Cortical thickness development of human primary visual cortex related to the age of blindness onset

Blindness primarily induces structural alteration in the primary visual cortex (V1). Some studies have found that the early blind subjects had a thicker V1 compared to sighted controls, whereas late blind subjects showed no significant differences in the V1. This implies that the age of blindness onset may exert significant effects on the development of cortical thickness of the V1. However, no previous research used a trajectory of the age of blindness onset-related changes to investigate these effects. Here we explored this issue by mapping the cortical thickness trajectory of the V1 against the age of blindness onset using data from 99 blind individuals whose age of blindness onset ranged from birth to 34\ua0years. We found that the cortical thickness of the V1 could be fitted well with a quadratic curve in both the left (F\ua0=\ua011.59, P\ua0=\ua03\ua0×\ua010) and right hemispheres (F\ua0=\ua06.54, P\ua0=\ua02\ua0×\ua010). Specifically, the cortical thickness of the V1 thinned rapidly during childhood and adolescence and did not change significantly thereafter. This trend was not observed in the primary auditory cortex (A1), primary motor cortex (M1), or primary somatosensory cortex (S1). These results provide evidence that an onset of blindness before adulthood significantly affects the cortical thickness of the V1 and suggest a critical period for cortical development of the human V1

Demographics.

<p>Demographics.</p

Diagnosis of PE from control with serum biomarkers.

<p>Left panel: estimated PE scores were computed from the PE serum peptide panel PAM model as a function of the gestational weeks; right panel: the log sFlt-1/PIGF serum concentration ratio was plotted as a function of the gestational weeks. Red indicates known PE cases; green indicates known healthy pregnancy controls. For either PE or control sample category, a loess curve was fitted to represent the overall trend of biomarker scoring as a function of gestational age.</p

Serum peptide biomarkers identified to separate PE and control subjects.

<p>FGA:</p>*<p>cluster 1;</p>**<p>cluster 2;</p>***<p>cluster 3;</p>****<p>cluster 4.</p><p>Score and minimal false discovery rate (<i>q</i> value) were computed using SAM algorithm.</p

PE patients' presenting signs and symptoms.

<p>PE patients' presenting signs and symptoms.</p

The serum concentrations of sFlt-1 (left) and PIGF (right) as a function of the gestation.

<p>For either PE (red) or control (green) data points, a loess curve was fitted to represent the overall trend of biomarker serum abundance as a function of gestation.</p

PAM predictive analysis of the 19-peptide biomarker panel differentiating PE from control samples.

<p>PAM prediction was performed with training data from PE (training, n = 21; testing, n = 10) and control (training, n = 21; testing, n = 10) samples evaluated with the biomarker panel. Samples are partitioned by the true class (upper) and predicted class (lower). The classification results from training and test sets are shown as 2 by 2 contingency tables, calculating the percentage of classifications that agreed with clinical diagnosis.</p

Correlation analyses of clinical and molecular findings identify candidate biological pathways in systemic juvenile idiopathic arthritis

Abstract Background Clinicians have long appreciated the distinct phenotype of systemic juvenile idiopathic arthritis (SJIA) compared to polyarticular juvenile idiopathic arthritis (POLY). We hypothesized that gene expression profiles of peripheral blood mononuclear cells (PBMC) from children with each disease would reveal distinct biological pathways when analyzed for significant associations with elevations in two markers of JIA activity, erythrocyte sedimentation rate (ESR) and number of affected joints (joint count, JC). Methods PBMC RNA from SJIA and POLY patients was profiled by kinetic PCR to analyze expression of 181 genes, selected for relevance to immune response pathways. Pearson correlation and Student's t-test analyses were performed to identify transcripts significantly associated with clinical parameters (ESR and JC) in SJIA or POLY samples. These transcripts were used to find related biological pathways. Results Combining Pearson and t-test analyses, we found 91 ESR-related and 92 JC-related genes in SJIA. For POLY, 20 ESR-related and 0 JC-related genes were found. Using Ingenuity Systems Pathways Analysis, we identified SJIA ESR-related and JC-related pathways. The two sets of pathways are strongly correlated. In contrast, there is a weaker correlation between SJIA and POLY ESR-related pathways. Notably, distinct biological processes were found to correlate with JC in samples from the earlier systemic plus arthritic phase (SAF) of SJIA compared to samples from the later arthritis-predominant phase (AF). Within the SJIA SAF group, IL-10 expression was related to JC, whereas lack of IL-4 appeared to characterize the chronic arthritis (AF) subgroup. Conclusions The strong correlation between pathways implicated in elevations of both ESR and JC in SJIA argues that the systemic and arthritic components of the disease are related mechanistically. Inflammatory pathways in SJIA are distinct from those in POLY course JIA, consistent with differences in clinically appreciated target organs. The limited number of ESR-related SJIA genes that also are associated with elevations of ESR in POLY implies that the SJIA associations are specific for SJIA, at least to some degree. The distinct pathways associated with arthritis in early and late SJIA raise the possibility that different immunobiology underlies arthritis over the course of SJIA.</p

Investigation of maternal environmental exposures in association with self-reported preterm birth

Identification of maternal environmental factors influencing preterm birth risks is important to understand the reasons for the increase in prematurity since 1990. Here, we utilized a health survey, the US National Health and Nutrition Examination Survey (NHANES) to search for personal environmental factors associated with preterm birth. 201 urine and blood markers of environmental factors, such as allergens, pollutants, and nutrients were assayed in mothers (range of N: 49-724) who answered questions about any children born preterm (delivery &lt;37 weeks). We screened each of the 201 factors for association with any child born preterm adjusting by age, race/ethnicity, education, and household income. We attempted to verify the top finding, urinary bisphenol A, in an independent study of pregnant women attending Lucile Packard Children's Hospital. We conclude that the association between maternal urinary levels of bisphenol A and preterm birth should be evaluated in a larger epidemiological investigation

A Data-Driven Algorithm Integrating Clinical and Laboratory Features for the Diagnosis and Prognosis of Necrotizing Enterocolitis

<div><p>Background</p><p>Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting.</p><p>Study design</p><p>A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data.</p><p>Results</p><p>Machine learning using clinical and laboratory results at the time of clinical presentation led to two NEC models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner.</p><p>Algorithm availability</p><p><a href="http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl" target="_blank">http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl</a> and smartphone application upon request.</p></div