Informatics

Clustermarket

The world-leading equipment management system for scientific research laboratories. Clustermarket is bringing lab management into the digital age by providing lab managers and scientists with an intuitive system for instrument and user management, reporting and analysis, maintenance and service management, lab communication, and much more. 

Clustermarket - the intuitive lab management system

Technology Networks

In this episode we speak with Trish Meek, Senior Director, Product Strategy and Marketing, Digital Science at Thermo Fisher Scientific about why data is so important to organizations who are wanting to improve their instrument usage and uptime and what the wider benefits of instrument and system connectivity really are. Learn more here: https://bit.ly/3oHrOva

Exploring Data Management in the Lab of the Future - Realizing Lab Productivity and Value From Your Data

In this episode, we speak with Bill Weiser, Senior Director, Global Quality Systems, Thermo Fisher Pharma Services, about how embracing a Pharma 4.0 strategy in contract developing as well as how it helps in manufacturing operations. Learn more about Pharma 4.0 here: https://technet.works/3rw6yKL

Exploring Data Management in the Lab of the Future - Pharma 4.0 Strategies for Contract Development and Manufacturing

In this episode we speak with Richard Milne, Vice President and General Manager, Digital Science about connecting and enhancing the scientific ecosystem where we will discover what the term scientific ecosystem means, and find out how technology is being used to address scientific challenges in new and exciting ways. Learn more: https://technet.works/3saTZDC

Exploring Data Management in the Lab of the Future - Connecting and Enhancing the Scientific Ecosystem

In the first episode of the series, Greg Clark, Senior Software Product Manager, Electron Microscopy at Thermo Fisher Scientific, speaks with us helping us to understand the main challenges that are faced by scientists in managing and interpreting imaging data today and just how important AI and Machine learning are to the future of imaging. Learn more: https://technet.works/3ucEFZQ

Exploring Data Management in the Lab of the Future - The Data Behind Imaging and Advancing Scientific Understanding

SCIEX

SCIEX OS software 2.2 is mass spectrometer software for the modern laboratory. Its remarkable quantitative usability, efficiency and integrity will transform and streamline your LC-MS/MS workflows. The powerful algorithms and automation at its core will enable you to make informed decisions quickly.

SCIEX OS Demonstration

Aging (Aging-US)

Copy: Aging-US published this research paper as the cover for Volume 13, Issue 24, entitled, “Age-dependent expression changes of circadian system-related genes reveal a potentially conserved link to aging” by researchers from Bioinformatics/High Throughput Analysis, Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, Jena, Germany; FLI Leibniz Institute for Age Research, Jena, Germany; Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany; German Center for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Germany; European Virus Bioinformatics Center (EVBC), Jena, Germany.

Abstract
The circadian clock system influences the biology of life by establishing circadian rhythms in organisms, tissues, and cells, thus regulating essential biological processes based on the day/night cycle. Circadian rhythms change over a lifetime due to maturation and aging, and disturbances in the control of the circadian system are associated with several age-related pathologies.

However, the impact of chronobiology and the circadian system on healthy organ and tissue aging remains largely unknown. Whether aging-related changes of the circadian system’s regulation follow a conserved pattern across different species and tissues, hence representing a common driving force of aging, is unclear.

Based on a cross-sectional transcriptome analysis covering 329 RNA-Seq libraries, we provide indications that the circadian system is subjected to aging-related gene alterations shared between evolutionarily distinct species, such as Homo sapiens, Mus musculus, Danio rerio, and Nothobranchius furzeri. We discovered differentially expressed genes by comparing tissue-specific transcriptional profiles of mature, aged, and old-age individuals and report on six genes (per2, dec2, cirp, klf10, nfil3, and dbp) of the circadian system, which show conserved aging-related expression patterns in four organs of the species examined. Our results illustrate how the circadian system and aging might influence each other in various tissues over a long lifespan and conceptually complement previous studies tracking short-term diurnal and nocturnal gene expression oscillations.

