Identification of Pre-Diagnostic Metabolic Patterns for Glioma Using Subset Analysis of Matched Repeated Time Points

Here, we present a strategy for early molecular marker pattern detection—Subset analysis of Matched Repeated Time points (SMART)—used in a mass-spectrometry-based metabolomics study of repeated blood samples from future glioma patients and their matched controls. The outcome from SMART is a predictive time span when disease-related changes are detectable, defined by time to diagnosis and time between longitudinal sampling, and visualization of molecular marker patterns related to future disease. For glioma, we detect significant changes in metabolite levels as early as eight years before diagnosis, with longitudinal follow up within seven years. Elevated blood plasma levels of myo-inositol, cysteine, N-acetylglucosamine, creatinine, glycine, proline, erythronic-, 4-hydroxyphenylacetic-, uric-, and aceturic acid were particularly evident in glioma cases. We use data simulation to ensure non-random events and a separate data set for biomarker validation. The latent biomarker, consisting of 15 interlinked and significantly altered metabolites, shows a strong correlation to oxidative metabolism, glutathione biosynthesis and monosaccharide metabolism, linked to known early events in tumor development. This study highlights the benefits of progression pattern analysis and provide a tool for the discovery of early markers of disease.

Download Full-text

Back in Time for Breakfast: An Analysis of the Changing Breakfast Cereal Aisle

Nutrients ◽

10.3390/nu13020489 ◽

2021 ◽

Vol 13 (2) ◽

pp. 489

Author(s):

Emilie Croisier ◽

Jaimee Hughes ◽

Stephanie Duncombe ◽

Sara Grafenauer

Keyword(s):

Dietary Fibre ◽

Saturated Fat ◽

Health Claims ◽

Whole Grain ◽

Breakfast Cereal ◽

Full Data ◽

Data Set ◽

Time Points ◽

Total Fat ◽

Over Time

Breakfast cereal improves overall diet quality yet is under constant scrutiny with assertions that the category has not improved over time. This study aimed to comprehensively analyse the category of breakfast cereals, the nutritional values, and health claims across eight distinct sub-categories at four time points (2013, 2015, 2018, and 2020). An audit of products from four major supermarkets in metropolitan Sydney (Aldi, Coles, IGA, and Woolworths) collected ingredient lists, nutrition information, claims and Health Star Rating (HSR) for biscuits and bites; brans; bubbles, puffs, and flakes; granola and clusters; hot cereal flavoured; hot cereal plain; muesli; breakfast biscuits. The median (IQR) were calculated for energy, protein, fat, saturated fat, carbohydrate, sugars, dietary fibre, and sodium for comparisons over time points by nutrient. Data from 2013 was compared with 2020 (by sub-category and then for a sub-section of common products available at each time point). Product numbers between 2013 (n = 283) and 2020 (n = 543) almost doubled, led by granola and clusters. Whole grain cereals ≥ 8 g/serve made up 67% of products (↑114%). While there were positive changes in nutrient composition over time within the full data set, the most notable changes were in the nutrition composition of cereals marketed as the same product in both years (n = 134); with decreases in mean carbohydrate (2%), sugar (10%) and sodium (16%) (p < 0.000), while protein and total fat increased significantly (p = 0.036; p = 0.021). Claims regarding Dietary Fibre and Whole Grain doubled since 2013. Analysis of sub-categories of breakfast cereal assisted in identifying some changes over time, but products common to both timeframes provided a clearer analysis of change within the breakfast category, following introduction of HSR. Whole grain products were lower in the two target nutrients, sodium and sugars, and well-chosen products represent a better choice within this category.

