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Background: Damage-associated molecular patterns (DAMPs) induced by immunogenic cell death (ICD) may be useful for the immunotherapy to patients undergoing pancreatic ductal adenocarcinoma (PDAC). The aim of this study is to predict the prognosis and immunotherapy responsiveness of PDAC patients using DAMPs-related genes.

Methods: K-means analysis was used to identify the DAMPs-related subtypes of 175 PDAC cases.

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The implementation and side effect management of immune checkpoint inhibitors in gynecologic oncology: a JAGO/NOGGO survey.

BMC Cancer

January 2025

Young Academy of Gynecologic Oncology (JAGO), Nord-Ostdeutsche Gesellschaft für Gynäkologische Onkologie (NOGGO), Berlin, Germany.

Background: The integration of immune checkpoint inhibitors (ICIs) into routine gynecologic cancer treatment requires a thorough understanding of how to manage immune-related adverse events (irAEs) to ensure patient safety. However, reports on real-world clinical experience in the management of ICIs in gynecologic oncology are very limited. The aim of this survey was to provide a real-world overview of the experiences and the current state of irAE management of ICIs in Germany, Switzerland, and Austria.

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Background: Over the past twenty years, the post-cancer rehabilitation has been developed, usually in a hospital setting. Although this allows better care organization and improved security, it is perceived as stressful and restrictive by the "cancer survivor". Therefore, the transfer of benefits to everyday life is more difficult, or even uncertain.

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Objectives: This study examines the associations between race and ethnicity and receipt of Baby Friendly Hospital Initiative (BFHI) key clinical practices that support breastfeeding in US hospitals.

Methods: National data from 2016 to 2019 CDC PRAMS were analyzed. Our sample included 60,395 mothers who initiated breastfeeding with healthy, term newborns.

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Triaging mammography with artificial intelligence: an implementation study.

Breast Cancer Res Treat

January 2025

Google Health, 1600 Amphitheatre Pkwy, Mountain View, CA, 94043, USA.

Purpose: Many breast centers are unable to provide immediate results at the time of screening mammography which results in delayed patient care. Implementing artificial intelligence (AI) could identify patients who may have breast cancer and accelerate the time to diagnostic imaging and biopsy diagnosis.

Methods: In this prospective randomized, unblinded, controlled implementation study we enrolled 1000 screening participants between March 2021 and May 2022.

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