Targeted treatments for fragile X syndrome

Fragile X syndrome (FXS) is the most common identifiable genetic cause of intellectual disability and autistic spectrum disorders (ASD), with up to 50% of males and some females with FXS meeting criteria for ASD. Autistic features are present in a very high percent of individuals with FXS, even those who do not meet full criteria for ASD. Recent major advances have been made in the understanding of the neurobiology and functions of FMRP, the FMR1 (fragile X mental retardation 1) gene product, which is absent or reduced in FXS, largely based on work in the fmr1 knockout mouse model. FXS has emerged as a disorder of synaptic plasticity associated with abnormalities of long-term depression and long-term potentiation and immature dendritic spine architecture, related to the dysregulation of dendritic translation typically activated by group I mGluR and other receptors. This work has led to efforts to develop treatments for FXS with neuroactive molecules targeted to the dysregulated translational pathway. These agents have been shown to rescue molecular, spine, and behavioral phenotypes in the FXS mouse model at multiple stages of development. Clinical trials are underway to translate findings in animal models of FXS to humans, raising complex issues about trial design and outcome measures to assess cognitive change that might be associated with treatment. Genes known to be causes of ASD interact with the translational pathway defective in FXS, and it has been hypothesized that there will be substantial overlap in molecular pathways and mechanisms of synaptic dysfunction between FXS and ASD. Therefore, targeted treatments developed for FXS may also target subgroups of ASD, and clinical trials in FXS may serve as a model for the development of clinical trial strategies for ASD and other cognitive disorders

Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks.

Intraoperative diagnosis is essential for providing safe and effective care during cancer surgery1. The existing workflow for intraoperative diagnosis based on hematoxylin and eosin staining of processed tissue is time, resource and labor intensive2,3. Moreover, interpretation of intraoperative histologic images is dependent on a contracting, unevenly distributed, pathology workforce4. In the present study, we report a parallel workflow that combines stimulated Raman histology (SRH)5-7, a label-free optical imaging method and deep convolutional neural networks (CNNs) to predict diagnosis at the bedside in near real-time in an automated fashion. Specifically, our CNNs, trained on over 2.5 million SRH images, predict brain tumor diagnosis in the operating room in under 150 s, an order of magnitude faster than conventional techniques (for example, 20-30 min)2. In a multicenter, prospective clinical trial (n = 278), we demonstrated that CNN-based diagnosis of SRH images was noninferior to pathologist-based interpretation of conventional histologic images (overall accuracy, 94.6% versus 93.9%). Our CNNs learned a hierarchy of recognizable histologic feature representations to classify the major histopathologic classes of brain tumors. In addition, we implemented a semantic segmentation method to identify tumor-infiltrated diagnostic regions within SRH images. These results demonstrate how intraoperative cancer diagnosis can be streamlined, creating a complementary pathway for tissue diagnosis that is independent of a traditional pathology laboratory