Download Full-text

Computational Approach to Identify Mutations in Genes of Notch Signaling Pathway and Its Association with OSCC

Journal of Pharmaceutical Research International ◽

10.9734/jpri/2020/v32i2030732 ◽

2020 ◽

pp. 84-92

Author(s):

Madhumithaa Sivarajan ◽

A. S. Smiline Girija ◽

A. Paramasivam ◽

J. Vijayashree Priyadharsini

Keyword(s):

Notch Signaling ◽

Signaling Pathway ◽

Tumor Development ◽

Suppressor Gene ◽

Notch Signaling Pathway ◽

Computational Approach ◽

Notch Signalling ◽

Cell Renewal ◽

Data Set ◽

The Stability

Derailments in signal transduction pathways are associated with the development of tumors. One such vital pathway is the Notch signaling pathway which is associated with various processes of carcinogenesis such as proliferation of cells, cell renewal, angiogenesis and oncogenic microenvironment preservation. Interestingly, Notch also plays a pivotal role in tumor development by acting as an oncogene as well as tumor suppressor gene. In view of this fact, the present study was designed to analyze mutations in Notch signalling pathway which might have a crucial role in the etiology of oral squamous cell carcinoma (OSCC) using computational approach. The Cancer Gene Atlas data set hosted in the cBioportal was used in the present study. These samples were queried for the presence of mutations in Notch signalling genes which included a predefined list of 55 genes. Further, the Oncoprint data obtained was compared to that of gnomAD database which identified novel and reported mutations in the genes analyzed. Additionally, I-Mutant and MutPred analysis was carried out to determine the stability and pathogenicity of the variations recorded. Among 55 genes analysed, SPEN gene was shown to possess the highest frequency of mutation (5%) followed by FBXW7, Notch1, EP300, NUMB, and RBPJL genes. Most of the mutations identified were novel as assessed using the control dataset from the gnomAD database. The stability of the protein was found to decrease upon nucleotide substitution. Finally, the MutPred score revealed that most of the mutant proteins were pathogenic. Several novel mutations have been identified in the pathway analyzed. Functional analysis of these variants using experimental approaches would aid in dissecting their association with OSCC.

Download Full-text

Newly Proposed Sepsis-Induced Coagulopathy Precedes International Society on Thrombosis and Haemostasis Overt-Disseminated Intravascular Coagulation and Predicts High Mortality

Journal of Intensive Care Medicine ◽

10.1177/0885066618773679 ◽

2018 ◽

Vol 35 (7) ◽

pp. 643-649 ◽

Cited By ~ 10

Author(s):

Toshiaki Iba ◽

Makoto Arakawa ◽

Marcello Di Nisio ◽

Satoshi Gando ◽

Hideaki Anan ◽

...

Keyword(s):

International Society ◽

Disseminated Intravascular Coagulation ◽

Early Warning ◽

Warning Sign ◽

Data Set ◽

Time Points ◽

Intravascular Coagulation ◽

Early Warning Sign ◽

Coagulation Tests ◽

The Relationship

Background: Disseminated intravascular coagulation (DIC) has been recognized as an urgent and critical condition in patients with sepsis. Therefore, unfamiliar and time-consuming tests or a complex scoring system are not suitable for diagnosis. Sepsis-induced coagulopathy (SIC), a newly proposed category delineated by a few global coagulation tests, has been established as an early warning sign for DIC. The purpose of this study was to elucidate the characteristics of SIC, especially in relation to the score of the International Society on Thrombosis and Haemostasis (ISTH) for overt DIC. Method: A data set for 332 patients with sepsis who were suspected to have DIC, antithrombin activity <70%, and treated with antithrombin substitution was utilized to examine the relationship between SIC and overt DIC. The performance of SIC calculated at baseline (ie, before treatment) as well as on days 2, 4, or 7 was analyzed in terms of its ability to predict 28-day mortality and overt DIC. Results: At baseline, 149 (98.7%) of 151 patients with overt DIC according to the ISTH definition were diagnosed as having SIC. Of the 49, 46 (93.9%) patients who developed overt DIC between days 2 and 4 had received a prior diagnosis of SIC. The sensitivity of baseline SIC for the prediction of death was significantly higher than that of overt DIC (86.8% vs 64.5%, P < .001). The sensitivity of SIC on days 2, 4, and 7 was significantly higher than those of overt DIC (96.1%, 92.3%, and 84.4% vs 67.1%, 57.7%, and 50.0%, P < .001, .001, and .001, respectively), although the specificity of SIC was lower at all time points.

