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Serena Yeung
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DBDS faculty featured prominently in cover story of latest SOM Magazine: “Medicine’s AI Boom: The Stanford Impact”

Eleven of DBDS’ faculty and associated faculty were featured in the latest issue of Stanford Medicine Magazine.
The Stanford Impact” focused on a wide scope of researchers that impact the AI work done in DBDS, including Nigam Shah, Serena Yeung (pictured), Roxana Daneshjou, Tina Hernandez-Boussard and James Zou.
“If you poll the 1,200 faculty in the School of Medicine, I’d be surprised if more than 10% know about any of Stanford’s AI history,” said Shah, MBBS, PhD, professor of medicine and of biomedical data science and chief data scientist for Stanford Health Care. “A lot of people think this right now is the first AI hype cycle,” the story quoted, and went on to include how AI is defined and in which ways Stanford is taking the lead in the field.
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Min Woo Sun and Robert Tibshirani publish “Public health factors help explain cross country heterogeneity in excess death during the COVID19 pandemic” in Nature Scientific Reports

Abstract

The COVID-19 pandemic has taken a devastating toll around the world. Since January 2020, the World Health Organization estimates 14.9 million excess deaths have occurred globally. Despite this grim number quantifying the deadly impact, the underlying factors contributing to COVID-19 deaths at the population level remain unclear. Prior studies indicate that demographic factors like proportion of population older than 65 and population health explain the cross-country difference in COVID-19 deaths. However, there has not been a comprehensive analysis including variables describing government policies and COVID-19 vaccination rate. Furthermore, prior studies focus on COVID-19 death rather than excess death to assess the impact of the pandemic. Through a robust statistical modeling framework, we analyze 80 countries and show that actionable public health efforts beyond just the factors intrinsic to each country are important for explaining the cross-country heterogeneity in excess death.

Our work on COVID-19 excess death and public health factors has been published in Nature Scientific Reports: https://www.nature.com/articles/s41598-023-43407-0.

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Gina Bouchard: “The colocatome as a spatial -omic reveals shared microenvironment features between tumour-stroma assembloids and lung cancer specimens” published in BioRxiv

Gina Bouchard: Computational frameworks to quantify and compare microenvironment spatial features of in vitro patient-derived models and clinical specimens are needed. Here, we acquired and analysed multiplexed immunofluorescence images of human lung adenocarcinoma (LUAD) alongside tumour-stroma assembloids constructed with organoids and fibroblasts harvested from the leading edge (Tumour-Adjacent Fibroblasts, TAFs) or core (Tumour Core Fibroblasts, TCFs) of human LUAD.

Read more: https://www.biorxiv.org/content/10.1101/2023.09.11.557278v1

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Gevaert team: Glioblastoma research study establishes a connection between spatial cellular architecture and clinical outcomes

Exciting work in glioblastoma research spearheaded by postdoc Yuan-Ning Zheng. The Gevaert team has developed a deep learning model to predict transcriptional subtypes of glioblastoma cells from spatial transcriptomics data and histology images. Moreover, this study establishes a connection between spatial cellular architecture and clinical outcomes.

Read more here:  https://www.nature.com/articles/s41467-023-39933-0

Watch video here: https://www.youtube.com/watch?v=7JxOaLAUaaI

The team has also developed a website where pathologist can test the model:
https://gbm360.stanford.edu/

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