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- Hankeova, S., et al.
(författare)
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Sex differences and risk factors for bleeding in Alagille syndrome
- 2022
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Ingår i: Embo Molecular Medicine. - : EMBO. - 1757-4676 .- 1757-4684. ; 14:12
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Tidskriftsartikel (refereegranskat)abstract
- Spontaneous bleeds are a leading cause of death in the pediatric JAG1-related liver disease Alagille syndrome (ALGS). We asked whether there are sex differences in bleeding events in patients, whether Jag1(Ndr/Ndr) mice display bleeds or vascular defects, and whether discovered vascular pathology can be confirmed in patients non-invasively. We performed a systematic review of patients with ALGS and vascular events following PRISMA guidelines, in the context of patient sex, and found significantly more girls than boys reported with spontaneous intracranial hemorrhage. We investigated vascular development, homeostasis, and bleeding in Jag1(Ndr/Ndr) mice, using retina as a model. Jag1(Ndr/Ndr) mice displayed sporadic brain bleeds, a thin skull, tortuous blood vessels, sparse arterial smooth muscle cell coverage in multiple organs, which could be aggravated by hypertension, and sex-specific venous defects. Importantly, we demonstrated that retinographs from patients display similar characteristics with significantly increased vascular tortuosity. In conclusion, there are clinically important sex differences in vascular disease in ALGS, and retinography allows non-invasive vascular analysis in patients. Finally, Jag1(Ndr/Ndr) mice represent a new model for vascular compromise in ALGS.
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- Matula, J, et al.
(författare)
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Resolving complex cartilage structures in developmental biology via deep learning-based automatic segmentation of X-ray computed microtomography images
- 2022
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 12:1, s. 8728-
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Tidskriftsartikel (refereegranskat)abstract
- The complex shape of embryonic cartilage represents a true challenge for phenotyping and basic understanding of skeletal development. X-ray computed microtomography (μCT) enables inspecting relevant tissues in all three dimensions; however, most 3D models are still created by manual segmentation, which is a time-consuming and tedious task. In this work, we utilised a convolutional neural network (CNN) to automatically segment the most complex cartilaginous system represented by the developing nasal capsule. The main challenges of this task stem from the large size of the image data (over a thousand pixels in each dimension) and a relatively small training database, including genetically modified mouse embryos, where the phenotype of the analysed structures differs from the norm. We propose a CNN-based segmentation model optimised for the large image size that we trained using a unique manually annotated database. The segmentation model was able to segment the cartilaginous nasal capsule with a median accuracy of 84.44% (Dice coefficient). The time necessary for segmentation of new samples shortened from approximately 8 h needed for manual segmentation to mere 130 s per sample. This will greatly accelerate the throughput of μCT analysis of cartilaginous skeletal elements in animal models of developmental diseases.
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