Download Full-text

Simple Assay for Detection of the Central Asia Outbreak Clade of theMycobacterium tuberculosisBeijing Genotype

Journal of Clinical Microbiology ◽

10.1128/jcm.00215-19 ◽

2019 ◽

Vol 57 (7) ◽

Cited By ~ 9

Author(s):

Egor Shitikov ◽

Anna Vyazovaya ◽

Maja Malakhova ◽

Andrei Guliaev ◽

Julia Bespyatykh ◽

...

Keyword(s):

Soviet Union ◽

Molecular Marker ◽

Central Asia ◽

Former Soviet Union ◽

Multidrug Resistant ◽

Rapid Screening ◽

Beijing Genotype ◽

Data Set ◽

Content Type ◽

Mdr Tb

ABSTRACTThe Central Asia outbreak (CAO) clade is a branch of theMycobacterium tuberculosisBeijing genotype that is associated with multidrug resistance, increased transmissibility, and epidemic spread in parts of the former Soviet Union. Furthermore, migration flows bring these strains far beyond their areas of origin. We aimed to find a specific molecular marker of the Beijing CAO clade and develop a simple and affordable method for its detection. Based on the bioinformatics analysis of the largeM. tuberculosiswhole-genome sequencing (WGS) data set (n = 1,398), we identified an IS6110insertion in theRv1359-Rv1360intergenic region as a specific molecular marker of the CAO clade. We further designed and optimized a multiplex PCR method to detect this insertion. The method was validatedin silicowith the recently published WGS data set from Central Asia (n = 277) and experimentally withM. tuberculosisisolates from European and Asian parts of Russia, the former Soviet Union, and East Asia (n = 319). The developed molecular assay may be recommended for rapid screening of retrospective collections and for prospective surveillance when comprehensive but expensive WGS is not available or practical. The assay may be especially useful in high multidrug-resistant tuberculosis (MDR-TB) burden countries of the former Soviet Union and in countries with respective immigrant communities.

Download Full-text

Stress Erythropoiesis Elicits a Program of Gene Regulation That Overlaps with Interferon-γ.

Blood ◽

10.1182/blood.v104.11.2161.2161 ◽

2004 ◽

Vol 104 (11) ◽

pp. 2161-2161

Author(s):

Kai Huang ◽

Monica L. Bailey ◽

Dwayne L. Barber

Keyword(s):

