AMH regulates a mosaic population of AMHR2-positive cells in the ovarian surface epithelium.

The function and homeostasis of the mammalian ovary depend on complex paracrine interactions between multiple cell types. Using primary mouse tissues and isolated cells, we showed in vitro that ovarian follicles secrete factor(s) that suppresses the growth of ovarian epithelial cells in culture. Most of the growth suppressive activity was accounted for by Anti-Mullerian Hormone/Mullerian Inhibitory Substance (AMH/MIS) secreted by granulosa cells of the follicles, as determined by immune depletion experiments.

Customized antisense oligonucleotide-based therapy for neurofilament-associated Charcot-Marie-Tooth disease.

DNA-based therapeutics have emerged as a revolutionary approach for addressing the treatment gap in rare inherited conditions by targeting the fundamental genetic causes of disease. Charcot-Marie-Tooth (CMT) disease, a group of inherited neuropathies, represents one of the most prevalent Mendelian disease groups in neurology and is characterized by diverse genetic aetiology. Axonal forms of CMT, known as CMT2, are caused by dominant mutations in >30 different genes that lead to degeneration of lower motor neuron axons.

A study concept of expeditious clinical enrollment for genetic modifier studies in Charcot-Marie-Tooth neuropathy 1A.

Background: Caused by duplications of the gene encoding peripheral myelin protein 22 (PMP22), Charcot-Marie-Tooth disease type 1A (CMT1A) is the most common hereditary neuropathy. Despite this shared genetic origin, there is considerable variability in clinical severity. It is hypothesized that genetic modifiers contribute to this heterogeneity, the identification of which may reveal novel therapeutic targets.

Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images.

The recent integration of open-source data with machine learning models, especially in the medical field, has opened new doors to studying disease progression and/or regression. However, the ability to use medical data for machine learning approaches is limited by the specificity of data for a particular medical condition. In this context, the most recent technologies, like generative adversarial networks (GANs), are being looked upon as a potential way to generate high-quality synthetic data that preserve the clinical variability of a condition.