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Background: Heart diseases are among the leading causes of death worldwide, many of which lead to pathological cardiomyocyte hypertrophy and capillary rarefaction in both patients and animal models, the quantification of which is both technically challenging and highly time-consuming. Here we developed a semiautomated pipeline for quantification of the size of cardiomyocytes and capillary density in cardiac histology, termed HeartJ, by generating macros in ImageJ, a broadly used, open-source, Java-based software.
Methods: We have used modified Gomori silver staining, which is easy to perform and digitize in high throughput, or Fluorescein-labeled lectin staining. The latter can be easily combined with other stainings, allowing additional quantitative analysis on the same section, e.g., the size of cardiomyocyte nuclei, capillary density, or single-cardiomyocyte protein expression. We validated the pipeline in a mouse model of cardiac hypertrophy induced by transverse aortic constriction, and in autopsy samples of patients with and without aortic stenosis.
Results: In both animals and humans, HeartJ-based histology quantification revealed significant hypertrophy of cardiomyocytes reflecting other parameters of hypertrophy and rarefaction of microvasculature and enabling the analysis of protein expression in individual cardiomyocytes. The analysis also revealed that murine and human cardiomyocytes had similar diameters in health and extent of hypertrophy in disease confirming the translatability of our murine cardiac hypertrophy model. HeartJ enables a rapid analysis that would not be feasible by manual methods. The pipeline has little hardware requirements and is freely available.
Conclusions: In summary, our analysis pipeline can facilitate effective and objective quantitative histological analyses in preclinical and clinical heart samples.
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http://dx.doi.org/10.1186/s12967-023-04544-2 | DOI Listing |
Front Med (Lausanne)
August 2025
Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, Türkiye.
Introduction: Accurate and timely diagnosis of central nervous system infections (CNSIs) is critical, yet current gold-standard techniques like lumbar puncture (LP) remain invasive and prone to delay. This study proposes a novel noninvasive framework integrating handcrafted radiomic features and deep learning (DL) to identify cerebrospinal fluid (CSF) alterations on magnetic resonance imaging (MRI) in patients with acute CNSI.
Methods: Fifty-two patients diagnosed with acute CNSI who underwent LP and brain MRI within 48 h of hospital admission were retrospectively analyzed alongside 52 control subjects with normal neurological findings.
Acad Radiol
September 2025
Radiology Department, Huashan Hospital, Affiliated with Fudan University, Shanghai 200040, China (S.L., D.G.); Shanghai Engineering Research Center of Intelligent Imaging for Critical Brain Diseases, Shanghai 200031, China (D.G.); Institute of Functional and Molecular Medical Imaging, Fudan Universi
Background: This study developed a deep learning model for segmenting and classifying the amygdala-hippocampus in Alzheimer's disease (AD), using a large-scale neuroimaging dataset to improve early AD detection and intervention.
Methods: We collected 1000 healthy controls (HC) and 1000 AD patients as internal training data from 15 Chinese medical centers. The independent external validation dataset was sourced from another three centers.
Stud 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.
PLoS One
August 2025
Department of Cellular and Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
The C. elegans ventral nerve cord (VNC) provides a genetically tractable model for investigating the developmental mechanisms involved in neuronal positioning and organization. The VNC of newly hatched larvae contains a set of 22 motoneurons organized into three distinct classes (DD, DA, and DB) that show consistent positioning and arrangement.
View Article and Find Full Text PDFFungal 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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