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PUPAID is a workflow written in R + ImageJ languages which is dedicated to the semi-automated processing and analysis of multi-channel immunofluorescence data. The workflow is designed to extract fluorescence signals within automatically-segmented cells, defined here as Areas of Interest (AOI), on whole multi-layer slides (or eventually cropped sections of them), defined here as Regions of Interest (ROI), in a simple and understandable yet thorough manner. The included (but facultative) R Shiny-based interactive application makes PUPAID also suitable for scientists who are not fluent with R programming. Furthermore, we show that PUPAID identifies significantly more cells, especially in high-density regions, as compared to already published state-of-the-art methods such as StarDist or Cellpose. For extended possibilities and downstream compatibility, single cell information is exported as FCS files (the standardized file format for single cell-based cytometry data) in order to be openable using any third-party cytometry analysis software or any analysis workflow which takes FCS files as input.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11412663 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308970 | PLOS |
Abdom Radiol (NY)
September 2025
Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
Objectives: The escalating global incidence of obesity, cardiometabolic disease and sarcopenia necessitates reliable body composition measurement tools. MRI-based assessment is the gold standard, with utility in both clinical and drug trial settings. This study aims to validate a new automated volumetric MRI method by comparing with manual ground truth, prior volumetric measurements, and against a new method for semi-automated single-slice area measurements.
View Article and Find Full Text PDFPLOS Digit Health
September 2025
Laerdal Medical AS, Stavanger, Norway.
Accurate observations at birth and during newborn resuscitation are fundamental for quality improvement initiatives and research. However, manual data collection methods often lack consistency and objectivity, are not scalable, and may raise privacy concerns. The NewbornTime project aims to develop an AI system that generates accurate timelines from birth and newborn resuscitation events by automated video recording and processing, providing a source of objective and consistent data.
View Article and Find Full Text PDFMed Phys
September 2025
Imaging Program, Lawson Research Institute, London, Canada.
Background: The gastrointestinal (GI) microbiota, composed of diverse microbial communities, is essential for physiological processes, including immune modulation. Strains such as Escherichia coli Nissle 1917 support gut health by reducing inflammation and resisting pathogens. Microbial therapies using such strains may restore GI balance and offer alternatives to antibiotics, whose overuse contributes to antibiotic resistance.
View Article and Find Full Text PDFStud Health Technol Inform
September 2025
Department of Computer Science, Kempten University of Applied Sciences, Kempten, Germany.
Introduction: Manual ICD-10 coding of German clinical texts is time-consuming and error-prone. This project aims to develop a semi-automated pipeline for efficient coding of unstructured medical documentation.
State Of The Art: Existing approaches often rely on fine-tuned language models that require large datasets and perform poorly on rare codes, particularly in low-resource languages such as German.
Fungal Genet Biol
August 2025
Key Laboratory of Cell Proliferation and Regulation Biology, Ministry of Education, College of Life Sciences, Beijing Normal University, Beijing 100875, PR China. Electronic address:
Accurate quantification of yeast sporulation efficiency is essential for genetic studies, but manual counting remains time-consuming and susceptible to subjective bias. Although deep learning tools like cellpose provide automated solutions, there exists a compelling need for alternative approaches that enable the quantification of spores. Our methodology employs ilastik's texture-feature optimization to reliably segment sporulating mother cells, intentionally avoiding explicit tetrad discrimination to ensure robustness across diverse spore morphologies.
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