2,221 research outputs found
Immediate remote ischemic postconditioning after hypoxia ischemia in piglets protects cerebral white matter but not grey matter
Remote ischemic postconditioning (RIPostC) is a promising therapeutic intervention whereby brief episodes of ischemia/reperfusion of one organ (limb) mitigate damage in another organ (brain) that has experienced severe hypoxia-ischemia. Our aim was to assess whether RIPostC is protective following cerebral hypoxia-ischemia in a piglet model of neonatal encephalopathy (NE) using magnetic resonance spectroscopy (MRS) biomarkers and immunohistochemistry. After hypoxia-ischemia (HI), 16 Large White female newborn piglets were randomized to: (i) no intervention (n = 8); (ii) RIPostC - with four, 10-min cycles of bilateral lower limb ischemia/reperfusion immediately after HI (n = 8). RIPostC reduced the hypoxic-ischemic-induced increase in white matter proton MRS lactate/N acetyl aspartate (p = 0.005) and increased whole brain phosphorus-31 MRS ATP (p = 0.039) over the 48 h after HI. Cell death was reduced with RIPostC in the periventricular white matter (p = 0.03), internal capsule (p = 0.002) and corpus callosum (p = 0.021); there was reduced microglial activation in corpus callosum (p = 0.001) and more surviving oligodendrocytes in corpus callosum (p = 0.029) and periventricular white matter (p = 0.001). Changes in gene expression were detected in the white matter at 48 h, including KATP channel and endothelin A receptor. Immediate RIPostC is a potentially safe and promising brain protective therapy for babies with NE with protection in white but not grey matter
Genome Diversity, Recombination, and Virulence across the Major Lineages of Paracoccidioides
We thank Angela Restrepo, Rosana Puccia, Zoilo Pires de Camargo, and Maria Sueli Felipe for kindly providing the isolates for this study. This project has been funded in whole or in part with federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract no. HHSN272200900018C. This work was partly supported by Colciencias via the grants “A Gene Atlas for Human Pathogenic Fungi” (122256934875) and “A Comprehensive Genomic and Transcriptomic Analysis of Dimorphic Human Pathogen Fungi and Its Relation with Virulence” (221365842971) and by the Universidad de Antioquia via a “Sostenibilidad 2015/2016” grant. Colciencias National Doctorate Program funding supported J.F.M.; Enlaza Mundos partly supported his fellowship. The Wellcome Trust supported R.A.F.Peer reviewedPublisher PD
Reproducibility of arterial spin labeling cerebral blood flow image processing: a report of the ISMRM open science initiative for perfusion imaging (OSIPI) and the ASL MRI challenge
Purpose: Arterial spin labeling (ASL) is a widely used contrast-free MRI method for assessing cerebral blood flow (CBF). Despite the generally adopted ASL acquisition guidelines, there is still wide variability in ASL analysis. We explored this variability through the ISMRM-OSIPI ASL-MRI Challenge, aiming to establish best practices for more reproducible ASL analysis.
Methods: Eight teams analyzed the challenge data, which included a high-resolution T1-weighted anatomical image and 10 pseudo-continuous ASL datasets simulated using a digital reference object to generate ground-truth CBF values in normal and pathological states. We compared the accuracy of CBF quantification from each team's analysis to the ground truth across all voxels and within predefined brain regions. Reproducibility of CBF across analysis pipelines was assessed using the intra-class correlation coefficient (ICC), limits of agreement (LOA), and replicability of generating similar CBF estimates from different processing approaches.
Results: Absolute errors in CBF estimates compared to ground-truth synthetic data ranged from 18.36 to 48.12 mL/100 g/min. Realistic motion incorporated into three datasets produced the largest absolute error and variability between teams, with the least agreement (ICC and LOA) with ground-truth results. Fifty percent of the submissions were replicated, and one produced three times larger CBF errors (46.59 mL/100 g/min) compared to submitted results.
