Background: Advances in three-dimensional culture technologies have led to progression in systems used to model the gonadal microenvironment in vitro. Despite demonstrating basic functionality, tissue organisation is often limited. We have previously detailed a three-dimensional culture model termed the three-layer gradient system to generate rat testicular organoids in vitro. Here we extend the model to human first-trimester embryonic gonadal tissue.
Results: Testicular cell suspensions reorganised into testis-like organoids with distinct seminiferous-like cords situated within an interstitial environment after 7 days. In contrast, tissue reorganisation failed to occur when mesonephros, which promotes testicular development in vivo, was included in the tissue digest. Organoids generated from dissociated female gonad cell suspensions formed loosely organised cords after 7 days. In addition to displaying testis-specific architecture, testis-like organoids demonstrated evidence of somatic cell differentiation. Within the 3-LGS, we observed the onset of AMH expression in the cytoplasm of SOX9-positive Sertoli cells within reorganised testicular cords. Leydig cell differentiation and onset of steroidogenic capacity was also revealed in the 3-LGS through the expression of key steroidogenic enzymes StAR and CYP17A1 within the interstitial compartment. While the 3-LGS generates a somatic cell environment capable of supporting germ cell survival in ovarian organoids germ cell loss was observed in testicular organoids.
Conclusion: The 3-LGS can be used to generate organised whole gonadal organoids within 7 days. The 3-LGS brings a new opportunity to explore gonadal organogenesis and contributes to the development of more complex in vitro models in the field of developmental and regenerative medicine.
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http://dx.doi.org/10.1186/s12915-021-01149-3 | DOI Listing |
Sci Rep
January 2025
Department of Artificial Intelligence and Data Science, College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia.
In the present digital scenario, the explosion of Internet of Things (IoT) devices makes massive volumes of high-dimensional data, presenting significant data and privacy security challenges. As IoT networks enlarge, certifying sensitive data privacy while still employing data analytics authority is vital. In the period of big data, statistical learning has seen fast progressions in methodological practical and innovation applications.
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January 2025
Agricultural College of Inner Mongolia Minzu University, Tongliao, 028000, Inner Mongolia, China.
Salinity tolerance in brewing sorghum is a very important trait, especially in areas that are affected by soil salinity. In order to elucidate the mechanism underlying salt tolerance, we conducted a comparative analysis of the transcriptome and metabolome in two distinct sweet sorghum genotypes, namely the salt-tolerant line NY1298 and the salt-sensitive line MY1176, following exposure to salt treatment. Our initial findings indicate the presence of genotype-specific responses in brewing sorghum under salt stress conditions.
View Article and Find Full Text PDFInt J Med Inform
January 2025
Background: Obesity, now the fifth leading global cause of death, has seen a dramatic rise in prevalence over the last forty years. It significantly increases the risk of diseases such as type 2 diabetes and cardiovascular disease. Early identification of obesity risk allows for preventative actions against obesity-related factors.
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January 2025
Department 8.1 - Biomedical Magnetic Resonance, Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany.
Purpose: To develop a low-cost, high-performance, versatile, open-source console for low-field MRI applications that can integrate a multitude of different auxiliary sensors.
Methods: A new MR console was realized with four transmission and eight reception channels. The interface cards for signal transmission and reception are installed in PCI Express slots, allowing console integration in a commercial PC rack.
In 2019, the novel coronavirus swept the world, exposing the monitoring and early warning problems of the medical system. Computer-aided diagnosis models based on deep learning have good universality and can well alleviate these problems. However, traditional image processing methods may lead to high false positive rates, which is unacceptable in disease monitoring and early warning.
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