240 research outputs found

    Predicting which children with juvenile idiopathic arthritis will not attain early remission with conventional treatment: Results from the Reacch-out cohort

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    Objective. To estimate the probability of early remission with conventional treatment for each child with juvenile idiopathic arthritis (JIA). Children with a low chance of remission may be candidates for initial treatment with biologics or triple disease-modifying antirheumatic drugs (DMARD). Methods. We used data from 1074 subjects in the Research in Arthritis in Canadian Children emphasizing Outcomes (ReACCh-Out) cohort. The predicted outcome was clinically inactive disease for ≥ 6 months starting within 1 year of JIA diagnosis in patients who did not receive early biologic agents or triple DMARD. Models were developed in 200 random splits of 75% of the cohort and tested on the remaining 25% of subjects, calculating expected and observed frequencies of remission and c-index values. Results. Our best Cox logistic model combining 18 clinical variables a median of 2 days after diagnosis had a c-index of 0.69 (95% CI 0.67-0.71), better than using JIA category alone (0.59, 95% CI 0.56-0.63). Children in the lowest probability decile had a 20% chance of remission and 21% attained remission; children in the highest decile had a 69% chance of remission and 73% attained remission. Compared to 5% of subjects identified by JIA category alone, the model identified 14% of subjects as low chance of remission (probability \u3c 0.25), of whom 77% failed to attain remission. Conclusion. Although the model did not meet our a priori performance threshold (c-index \u3e 0.70), it identified 3 times more subjects with low chance of remission than did JIA category alone, and it may serve as a benchmark for assessing value added by future laboratory/imaging biomarkers

    Synaptic and Intrinsic Activation of GABAergic Neurons in the Cardiorespiratory Brainstem Network

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    GABAergic pathways in the brainstem play an essential role in respiratory rhythmogenesis and interactions between the respiratory and cardiovascular neuronal control networks. However, little is known about the identity and function of these GABAergic inhibitory neurons and what determines their activity. In this study we have identified a population of GABAergic neurons in the ventrolateral medulla that receive increased excitatory post-synaptic potentials during inspiration, but also have spontaneous firing in the absence of synaptic input. Using transgenic mice that express GFP under the control of the Gad1 (GAD67) gene promoter, we determined that this population of GABAergic neurons is in close apposition to cardioinhibitory parasympathetic cardiac neurons in the nucleus ambiguus (NA). These neurons fire in synchronization with inspiratory activity. Although they receive excitatory glutamatergic synaptic inputs during inspiration, this excitatory neurotransmission was not altered by blocking nicotinic receptors, and many of these GABAergic neurons continue to fire after synaptic blockade. The spontaneous firing in these GABAergic neurons was not altered by the voltage-gated calcium channel blocker cadmium chloride that blocks both neurotransmission to these neurons and voltage-gated Ca2+ currents, but spontaneous firing was diminished by riluzole, demonstrating a role of persistent sodium channels in the spontaneous firing in these cardiorespiratory GABAergic neurons that possess a pacemaker phenotype. The spontaneously firing GABAergic neurons identified in this study that increase their activity during inspiration would support respiratory rhythm generation if they acted primarily to inhibit post-inspiratory neurons and thereby release inspiration neurons to increase their activity. This population of inspiratory-modulated GABAergic neurons could also play a role in inhibiting neurons that are most active during expiration and provide a framework for respiratory sinus arrhythmia as there is an increase in heart rate during inspiration that occurs via inhibition of premotor parasympathetic cardioinhibitory neurons in the NA during inspiration

    Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

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    Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

    Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

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    This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

    Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

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    Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

    Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

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    Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment
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