteoporosis in children and adolescents:when to suspect and how to diagnose it

Early recognition of osteoporosis in children and adolescents is important in order to establish an appropriate diagnosis of the underlying condition and to initiate treatment if necessary. In this review, we present the diagnostic work-up, and its pitfalls, of pediatric patients suspected of osteoporosis including a careful collection of the medical and personal history, a complete physical examination, biochemical data, molecular genetics, and imaging techniques. The most recent and relevant literature has been reviewed to offer a broad overview on the topic. Genetic and acquired pediatric bone disorders are relatively common and cause substantial morbidity. In recent years, there has been significant progress in the understanding of the genetic and molecular mechanistic basis of bone fragility and in the identification of acquired causes of osteoporosis in children. Specifically, drugs that can negatively impact bone health (e.g. steroids) and immobilization related to acute and chronic diseases (e.g. Duchenne muscular dystrophy) represent major risk factors for the development of secondary osteoporosis and therefore an indication to screen for bone mineral density and vertebral fractures. Long-term studies in children chronically treated with steroids have resulted in the development of systematic approaches to diagnose and manage pediatric osteoporosis. Conclusions: Osteoporosis in children requires consultation with and/or referral to a pediatric bone specialist. This is particularly relevant since children possess the unique ability for spontaneous and medication-assisted recovery, including reshaping of vertebral fractures. As such, pediatricians have an opportunity to improve bone mass accrual and musculoskeletal health in osteoporotic children.What is Known:• Both genetic and acquired pediatric disorders can compromise bone health and predispose to fractures early in life.• The identification of children at risk of osteoporosis is essential to make a timely diagnosis and start the treatment, if necessary.What is New:• Pediatricians have an opportunity to improve bone mass accrual and musculoskeletal health in osteoporotic children and children at risk of osteoporosis.• We offer an extensive but concise overview about the risk factors for osteoporosis and the diagnostic work-up (and its pitfalls) of pediatric patients suspected of osteoporosis