19 results match your criteria: "Qatar Computing research Institute (QCRI)[Affiliation]"

Background: Advances in our understanding of the tumor microenvironment have radically changed the cancer field, highlighting the emerging need for biomarkers of an active, favorable tumor immune phenotype to aid treatment stratification and clinical prognostication. Numerous immune-related gene signatures have been defined; however, their prognostic value is often limited to one or few cancer types. Moreover, the area of non-coding RNA as biomarkers remains largely unexplored although their number and biological roles are rapidly expanding.

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Article Synopsis
  • This study explores the relationship between serum metabolites and metabolic syndrome features in Arabic individuals with obesity, comparing those with only obesity to those with obesity and metabolic syndrome.
  • Researchers found significant differences in 83 metabolites, particularly lipids like sphingomyelins, which were lower in those with metabolic syndrome, suggesting a correlation with negative health markers.
  • Key metabolic pathways associated with chronic inflammation were also identified as being expressed differently between the two groups, highlighting the complex metabolic changes linked to obesity and metabolic syndrome.
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Background: Obesity-associated dysglycemia is associated with metabolic disorders. MicroRNAs (miRNAs) are known regulators of metabolic homeostasis. We aimed to assess the relationship of circulating miRNAs with clinical features in obese Qatari individuals.

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We recently found by single-cell mass cytometry that human B cells internalize graphene oxide (GO). The functional impact of such uptake on B cells remains unexplored. Here, we disclosed the effects of GO and amino-functionalized GO (GONH) interacting with human B cells and at the protein and gene expression levels.

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Breast cancer largely dominates the global cancer burden statistics; however, there are striking disparities in mortality rates across countries. While socioeconomic factors contribute to population-based differences in mortality, they do not fully explain disparity among women of African ancestry (AA) and Arab ancestry (ArA) compared to women of European ancestry (EA). In this study, we sought to identify molecular differences that could provide insight into the biology of ancestry-associated disparities in clinical outcomes.

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Article Synopsis
  • - The study focuses on developing new strategies for diabetes treatment using human pluripotent stem cells (hPSCs) to create insulin-secreting beta cells, specifically examining a new type of pancreatic progenitors identified as PDX1/NKX6.1.
  • - Researchers differentiated three hPSC lines into these PDX1/NKX6.1 progenitors and assessed their ability to become functional beta cells through various protocols, including transcriptome analysis.
  • - The results showed that these progenitors could efficiently develop into insulin-secreting cells that respond to glucose, highlighting their potential as a novel source for beta cell therapy in diabetes treatment.
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Objectives: Poor glycemic control is associated with mortality in critical patients with diabetes. The aim of the study was to assess the predicting value of stress hyperglycemia in patients with diabetes following hospital admission for sepsis.

Design: Retrospective observational study.

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Background: An immune active cancer phenotype typified by a T helper 1 (Th-1) immune response has been associated with increased responsiveness to immunotherapy and favorable prognosis in some but not all cancer types. The reason of this differential prognostic connotation remains unknown.

Methods: To explore the contextual prognostic value of cancer immune phenotypes, we applied a multimodal pan-cancer analysis among 31 different histologies (9282 patients), encompassing immune and oncogenic transcriptomic analysis, mutational and neoantigen load and copy number variations.

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Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework.

Brief Bioinform

March 2021

Monash Biomedicine Discovery Institute, Monash University, Australia. He is also affiliated with the Monash Centre for Data Science, Faculty of Information Technology, Monash University. His research interests include bioinformatics, computational biology, machine learning, data mining, and pattern recognition.

Promoters are short consensus sequences of DNA, which are responsible for transcription activation or the repression of all genes. There are many types of promoters in bacteria with important roles in initiating gene transcription. Therefore, solving promoter-identification problems has important implications for improving the understanding of their functions.

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Amyloid fibrillation is the root cause of several neuro as well as non-neurological disorders. Understanding the molecular basis of amyloid aggregate formation is crucial for deciphering various neurodegenerative diseases. In our study, we have examined the lysozyme fibrillation process using nano-infrared spectroscopy (nanoIR).

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Background: The burden of sepsis represents a global health care problem. We aimed to assess the case fatality rate (CFR) and its predictors in subjects with sepsis admitted to a general Italian hospital from 2009 to 2016, stratified by risk score.

Methods: We performed a retrospective analysis of all sepsis-related hospitalizations after Emergency Department (ED) visit in a public Italian hospital in an 8-year period.

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Objective: Brain damage, long-term disability and death are the dreadful consequences of ischemic stroke. It causes imbalance in the biochemical constituents that distorts the brain dynamics. Understanding the sub-cellular alterations associated with the stroke will contribute to deeper molecular understanding of brain plasticity and recovery.

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An Evolutionary Bootstrapping Development Approach for a Mental Health Conversational Agent.

Stud Health Technol Inform

July 2019

Global Health and Population, Harvard T H Chan School of Public Health, Harvard University, United States of America.

Conversational agents are being used to help in the screening, assessment, diagnosis, and treatment of common mental health disorders. In this paper, we propose a bootstrapping approach for the development of a digital mental health conversational agent (i.e.

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Developing a Digital Mental Health Platform for the Arab World: From Research to Action.

Stud Health Technol Inform

July 2019

Global Health and Population, Harvard T H Chan School of Public Health, Harvard University, United States of America.

Individuals within the Arab world rarely access mental health services. One of the major reasons for this relates to the stigma associated with mental disorders. According to the World Health Organization (WHO), untreated and undiagnosed individuals living with moderate to severe mental health disorders are more likely to die 10-20 years earlier than the estimated life expectancy of the general population.

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Chromosomal translocations that generate in-frame oncogenic gene fusions are notable examples of the success of targeted cancer therapies. We have previously described gene fusions of FGFR3-TACC3 (F3-T3) in 3% of human glioblastoma cases. Subsequent studies have reported similar frequencies of F3-T3 in many other cancers, indicating that F3-T3 is a commonly occuring fusion across all tumour types.

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: Analyze cancer genomics and epigenomics data using Bioconductor packages.

F1000Res

June 2016

Department of Genetics, Ribeirao Preto Medical School, University of Sao Paulo, Ribeirao Preto, Brazil; Department of Neurosurgery, Henry Ford Hospital, Detroit, MI, USA.

Biotechnological advances in sequencing have led to an explosion of publicly available data via large international consortia such as The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap). These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The Bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data.

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Combining Human Computing and Machine Learning to Make Sense of Big (Aerial) Data for Disaster Response.

Big Data

March 2016

6 Laboratory of Geographical Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland .

Aerial imagery captured via unmanned aerial vehicles (UAVs) is playing an increasingly important role in disaster response. Unlike satellite imagery, aerial imagery can be captured and processed within hours rather than days. In addition, the spatial resolution of aerial imagery is an order of magnitude higher than the imagery produced by the most sophisticated commercial satellites today.

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TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data.

Nucleic Acids Res

May 2016

Department of Genetics Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil Center for Integrative Systems Biology - CISBi, NAP/USP, Ribeirão Preto, São Paulo, Brazil

The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers.

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Background: Inflammatory breast cancer (IBC) is the most rare and aggressive variant of breast cancer (BC); however, only a limited number of specific gene signatures with low generalization abilities are available and few reliable biomarkers are helpful to improve IBC classification into a molecularly distinct phenotype. We applied a network-based strategy to gain insight into master regulators (MRs) linked to IBC pathogenesis.

Methods: In-silico modeling and Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) on IBC/non-IBC (nIBC) gene expression data (n = 197) was employed to identify novel master regulators connected to the IBC phenotype.

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