Fetal Liver ◽

Liver Cells ◽

Early Gene ◽

Deubiquitinating Enzyme ◽

Wild Type ◽

Data Set ◽

Time Points ◽

Stress Erythropoiesis ◽

Adult Mice ◽

Fetal Liver Cells

Abstract Erythropoietin (EPO), the primary cytokine regulator of red blood cell production, acts through binding to its cognate receptor (EPO-R), which is primarily expressed on erythroid precursors. Knockout studies have illustrated a critical role for EPO, EPO-R and the downstream tyrosine kinase JAK2 in embryogenesis as mice lacking any of these components die from a fatal anemia at E13.5. These data suggest that EPO-R and/or JAK2 are required to promote erythropoiesis in vivo. EPO provides mitogenic, differentiative and cell survival signals to erythroid progenitors. We have performed microarray studies to identify target genes regulated by EPO in cell lines and primary cells. We utilized an erythroid cell line (HCD-57), a myeloid cell line stably expressing the EPO-R (Ba/F3-EPO-R), fetal liver cells isolated from E13.5 mice as well as splenocytes isolated from Phenylhydrazine (PHZ)-primed adult mice. Fetal liver cells permit the study of normal erythropoiesis in a fetal setting whereas the PHZ-primed erythroblasts permit analysis of stress erythropoiesis in adult mice. We harvested cells at 1, 8, 12 and 24 hr after EPO stimulation which correspond to immediate early gene induction (1 hr), S phase entry (8 hr) and G2/M (24 hr) time points. RNA was prepared and hybridized to the Affymetrix U74A mouse chip. Data was analyzed and only those genes with statistical significance (p < 0.05) were considered for further characterization. Analysis of the 1 hr time points has revealed that six genes are co-regulated by EPO in all four cellular environments. Included within this co-hort are the Suppressor of Cytokine Signaling genes (Cis, SOCS-1 and SOCS-3) and Myc, as well as two novel genes. We compared our datasets with other published analyses. The Williams laboratory has identified an Interferon-Stimulated Gene “ISG” data set corresponding to genes induced by Type I or Type II Interferon’s. We queried our PHZ-primed erythroblast data set against the Williams ISG database. Of the 305 human genes in the ISG database, 218 are expressed on the Affymetrix chip. We searched our dataset for genes that are induced 1.5-fold or greater at 2 of 4, 3 of 4 or 4 of 4 time points. Thirty-four genes are also stimulated by EPO in PHZ-primed erythroblasts including classical IFN-regulated genes such as Interferon-regulator factor-1 (IRF-1), Interferon-stimulated gene-15 (ISG-15), Interferon-induced transmembrane protein 3-like (IFITM-3l), Protein Kinase R (PKR) and Signal Transducer and Activator of Transcription-1 (STAT1). We have previously demonstrated that STAT1 is a negative regulator of murine erythropoiesis utilizing STAT1-deficient mice. We also analyzed immediate early gene regulation in fetal liver cells and PHZ-primed erythroblasts isolated from STAT1-deficient mice stimulated with EPO for 1 hr. These data were compared with the relevant wild type data sets. EPO stimulates the induction of the ubiquitin-like protein, ISG-15 in both wild type and STAT1−/− erythroblasts. Several signaling proteins have been shown to be covalently modified by ISG-15 including STAT1. ISG-15 is removed from ISGylated products by the deubiquitinating enzyme, Ubp43. EPO stimulates a rapid accumulation of Ubp43 in wild type cells, however, EPO fails to induce Ubp43 mRNA in STAT1-deficient fetal liver and PHZ-primed erythroblasts. Experiments are underway to confirm that the mechanism by which STAT1 exerts negative regulation of erythropoiesis is via upregulation of the deubiquitinating enzyme, Ubp43.

Download Full-text

A Growth Model for Multilevel Ordinal Data

Journal of Educational and Behavioral Statistics ◽

10.3102/10769986030004369 ◽

2005 ◽

Vol 30 (4) ◽

pp. 369-396 ◽

Cited By ~ 8

Author(s):

Eisuke Segawa

Keyword(s):

Latent Variable ◽

Ordinal Data ◽

Linear Models ◽

Growth Models ◽

Simulated Data ◽

Real Data ◽

Analytic Structure ◽

Data Sets ◽

Data Set ◽

Time Points

Multi-indicator growth models were formulated as special three-level hierarchical generalized linear models to analyze growth of a trait latent variable measured by ordinal items. Items are nested within a time-point, and time-points are nested within subject. These models are special because they include factor analytic structure. This model can analyze not only data with item- and time-level missing observations, but also data with time points freely specified over subjects. Furthermore, features useful for longitudinal analyses, “autoregressive error degree one” structure for the trait residuals and estimated time-scores, were included. The approach is Bayesian with Markov Chain and Monte Carlo, and the model is implemented in WinBUGS. They are illustrated with two simulated data sets and one real data set with planned missing items within a scale.

