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Summary: Skyline is a Windows application for targeted mass spectrometry method creation and quantitative data analysis. Like most graphical user interface (GUI) tools, it has a complex user interface with many ways for users to edit their files which makes the task of logging user actions challenging and is the reason why audit logging of every change is not common in GUI tools. We present an object comparison-based approach to audit logging for Skyline that is extensible to other GUI tools. The new audit logging system keeps track of all document modifications made through the GUI or the command line and displays them in an interactive grid. The audit log can also be uploaded and viewed in Panorama, a web repository for Skyline documents that can be configured to only accept documents with a valid audit log, based on embedded hashes to protect log integrity. This makes workflows involving Skyline and Panorama more reproducible.
Availability And Implementation: Skyline is freely available at https://skyline.ms.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btaa547 | DOI Listing |
Pediatr Res
September 2025
Department of Neonatal Surgery, Children's Hospital of Nanjing Medical University, Nanjing, China.
Background: Children hospitalized for surgery face malnutrition risks. This study assessed nutritional risk and status in hospitalized neonatal surgical patients using a modified Screening Tool for the Assessment of Malnutrition in Pediatrics (STAMP) combined with anthropometry.
Methods: A retrospective analysis of neonatal surgical patients from December 2020 to October 2024 was conducted at a children's hospital, utilizing the modified STAMP and anthropometric measurements.
IEEE J Biomed Health Inform
August 2025
Breast cancer is the leading cause of cancer death in women worldwide, emphasizing the need for prevention and early detection. Mammography screening plays a crucial role in secondary prevention, but large datasets of referred mammograms from hospital databases are hard to access due to privacy concerns, and publicly available datasets are often unreliable and unbalanced. We propose a novel workflow using a statistical generative model based on generative adversarial networks to generate high-resolution synthetic mammograms.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
August 2025
Department of Ophthalmology, Eye Institute, Tianjin Medical University Eye Hospital, Tianjin Medical University Eye Institute, Tianjin, China.
Purpose: Internal limiting membrane (ILM) peeling in macular hole (MH) surgery is critical but challenging, and current practices lack standardized tools for quantifying and visualizing optimal peeling dimensions.This study aimed to develop a machine learning framework to recommend surgeon-specific ILM peeling radius during macular hole surgery, integrating predictive modeling with schematic visualization to guide operative planning.
Methods: This retrospective study analyzed data from 95 patients with idiopathic MH who underwent vitrectomy with ILM peeling.
BMC Bioinformatics
August 2025
Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
Background: The rapid expansion of next-generation sequencing (NGS) technologies has generated vast amounts of genomic data, creating a growing demand for secure, scalable, and accessible tools to support variant interpretation. However, many existing solutions are command-line based, rely on cloud or server infrastructures that may pose data privacy risks, lack flexibility in supporting both VCF, CSV and TSV formats, or struggle to handle the scale and complexity of modern genomic datasets. There is a clear need for a user-friendly, locally operated application capable of efficiently processing annotated variant data for large-scale cohort level analysis.
View Article and Find Full Text PDFPLoS One
August 2025
Artificial Intelligence and Cognitive Load Lab, School of Computer Science, TU Dublin, Dublin, Ireland.
Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) have emerged as a transformative technology with applications spanning robotics, virtual reality, medicine, and rehabilitation. However, existing BCI frameworks face several limitations, including a lack of stage-wise flexibility essential for experimental research, steep learning curves for researchers without programming expertise, elevated costs due to reliance on proprietary software, and a lack of all-inclusive features leading to the use of multiple external tools affecting research outcomes. To address these challenges, we present PyNoetic, a modular BCI framework designed to cater to the diverse needs of BCI research.
View Article and Find Full Text PDF