Boston University Medical Campus

Mission Statement

The BU CTSI’s vision is to be the strongest possible advocate for, and participant in, translational research that serves the health needs of our diverse patient populations by working with our community to create unique resources that can be integrated with and distributed to the national CTSA network.

At a Glance

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Educational Resources Posted

1

Consortium News Stories Posted

1

Events Posted

0

Opportunities Posted

230

Publications citing CTSA Program Grant

14

Users from Hub Registered
Programs

UL1 Award

KL2 Award

TL1 Award

Funded Years
2012 - 2013, 2015 - 2018, 2020
Connect with Us
Page Administrators
Helia M
Morris
Executive Director

Consortium News

Small molecules are powerful tools for understanding biological systems, and form the foundation for most therapies of human disease. The Boston University Center for Molecular Discovery (BU-CMD) has established an open-access small molecule sharing network that connects chemists who make molecules with biologists who wish to test them in various diseases. This collaborative nexus is designed to

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Boston University Medical Campus
The Boston University Clinical Translational Science Institute (BU-CTSI) is holding their 8th Annual David Seldin Memorial Translational Science Symposium: “From Server to Bedside: Advancing Health and Healthcare Through Data Science.” The day is organized around three general themes: Foundational Systems; Discovery and Prediction; and Effectiveness and Implementation. Presentations will be bring

Poster Sessions

Boston University Medical Campus

A hallmark of severe COVID-19 pneumonia is SARS-CoV-2 infection of the facultative progenitors of lung alveoli, the alveolar epithelial type 2 cells (AT2s). However, inability to access these cells from patients, particularly at early stages of disease, limits an understanding of disease inception. Here, we present an in vitro human model that simulates the initial apical infection of alveolar

Publications

Machine learning models to predict length of stay and discharge destination in complex head and neck surgery

This study develops machine learning (ML) algorithms that use preoperative-only features to predict discharge-to-nonhome-facility (DNHF) and length-of-stay (LOS) following complex head and neck surgeries. Patients undergoing laryngectomy or composite tissue excision followed by free tissue transfer

Caregiver engagement practices in National Cancer Institute Clinical Oncology Research Program settings: Implications for research to advance the field

Supportive care interventions have demonstrated benefits for both informal and/or family cancer caregivers and their patients, but uptake generally is poor. To the authors' knowledge, little is known regarding the availability of supportive care services in community oncology practices, as well as

International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries: A Retrospective Cohort Study

To perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions. Retrospective cohort study. The Consortium