Download Full-text

A Consensus Time for Performing Quality Control of 225Ac-Labeled Radiopharmaceuticals

10.21203/rs.3.rs-39342/v1 ◽

2020 ◽

Author(s):

James M. Kelly ◽

Alejandro Amor-Coarasa ◽

Elizabeth Sweeney ◽

John Babich

Keyword(s):

Quality Control ◽

Early Time ◽

Average Error ◽

Secular Equilibrium ◽

Data Set ◽

Testing Procedures ◽

Time Points ◽

Consensus Time ◽

Layer Chromatography ◽

Time Point

Abstract Background: As 225Ac-labeled radiopharmaceuticals continue to show promise as targeted alpha therapeutics, there is a growing need to standardize quality control (QC) testing procedures. The determination of radiochemical purity (RCP) is an essential QC test. A significant obstacle to RCP testing is the disruption of the secular equilibrium between actinium-225 and its daughter radionuclides during labeling and analysis. In order to accelerate translation of actinium-225 targeted alpha therapy, we aimed to determine the earliest time point at which the RCP of an 225Ac-labeled radiopharmaceutical can be accurately calculated.Results: Six ligands were conjugated to macrocyclic metal chelators and labeled with actinium-225 under conditions designed to generate diverse incorporation yields. RCP was determined by radio thin layer chromatography (radioTLC) followed by exposure of the TLC plate on a phosphor screen either 0.5, 2, 3.5, 5, 6.5, or 26 h after the plate was developed. The dataset was used to create models for predicting the true RCP using pre-equilibrium measurements at early time points. The 585 TLC measurements span RCP values of 1.8% to 99.5%. The statistical model created from these data predicted an independent data set with high accuracy. Predictions made at 0.5 h are more uncertain than predictions made at later time points. This is primarily due to the decay of bismuth-213. At 2 h the mean average error is < 3%. A measurement of RCP > 90% at this time point predicts a true RCP > 97%.Conclusions: RCP of 225Ac-labeled radiopharmaceuticals can be quantified with acceptable accuracy at least 2 h after radioTLC. This time point best balances the need to accurately quantify RCP with the need to safely release the batch as quickly as possible.

Download Full-text

To what extent do medication experts agree on diagnoses derived from psychotropic medication prescribed for children and adolescents in a state Medicare database?

Developmental Child Welfare ◽

10.1177/25161032211019045 ◽

2021 ◽

pp. 251610322110190

Author(s):

Conor O’Brien ◽

John T. Rapp

Keyword(s):

Attention Deficit Hyperactivity Disorder ◽

Excellent Agreement ◽

Psychotropic Medication ◽

Psychotropic Medications ◽

Statistical Analyses ◽

Poor Agreement ◽

Data Set ◽

Time Points ◽

Hyperactivity Disorder ◽

Medicare Database

This study evaluated the extent to which psychotropic medication experts agreed on psychiatric/behavior diagnoses derived from 30 individuals’ psychotropic medication regimens. Three medication experts reviewed the medication regimens and inferred one or more diagnoses based on the medication listed. Thereafter, we used kappa statistical analyses and category-by-category analyses to evaluate agreement of diagnoses (a) across all three reviewers for two time points (separated by 8 to 14 months) covered by each data set, (b) across each pairing of reviewers at the two time points, and (c) within each reviewer across both time points. Between-reviewer kappa statistical analyses of first- and last-regimen data yielded only five instances with excellent agreement and 13 instances of poor agreement. All remaining instances indicated various levels of disagreement. Similarly, within-expert kappa statistical analyses showed two instances with excellent agreement, four instances with poor agreement, and the remaining instances with various levels of disagreement. Overall, the highest kappa values were attached to low-count regimens, while most scores, regardless of medication count, were low and indicated potential disagreement. The category-by-category analyses yielded similar results. A secondary, conditional analysis revealed higher agreements between and within reviewers when medication regimens contained psychotropic medications typically prescribed to individuals diagnosed with Attention Deficit Hyperactivity Disorder.

