Animal models that have been used to examine the regenerative capacity of cell-seeded scaffolds in a urinary bladder augmentation model have ultimately translated poorly in the clinical setting. This may be due to a number of factors including cell types used for regeneration and anatomical/physiological differences between lower primate species and their human counterparts. We postulated that mesenchymal stem cells (MSCs) could provide a cell source for partial bladder regeneration in a newly described nonhuman primate bladder (baboon) augmentation model. Cell-sorted CD105(+) /CD73(+) /CD34(-) /CD45(-) baboon MSCs transduced with green fluorescent protein (GFP) were seeded onto small intestinal submucosa (SIS) scaffolds. Baboons underwent an approximate 40%-50% cystectomy followed by augmentation cystoplasty with the aforementioned scaffolds or controls and finally enveloped with omentum. Bladders from sham, unseeded SIS, and MSC/SIS scaffolds were subjected to trichrome, H&E, and immunofluorescent staining 10 weeks postaugmentation. Immunofluorescence staining for muscle markers combined with an anti-GFP antibody revealed that >90% of the cells were GFP(+) /muscle marker(+) and >70% were GFP(+) /Ki-67(+) demonstrating grafted cells were present and actively proliferating within the grafted region. Trichrome staining of MSC/SIS-augmented bladders exhibited typical bladder architecture and quantitative morphometry analyses revealed an approximate 32% and 52% muscle to collagen ratio in unseeded versus seeded animals, respectively. H&E staining revealed a lack of infiltration of inflammatory cells in grafted animals and in corresponding kidneys and ureters. Simple cystometry indicated recovery between 28% and 40% of native bladder capacity. Data demonstrate MSC/SIS composites support regeneration of bladder tissue and validate this new bladder augmentation model.
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http://dx.doi.org/10.1002/stem.568 | DOI Listing |
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
J Biol Rhythms
January 2025
Shiu Chien-Gene Lay Department of Bioengineering, University of California, San Diego, La Jolla, California.
The nature of biological research is changing, driven by the emergence of big data, and new computational models to parse out the information therein. Traditional methods remain the core of biological research but are increasingly either augmented or sometimes replaced by emerging data science tools. This presents a profound opportunity for those circadian researchers interested in incorporating big data and related analyses into their plans.
View Article and Find Full Text PDFInt J Gynecol Cancer
January 2025
Institute of Image-Guided Surgery, IHU Strasbourg, France; University of Strasbourg, ICube, Laboratory of Engineering, Computer Science and Imaging, Department of Robotics, Imaging, Teledetection and Healthcare Technologies, CNRS, UMR, Strasbourg, France.
Objective: Evaluation of prognostic factors is crucial in patients with endometrial cancer for optimal treatment planning and prognosis assessment. This study proposes a deep learning pipeline for tumor and uterus segmentation from magnetic resonance imaging (MRI) images to predict deep myometrial invasion and cervical stroma invasion and thus assist clinicians in pre-operative workups.
Methods: Two experts consensually reviewed the MRIs and assessed myometrial invasion and cervical stromal invasion as per the International Federation of Gynecology and Obstetrics staging classification, to compare the diagnostic performance of the model with the radiologic consensus.
ACS Electrochem
January 2025
Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, California 90095, United States.
In electrochemical analysis, mechanism assignment is fundamental to understanding the chemistry of a system. The detection and classification of electrochemical mechanisms in cyclic voltammetry set the foundation for subsequent quantitative evaluation and practical application, but are often based on relatively subjective visual analyses. Deep-learning (DL) techniques provide an alternative, automated means that can support experimentalists in mechanism assignment.
View Article and Find Full Text PDFInt J Clin Health Psychol
January 2025
Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany.
Fear extinction is the foundation of exposure therapy for anxiety and phobias. However, the stability of extinction memory diminishes over time, coinciding with fear recovery. To augment long-term extinction retention, the temporal distribution of extinction learning sessions is critical.
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