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      Engineering three-dimensional microenvironments towards in vitro disease models of the central nervous system

      , , , , , , ,
      Biofabrication
      IOP Publishing

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          Cerebral organoids model human brain development and microcephaly

          The complexity of the human brain has made it difficult to study many brain disorders in model organisms, and highlights the need for an in vitro model of human brain development. We have developed a human pluripotent stem cell-derived 3D organoid culture system, termed cerebral organoid, which develops various discrete though interdependent brain regions. These include cerebral cortex containing progenitor populations that organize and produce mature cortical neuron subtypes. Furthermore, cerebral organoids recapitulate features of human cortical development, namely characteristic progenitor zone organization with abundant outer radial glial stem cells. Finally, we use RNAi and patient-specific iPS cells to model microcephaly, a disorder that has been difficult to recapitulate in mice. We demonstrate premature neuronal differentiation in patient organoids, a defect that could explain the disease phenotype. Our data demonstrate that 3D organoids can recapitulate development and disease of even this most complex human tissue.
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            The amyloid hypothesis of Alzheimer's disease: progress and problems on the road to therapeutics.

            It has been more than 10 years since it was first proposed that the neurodegeneration in Alzheimer's disease (AD) may be caused by deposition of amyloid beta-peptide (Abeta) in plaques in brain tissue. According to the amyloid hypothesis, accumulation of Abeta in the brain is the primary influence driving AD pathogenesis. The rest of the disease process, including formation of neurofibrillary tangles containing tau protein, is proposed to result from an imbalance between Abeta production and Abeta clearance.
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              Cancer statistics, 2014.

              Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data were collected by the National Cancer Institute, the Centers for Disease Control and Prevention, and the North American Association of Central Cancer Registries and mortality data were collected by the National Center for Health Statistics. A total of 1,665,540 new cancer cases and 585,720 cancer deaths are projected to occur in the United States in 2014. During the most recent 5 years for which there are data (2006-2010), delay-adjusted cancer incidence rates declined slightly in men (by 0.6% per year) and were stable in women, while cancer death rates decreased by 1.8% per year in men and by 1.4% per year in women. The combined cancer death rate (deaths per 100,000 population) has been continuously declining for 2 decades, from a peak of 215.1 in 1991 to 171.8 in 2010. This 20% decline translates to the avoidance of approximately 1,340,400 cancer deaths (952,700 among men and 387,700 among women) during this time period. The magnitude of the decline in cancer death rates from 1991 to 2010 varies substantially by age, race, and sex, ranging from no decline among white women aged 80 years and older to a 55% decline among black men aged 40 years to 49 years. Notably, black men experienced the largest drop within every 10-year age group. Further progress can be accelerated by applying existing cancer control knowledge across all segments of the population. © 2014 American Cancer Society, Inc.

                Author and article information

                Contributors
                Journal
                Biofabrication
                Biofabrication
                IOP Publishing
                1758-5090
                July 01 2019
                June 06 2019
                : 11
                : 3
                : 032003
                Article
                10.1088/1758-5090/ab17aa
                30965297
                d5b06a59-e47f-4741-850c-14081331633d
                © 2019

                http://iopscience.iop.org/info/page/text-and-data-mining

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