Review: Carbon Nanotubes and Chronic Granulomatous Disease

Use of nanomaterials in manufactured consumer products is a rapidly expanding industry and potential toxicities are just beginning to be explored. Combustion-generated multiwall carbon nanotubes (MWCNT) or nanoparticles are ubiquitous in non-manufacturing environments and detectable in vapors from diesel fuel, methane, propane, and natural gas. In experimental animal models, carbon nanotubes have been shown to induce granulomas or other inflammatory changes. Evidence suggesting potential involvement of carbon nanomaterials in human granulomatous disease, has been gathered from analyses of dusts generated in the World Trade Center disaster combined with epidemiological data showing a subsequent increase in granulomatous disease of first responders. In this review we will discuss evidence for similarities in the pathophysiology of carbon nanotube-induced pulmonary disease in experimental animals with that of the human granulomatous disease, sarcoidosis

Novel wet laid nonwoven carbon fiber mats and their composites

In the wake of lightweight and specific strength, composite materials are increasingly used for few decades. In order to meet the industry production rates, a novel mixing method has been developed in this work that provides more control on fiber length and homogeneity in wet-laid (WL) carbon fiber (CF) mats. The WL process has been adopted from papermaking industries to produce non-woven CF fiber mats.This work investigates the production of CF mats in three main phases; (a) First, the mixing regime of the WL method is explored to optimize the process of fiber dispersion. Experimental and theoretical computational fluid dynamics (CFD) studies have been conducted to understand the different factors of the process, in order to obtain the most optimal time of production. Mats produced are imaged through the Back Light Scattering (BLS) technique and computationally analyzed using a Matlab generated code to determine the fiber density distribution through pixel counts and compare the improved results of the mixing method developed in this work to the traditional propeller mixing. Processing time was reduced by 60% to produce a mat on laboratory scale with optimal characteristics; (b) Second composites were made from mats produced by each of the two mixing methods presented in the first part of the work. An object oriented finite element analysis (OFF) investigated the isotropic nature of the composites. The mechanical properties of these composites were evaluated in tensile, flex and inter laminar shear (ILSS). Tensile data showed improvement in standard deviation between samples collected from plates made with mats produced through the innovated mixing method when comparing them to composites made with the mats produced through the traditional method; and (c) Third, the in-plane permeability of the mats was analyzed in respect to changes in the fiber length and mats grammage per square meter (gsm) and a link between local permeability in response to changes in complex geometries is investigated.The novel mixing method for fiber distribution in WL discussed in this work presents an innovation in composites production, leading to improved production rate of nonwoven CF mats, ease of production and reproducibility of composites

Cardiac output assessment in pregnancy: comparison of two automated monitors with echocardiography.

OBJECTIVE: To compare non-invasive hemodynamic measurements obtained in pregnant and postpartum women using two automated cardiac output monitors against those obtained by two-dimensional (2D) transthoracic echocardiography (TTE). METHODS: This was a cross-comparison study into which we recruited 114 healthy women, either with normal singleton pregnancy (across all three trimesters) or within 72 hours following delivery. Cardiac output estimations were obtained non-invasively using two different monitors, Ultrasound Cardiac Output Monitor (USCOM®, which uses continuous-wave Doppler analysis of transaortic blood flow) and Non-Invasive Cardiac Output Monitor (NICOM®, which uses thoracic bioreactance), and 2D-TTE. The performance of each monitor was assessed relative to that of TTE by calculating bias, precision, 95% limits of agreement and mean percentage difference (MPD). Intraobserver repeatability was assessed for both monitors and interobserver reproducibility was assessed for USCOM, NICOM being operator-independent. RESULTS: Following exclusions due to poor-quality results of a monitor or TTE, or for medical reasons, our analysis included 98 women (29 in the first trimester, 25 in the second and 21 in the third, and 23 postpartum). For cardiac output estimation, when compared with TTE, USCOM had a bias ranging from 0.4 to 0.9 L/min. The MPD of USCOM was 29% in the third-trimester cohort. NICOM had a bias ranging from -1.0 to 0.6 L/min, with a MPD of 32% in the third-trimester group. There was limited agreement between the cardiac output monitors and TTE in the first and second trimesters, with a MPD of 38% for USCOM in both first and second trimesters, and 71% and 61% for NICOM in first and second trimesters, respectively. For cardiac output estimation using USCOM, we found excellent intraobserver repeatability (intraclass correlation coefficient (ICC), 0.97; 95% CI, 0.95-0.98) and interobserver reproducibility (ICC, 0.90; 95% CI, 0.81-0.94), and the repeatability for NICOM was comparable (ICC, 0.95; 95% CI, 0.93-0.97). CONCLUSIONS: We found good agreement of both USCOM and NICOM when compared with 2D-TTE, specifically in the third trimester of pregnancy. Both devices had good intraobserver repeatability and either had good interobserver reproducibility or were operator-independent. Future studies should take into account the significant differences in the precise maternal hemodynamic values obtained by these devices, and consider developing device-specific reference ranges in pregnancy and the postpartum period. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

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

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

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

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

Biopsy-Proven Acute Tubular Necrosis due to Vancomycin Toxicity

Vancomycin (VAN) has been associated with acute kidney injury (AKI) since it has been put into clinical use in the 1950's. Early reports of AKI were likely linked to the impurities of the VAN preparation. With the advent of the more purified forms of VAN, the incidence of AKI related to VAN were limited to acute interstitial nephritis (AIN) or as a potentiating agent to other nephrotoxins such as Aminoglycosides. VAN as the sole etiologic factor for nephrotoxic acute tubular necrosis (ATN) has not been described. Here, we report a case of biopsy-proven ATN resulting from VAN

Shopping Queries Image Dataset (SQID): An Image-Enriched ESCI Dataset for Exploring Multimodal Learning in Product Search

Recent advances in the fields of Information Retrieval and Machine Learning have focused on improving the performance of search engines to enhance the user experience, especially in the world of online shopping. The focus has thus been on leveraging cutting-edge learning techniques and relying on large enriched datasets. This paper introduces the Shopping Queries Image Dataset (SQID), an extension of the Amazon Shopping Queries Dataset enriched with image information associated with 190,000 products. By integrating visual information, SQID facilitates research around multimodal learning techniques that can take into account both textual and visual information for improving product search and ranking. We also provide experimental results leveraging SQID and pretrained models, showing the value of using multimodal data for search and ranking. SQID is available at: https://github.com/Crossing-Minds/shopping-queries-image-dataset