Conclusions: Variability in CBF measurements, influenced by differences in image processing, especially to compensate for motion, highlights the significance of standardizing ASL analysis workflows. We provide a recommendation for ASL processing based on top-performing approaches as a step toward ASL standardization
Reproducibility of arterial spin labeling cerebral blood flow image processing:A report of the ISMRM open science initiative for perfusion imaging (OSIPI) and the ASL MRI challenge
Purpose: Arterial spin labeling (ASL) is a widely used contrast-free MRI method for assessing cerebral blood flow (CBF). Despite the generally adopted ASL acquisition guidelines, there is still wide variability in ASL analysis. We explored this variability through the ISMRM-OSIPI ASL-MRI Challenge, aiming to establish best practices for more reproducible ASL analysis. Methods: Eight teams analyzed the challenge data, which included a high-resolution T1-weighted anatomical image and 10 pseudo-continuous ASL datasets simulated using a digital reference object to generate ground-truth CBF values in normal and pathological states. We compared the accuracy of CBF quantification from each team's analysis to the ground truth across all voxels and within predefined brain regions. Reproducibility of CBF across analysis pipelines was assessed using the intra-class correlation coefficient (ICC), limits of agreement (LOA), and replicability of generating similar CBF estimates from different processing approaches. Results: Absolute errors in CBF estimates compared to ground-truth synthetic data ranged from 18.36 to 48.12 mL/100 g/min. Realistic motion incorporated into three datasets produced the largest absolute error and variability between teams, with the least agreement (ICC and LOA) with ground-truth results. Fifty percent of the submissions were replicated, and one produced three times larger CBF errors (46.59 mL/100 g/min) compared to submitted results. Conclusions: Variability in CBF measurements, influenced by differences in image processing, especially to compensate for motion, highlights the significance of standardizing ASL analysis workflows. We provide a recommendation for ASL processing based on top-performing approaches as a step toward ASL standardization.</p
Reproducibility of arterial spin labeling cerebral blood flow image processing:A report of the ISMRM open science initiative for perfusion imaging (OSIPI) and the ASL MRI challenge
Purpose: Arterial spin labeling (ASL) is a widely used contrast-free MRI method for assessing cerebral blood flow (CBF). Despite the generally adopted ASL acquisition guidelines, there is still wide variability in ASL analysis. We explored this variability through the ISMRM-OSIPI ASL-MRI Challenge, aiming to establish best practices for more reproducible ASL analysis. Methods: Eight teams analyzed the challenge data, which included a high-resolution T1-weighted anatomical image and 10 pseudo-continuous ASL datasets simulated using a digital reference object to generate ground-truth CBF values in normal and pathological states. We compared the accuracy of CBF quantification from each team's analysis to the ground truth across all voxels and within predefined brain regions. Reproducibility of CBF across analysis pipelines was assessed using the intra-class correlation coefficient (ICC), limits of agreement (LOA), and replicability of generating similar CBF estimates from different processing approaches. Results: Absolute errors in CBF estimates compared to ground-truth synthetic data ranged from 18.36 to 48.12 mL/100 g/min. Realistic motion incorporated into three datasets produced the largest absolute error and variability between teams, with the least agreement (ICC and LOA) with ground-truth results. Fifty percent of the submissions were replicated, and one produced three times larger CBF errors (46.59 mL/100 g/min) compared to submitted results. Conclusions: Variability in CBF measurements, influenced by differences in image processing, especially to compensate for motion, highlights the significance of standardizing ASL analysis workflows. We provide a recommendation for ASL processing based on top-performing approaches as a step toward ASL standardization.</p
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Prion-like domain mutations in hnRNPs cause multisystem proteinopathy and ALS
Summary Algorithms designed to identify canonical yeast prions predict that ~250 human proteins, including several RNA-binding proteins associated with neurodegenerative disease, harbor a distinctive prion-like domain (PrLD) enriched in uncharged polar amino acids and glycine. PrLDs in RNA-binding proteins are essential for the assembly of ribonucleoprotein granules. However, the interplay between human PrLD function and disease is not understood. Here, we define pathogenic mutations in PrLDs of hnRNPA2/B1 and hnRNPA1 in families with inherited degeneration affecting muscle, brain, motor neuron and bone, and a case of familial ALS. Wild-type hnRNPA2 and hnRNPA1 display an intrinsic tendency to assemble into self-seeding fibrils, which is exacerbated by the disease mutations. Indeed, the pathogenic mutations strengthen a ‘steric zipper’ motif in the PrLD, which accelerates formation of self-seeding fibrils that cross-seed polymerization of wild-type hnRNP. Importantly, the disease mutations promote excess incorporation of hnRNPA2 and hnRNPA1 into stress granules and drive the formation of cytoplasmic inclusions in animal models that recapitulate the human pathology. Thus, dysregulated polymerization caused by a potent mutant ‘steric zipper’ motif in a PrLD can initiate degenerative disease. Related proteins with PrLDs must be considered candidates for initiating and perhaps propagating proteinopathies of muscle, brain, motor neuron and bone
Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas
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 Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context
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
Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas
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
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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