Publications by authors named "Ilona Binenbaum"

A chronic metabolic illness, type 2 diabetes (T2D) is a polygenic and multifactorial complicated disease. With an estimated 463 million persons aged 20 to 79 having diabetes, the number is expected to rise to 700 million by 2045, creating a significant worldwide health burden. Polygenic variants of diabetes are influenced by environmental variables.

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Background: We investigated the impact of the indoor mass gathering of young people during the Patras Carnival in Greece on the course of the COVID-19 pandemic and the influenza A epidemic. Materials and Methods: For influenza A, we tested 331 subjects with high fever (>38 °C), who arrived at five separate private laboratories over a two-week period after the carnival, via rapid test. One hundred and eighty-eight of them were young adults (17−35 years old), all unvaccinated against influenza A but all immunized against SARS-CoV-2, either through vaccination or previous infection.

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Background: Familial adenomatous polyposis is an inherited genetic disease, characterized by colorectal polyps. It is caused by inactivating mutations in the Adenomatous polyposis coli (Apc) gene. Mice carrying a nonsense mutation in the Apc gene at R850, which is designated Apc (Multiple intestinal neoplasia), develop intestinal adenomas.

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HER3 is highly expressed in luminal breast cancer subtypes. Its activation by NRG1 promotes activation of AKT and ERK1/2, contributing to tumour progression and therapy resistance. HER3-targeting agents that block this activation, are currently under phase 1/2 clinical studies, and although they have shown favorable tolerability, their activity as a single agent has proven to be limited.

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Uncontrolled proliferation and altered metabolic reprogramming are hallmarks of cancer. Active glycolysis and glutaminolysis are characteristic features of these hallmarks and required for tumorigenesis. A fine balance between cancer metabolism and autophagy is a prerequisite of homeostasis within cancer cells.

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Article Synopsis
  • The Collaborative Cross (CC) mouse population helps scientists study how genes affect things like obesity, which is influenced by both genes and the environment.
  • Researchers created a new automated process to look at these genetic factors and how they relate to diet-induced obesity in different mice.
  • They found that male and female mice respond differently to a high-fat diet, showing that genetics and obesity can be influenced in distinct ways based on sex, which needs more study.
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Cellular senescence is a natural condition of irreversible cell cycle arrest and apoptotic resistance that occurs in cells exposed to various stress factors, such as replicative stress or overexpression of oncogenes. Unraveling the complex regulation of senescence in cells is essential to strengthen senescence-related therapeutic approaches in cancer, as cellular senescence plays a dual role in tumorigenesis, having both anti- and pro-tumorigenic effects. In our study we created a model of replicative cellular senescence, based on transcriptomic data, including an extra intermediate time-point prior to cells entering senescence, to elucidate the interplay of networks governing cellular senescence with networks involved in tumorigenesis.

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The Collaborative Cross (CC) mouse resource is a next-generation mouse genetic reference population (GRP) designed for high-resolution mapping of quantitative trait loci (QTL) of large effect affecting complex traits during health and disease. The CC resource consists of a set of 72 recombinant inbred lines (RILs) generated by reciprocal crossing of five classical and three wild-derived mouse founder strains. Complex traits are controlled by variations within multiple genes and environmental factors, and their mutual interactions.

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Healthcare systems around the world are facing incredible challenges due to the ageing population and the related disability, and the increasing use of technologies and citizen's expectations. Improving health outcomes while containing costs acts as a stumbling block. In this context, Big Data can help healthcare providers meet these goals in unprecedented ways.

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The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development.

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