Nature Metabolism is Now Available!

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Nature Metabolism cover image

You asked, we answered: Nature Metabolism has been added to the Library’s e-journal collection! This new journal from nature.com is interested in the best research from across all fields of metabolism research, incorporating the work of basic scientists and researchers in the industry. At its core, the research published in Nature Metabolism sheds light on how cellular metabolism informs cellular function, on the physiology and homeostasis of organs and tissues, on the regulation of organismal energy homeostasis, and on the molecular pathophysiology of metabolic diseases, such as diabetes and obesity, or the treatment thereof.  Want to publish in Nature Metabolism? Visit their page just for authors.

Nature Metabolism may be found in OneSearch.

A Mid-Year Look at MSK Publications Popular on the Web in 2019

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Each month, the Synapse team looks for recently published works authored by MSK researchers and staff and adds them to Synapse, our database of MSK authors and their publications. For 2019, we have already indexed almost 2,000 publications (including journal articles, meeting abstracts, reviews, conference papers, editorials etc.) Using the Altmetric integration we have added to Synapse, we can track the online attention publications receive across the web.

The following 2019 MSK publications have received the highest amount of online attention. Combined, they were featured in 111 news stories and tweeted over 2,886 times. To view the news stories and tweets, follow the links below and click in the colored Altmetric section to the right.

For questions about Synapse or Altmetric, contact Jeanine McSweeney.

When Artificial Intelligence Meets Pathology

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In a recent Nature Medicine article, MSK’s Dr. Thomas Fuchs and his colleagues describe how they trained artificial intelligence (AI) to process pathology slides at large scale. The research was completed with the start-up Paige.AI and is reported by The Cancer Letter, Tech Crunch, Becker’s Healthcare, and 360 DX.

Dr. Thomas Fuchs. Credit: Richard DeWitt

To train deep learning systems, researchers typically need to manually curate data, a time-consuming process that has previously prevented large-scale AI applications to pathology datasets. In this study, researchers instead trained the system using only the slide-level diagnostic information already in the electronic health record. The study included 44,732 slides of prostate, skin, or axillary lymph node tissue from 15,187 patients at 800 institutions. Their calculations indicate that the model could reduce a pathologist’s workload by up to 75%, classifying cancer without sacrificing sensitivity.

To learn more about artificial intelligence, including a list of the latest AI-related publications from MSK authors, check out the Library’s new LibGuide.