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http://dx.doi.org/10.1126/science.1218031 | DOI Listing |
Background: To report clinical outcomes from a single-center cohort undergoing PAUL® Glaucoma Implant (PGI) surgery for secondary glaucoma after vitreoretinal surgery (VR).
Methods: Retrospective review of patients undergoing PGI surgery at the University Eye Hospital Bonn, Germany, from 04/2021 to 05/2023.
Results: 33 eyes of 33 patients were included.
Surg Oncol
January 2025
Department of Digestive Surgery and Transplantation, Lille University Hospital, Lille, France. Electronic address:
J Clin Med
January 2025
Eye Clinic, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, 25121 Brescia, Italy.
This study aims to evaluate the repeatability of the Pentacam HR, comparing two different measurement modes (50-cornea fine and 25-3D scan) in patients affected by keratoconus. Multicenter retrospective study, conducted at Eye Clinic of the ASST-Spedali Civili-University of Brescia, Italy, and St. Paul's Eye Unit, Royal Liverpool University Hospital, United Kingdom.
View Article and Find Full Text PDFBreast
January 2025
Medical Genetics Center (MGZ), Bayerstr. 3-5, 80335, Munich, Germany; NASGE, Nationale Allianz für seltene genetische Erkrankungen, Germany; Department of Medicine IV, Klinikum der Universität, Ludwig-Maximilians-Universität, Ziemssenstr. 5, 80336, Munich, Germany. Electronic address:
As multigene panel testing is becoming routine in clinical care, there are recommendations at national and international level, as to which genes should be analyzed in the context of a hereditary breast and ovarian cancer (HBOC). However, the individual composition of gene panels offered by testing laboratories vary, resulting in a different variant diagnostic rate. Therefore, we performed a retrospective NGS dataset analysis of suspected HBOC patients who had been tested at different German diagnostic laboratories that are part of the NASGE network.
View Article and Find Full Text PDFMed Phys
January 2025
Department of Radiation Oncology, Duke University, North Carolina, USA.
Background: The electronic compensation (ECOMP) technique for breast radiation therapy provides excellent dose conformity and homogeneity. However, the manual fluence painting process presents a challenge for efficient clinical operation.
Purpose: To facilitate the clinical treatment planning automation of breast radiation therapy, we utilized reinforcement learning (RL) to develop an auto-planning tool that iteratively edits the fluence maps under the guidance of clinically relevant objectives.
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