Download Full-text

Granger-Causal Testing for Irregularly Sampled Time Series with Application to Nitrogen Signaling in Arabidopsis

10.1101/2020.06.15.152819 ◽

2020 ◽

Author(s):

Sachin Heerah ◽

Roberto Molinari ◽

Stéphane Guerrier ◽

Amy Marshall-Colon

Keyword(s):

Time Series ◽

Arabidopsis Thaliana ◽

Time Series Data ◽

Series Data ◽

Causal Relationships ◽

Long Distance ◽

Data Set ◽

Time Points ◽

Link Type ◽

Causal Genes

AbstractMotivationIdentification of system-wide causal relationships can contribute to our understanding of long-distance, intercellular signaling in biological organisms. Dynamic transcriptome analysis holds great potential to uncover coordinated biological processes between organs. However, many existing dynamic transcriptome studies are characterized by sparse and often unevenly spaced time points that make the identification of causal relationships across organs analytically challenging. Application of existing statistical models, designed for regular time series with abundant time points, to sparse data may fail to reveal biologically significant, causal relationships. With increasing research interest in biological time series data, there is a need for new statistical methods that are able to determine causality within and between time series data sets. Here, a statistical framework was developed to identify (Granger) causal gene-gene relationships of unevenly spaced, multivariate time series data from two different tissues of Arabidopsis thaliana in response to a nitrogen signal.ResultsThis work delivers a statistical approach for modelling irregularly sampled bivariate signals which embeds functions from the domain of engineering that allow to adapt the model’s dependence structure to the specific sampling time. Using Maximum-Likelihood to estimate the parameters of this model for each bivariate time series, it is then possible to use bootstrap procedures for small samples (or asymptotics for large samples) in order to test for Granger-Causality. When applied to the Arabidopsis thaliana data, the proposed approach produced 3,078 significant interactions, in which 2,012 interactions have root causal genes and 1,066 interactions have shoot causal genes. Many of the predicted causal and target genes are known players in local and long-distance nitrogen signaling, including genes encoding transcription factors, hormones, and signaling peptides. Of the 1,007 total causal genes (either organ), 384 are either known or predicted mobile transcripts, suggesting that the identified causal genes may be directly involved in long-distance nitrogen signaling through intercellular interactions. The model predictions and subsequent network analysis identified nitrogen-responsive genes that can be further tested for their specific roles in long-distance nitrogen signaling.AvailabilityThe method was developed with the R statistical software and is made available thorugh the R package “irg” hosted on the GitHub repository https://github.com/SMAC-Group/irg. A sample data set is made available as an example to apply the method and the complete Arabidopsis thaliana data can be found at: https://www.ncbi.nlm.nih.gov/geo/query/[email protected]

Download Full-text

Sentiment Analysis of Comments of American Birders during Two Waves of the COVID-19 Pandemic Reveal More Negative Sentiments in the Context of Birding

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph182413142 ◽

2021 ◽

Vol 18 (24) ◽

pp. 13142

Author(s):

Christoph Randler ◽

Nadine Kalb ◽

Piotr Tryjanowski

Keyword(s):

Mental Health ◽

Sentiment Analysis ◽

Human Nature ◽

Positive Impact ◽

Health Benefit ◽

Data Set ◽

Time Points ◽

One Year ◽

Second Wave

Human–nature relationships are an important aspect of leisure research. Previous studies also reported that nature-related activities have a health benefit. In this study, we surveyed US-American birdwatchers at two time points during the COVID pandemic (independent samples). During the beginning of the COVID pandemic in spring 2020, we analyzed their comments with an AI sentiment analysis. Approximately one year later (winter 2020/21), during the second wave, the study was repeated, and a second data set was analyzed. Here we show that during the ongoing pandemic, the sentiments became more negative. This is an important result because it shows that despite the positive impact of nature on mental health, the sentiments become more negative in the enduring pandemic.

Download Full-text