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DOI - https://doi.org/10.18632/aging.203788

Full Text - https://www.aging-us.com/article/203788/text

Correspondence to: Emanuel Barth email: emanuel.barth@uni-jena.de

Keywords: aging, circadian clock system, circadian rhythm, inter-species comparison, longevity, RNA-Seq

About Aging-US

Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways.

Please visit our website at http://www.Aging-US.com​​ or connect with us on:

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YouTube - https://www.youtube.com/agingus​
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Aging-US is published by Impact Journals, LLC please visit http://www.ImpactJournals.com​​ or connect with @ImpactJrnls

Media Contact
18009220957
MEDIA@IMPACTJOURNALS.COM

The Circadian System and Aging

Oncotarget

Oncotarget published this trending research paper in Volume 12, Issue 25, entitled, "Radiomics in predicting recurrence for patients with locally advanced breast cancer using quantitative ultrasound" by researchers from the Department of Radiation Oncology, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Canada; Physical Sciences, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Medicine, University of Toronto, Toronto, Canada; Department of Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Surgery, University of Toronto, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada; Department of Electrical Engineering and Computer Sciences, Lassonde School of Engineering, York University, Toronto, Canada; Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada; Department of Medical Imaging, University of Toronto, Toronto, Canada; Department of Physics, Ryerson University, Toronto, Canada.

Abstract:
Background: The purpose of the study was to investigate the role of pre-treatment quantitative ultrasound (QUS)-radiomics in predicting recurrence for patients with locally advanced breast cancer (LABC).

Materials and Methods: A prospective study was conducted in patients with LABC (n = 83). Primary tumours were scanned using a clinical ultrasound device before starting treatment. Ninety-five imaging features were extracted-spectral features, texture, and texture-derivatives. Patients were determined to have recurrence or no recurrence based on clinical outcomes. Machine learning classifiers with k-nearest neighbour (KNN) and support vector machine (SVM) were evaluated for model development using a maximum of 3 features and leave-one-out cross-validation.

Results: With a median follow up of 69 months (range 7–118 months), 28 patients had disease recurrence (local or distant). The best classification results were obtained using an SVM classifier with a sensitivity, specificity, accuracy and area under curve of 71%, 87%, 82%, and 0.76, respectively. Using the SVM model for the predicted non-recurrence and recurrence groups, the estimated 5-year recurrence-free survival was 83% and 54% (p = 0.003), and the predicted 5-year overall survival was 85% and 74% (p = 0.083), respectively.

Conclusions: A QUS-radiomics model using higher-order texture derivatives can identify patients with LABC at higher risk of disease recurrence before starting treatment.

Press release - https://www.oncotarget.com/news/pr/radiomics-for-patients-with-breast-cancer-using-ultrasound/

Sign up for free Altmetric alerts about this article - https://oncotarget.altmetric.com/details/email_updates?id=10.18632%2Foncotarget.28139

DOI - https://doi.org/10.18632/oncotarget.28139

Full text - https://www.oncotarget.com/article/28139/text/

Correspondence to - Gregory J. Czarnota - gregory.czarnota@sunnybrook.ca

Keywords - radiomics, breast cancer, quantitative ultrasound, recurrence, machine learning

About Oncotarget

Oncotarget is a bi-weekly, peer-reviewed, open access biomedical journal covering research on all aspects of oncology.

To learn more about Oncotarget, please visit https://www.oncotarget.com or connect with:

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LinkedIn - https://www.linkedin.com/company/oncotarget
Pinterest - https://www.pinterest.com/oncotarget/
Reddit - https://www.reddit.com/user/Oncotarget/

Oncotarget is published by Impact Journals, LLC please visit https://www.ImpactJournals.com or connect with @ImpactJrnls

Media Contact
MEDIA@IMPACTJOURNALS.COM
18009220957

Trending With Impact: Pre-Treatment Ultrasound Used to Predict Breast Cancer Recurrence

PerkinElmer Applied

Whether you're looking to expand and enhance your lab's capabilities or open a new lab, jumpstart those efforts quickly and easily with the LABStartUp Program: https://bit.ly/3y7qvZm. Once accepted by PerkinElmer, you'll enjoy match-funding, up to 30% off consumables, special financing options and a free poster.

Informatics

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