Neue Untersuchungsergebnisse zum alter von dunkelbäuchigen Ringelgänsen Branta b. bernicla

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Facile isolation and the characterization of human retinal stem cells

This study identifies and characterizes retinal stem cells (RSCs) in early postnatal to seventh-decade human eyes. Different subregions of human eyes were dissociated and cultured by using a clonal sphere-forming assay. The stem cells were derived only from the pars plicata and pars plana of the retinal ciliary margin, at a frequency of approximately 1:500. To test for long-term self-renewal, both the sphere assay and monolayer passaging were used. By using the single sphere passaging assay, primary spheres were dissociated and replated, and individual spheres demonstrated 100% self-renewal, with single spheres giving rise to one or more new spheres in each subsequent passage. The clonal retinal spheres were plated under differentiation conditions to assay the differentiation potential of their progeny. The spheres were produced all of the different retinal cell types, demonstrating multipotentiality. Therefore, the human eye contains a small population of cells (approximately equal to 10,000 cells per eye) that have retinal stem-cell characteristics (proliferation, self-renewal, and multipotentiality). To test the in vivo potential of the stem cells and their progeny, we transplanted dissociated human retinal sphere cells, containing both stem cells and progenitors, into the eyes of postnatal day 1 NOD/SCID mice and embryonic chick eyes. The progeny of the RSCs were able to survive, migrate, integrate, and differentiate into the neural retina, especially as photoreceptors. Their facile isolation, integration, and differentiation suggest that human RSCs eventually may be valuable in treating human retinal diseases

The proliferation and expansion of retinal stem cells require functional Pax6

AbstractRetinal stem cells (RSCs) exist as rare pigmented ciliary epithelial cells in adult mammalian eyes. We hypothesized that RSCs are at the top of the retinal cell lineage. Thus, genes expressed early in embryonic development to establish the retinal field in forebrain neuroectoderm may play important roles in RSCs. Pax6, a paired domain and homeodomain-containing transcription factor, is one of the earliest genes expressed in the eye field and is considered a master control gene for retinal and eye development. Here, we demonstrate that Pax6 is enriched in RSCs. Inactivation of Pax6 in vivo results in loss of competent RSCs as assayed by the failure to form clonal RSC spheres from the optic vesicles of conventional Pax6 knockout embryos and from the ciliary epithelial cells of adult Pax6 conditional knockout mice. In vitro clonal inactivation of Pax6 in adult RSCs results in a serious proliferation defect, suggesting that Pax6 is required for the proliferation and expansion of RSCs

Automated syndrome diagnosis by three-dimensional facial imaging.

PurposeDeep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30-40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces.MethodsWe analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images.ResultsUnrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative.ConclusionDeep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of "unaffected" relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance

Automated syndrome diagnosis by three-dimensional facial imaging.

PurposeDeep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30-40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces.MethodsWe analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images.ResultsUnrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative.ConclusionDeep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of "unaffected" relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance