Identification and Quantification of Gaps in Access to Autism Resources in the United States: An Infodemiological Study.

BACKGROUND: Autism affects 1 in every 59 children in the United States, according to estimates from the Centers for Disease Control and Prevention's Autism and Developmental Disabilities Monitoring Network in 2018. Although similar rates of autism are reported in rural and urban areas, rural families report greater difficulty in accessing resources. An overwhelming number of families experience long waitlists for diagnostic and therapeutic services. OBJECTIVE: The objective of this study was to accurately identify gaps in access to autism care using GapMap, a mobile platform that connects families with local resources while continuously collecting up-to-date autism resource epidemiological information. METHODS: After being extracted from various databases, resources were deduplicated, validated, and allocated into 7 categories based on the keywords identified on the resource website. The average distance between the individuals from a simulated autism population and the nearest autism resource in our database was calculated for each US county. Resource load, an approximation of demand over supply for diagnostic resources, was calculated for each US county. RESULTS: There are approximately 28,000 US resources validated on the GapMap database, each allocated into 1 or more of the 7 categories. States with the greatest distances to autism resources included Alaska, Nevada, Wyoming, Montana, and Arizona. Of the 7 resource categories, diagnostic resources were the most underrepresented, comprising only 8.83% (2472/28,003) of all resources. Alarmingly, 83.86% (2635/3142) of all US counties lacked any diagnostic resources. States with the highest diagnostic resource load included West Virginia, Kentucky, Maine, Mississippi, and New Mexico. CONCLUSIONS: Results from this study demonstrate the sparsity and uneven distribution of diagnostic resources in the United States, which may contribute to the lengthy waitlists and travel distances-barriers to be overcome to be able to receive diagnosis in specific regions. More data are needed on autism diagnosis demand to better quantify resource needs across the United States.

The novel kinase inhibitor ponatinib is an effective anti-angiogenic agent against neuroblastoma.

Background High-risk neuroblastoma has poor outcomes with high rates of relapse despite aggressive treatment, and novel therapies are needed to improve these outcomes. Ponatinib is a multi-tyrosine kinase inhibitor that targets many pathways implicated in neuroblastoma pathogenesis. We hypothesized that ponatinib would be effective against neuroblastoma in preclinical models. Methods We evaluated the effects of ponatinib on survival and migration of human neuroblastoma cells in vitro. Using orthotopic xenograft mouse models of human neuroblastoma, we analyzed tumors treated with ponatinib for growth, gross and histologic appearance, and vascularity. Results Ponatinib treatment of neuroblastoma cells resulted in decreased cell viability and migration in vitro. In mice with orthotopic xenograft neuroblastoma tumors, treatment with ponatinib resulted in decreased growth and vascularity. Conclusions Ponatinib reduces neuroblastoma cell viability in vitro and reduces tumor growth and vascularity in vivo. The antitumor effects of ponatinib suggest its potential as a novel therapeutic agent for neuroblastoma, and further preclinical testing is warranted.

Lateral cell polarization drives organization of epithelia in sea anemone embryos and embryonic cell aggregates.

One of the first organizing processes during animal development is the assembly of embryonic cells into epithelia. In certain animals, including Hydra and sea anemones, epithelia also emerge when cells from dissociated tissues are aggregated back together. Although cell adhesion is required to keep cells together, it is not clear whether cell polarization plays a role as epithelia emerge from disordered aggregates. Here, we demonstrate that lateral cell polarization is essential for epithelial organization in both embryos and aggregates of the sea anemone Nematostella vectensis. Specifically, knock down of the lateral polarity protein Lgl disrupts epithelia in developing embryos and impairs the capacity of dissociated cells to epithelialize from aggregates. Cells in lgl mutant epithelia lose their columnar shape and have mispositioned mitotic spindles and ciliary basal bodies. Together, our data suggest that in Nematostella, Lgl is required to establish lateral cell polarity and position cytoskeletal organelles in cells of embryos and aggregates during de novo epithelial organization.

Estimation of the zero-pressure computational start shape of atherosclerotic plaques: Improving the backward displacement method with deformation gradient tensor.

Advances in medical imaging have enabled patient-specific biomechanical modelling of arterial lesions such as atherosclerosis and aneurysm. Geometry acquired from in-vivo imaging is already pressurized and a zero-pressure computational start shape needs to be identified. The backward displacement algorithm was proposed to solve this inverse problem, utilizing fixed-point iterations to gradually approach the start shape. However, classical fixed-point implementations were reported with suboptimal convergence properties under large deformations. In this paper, a dynamic learning rate guided by the deformation gradient tensor was introduced to control the geometry update. The effectiveness of this new algorithm was demonstrated for both idealized and patient-specific models. The proposed algorithm led to faster convergence by accelerating the initial steps and helped to avoid the non-convergence in large-deformation problems.

Identifying functions and prognostic biomarkers of network motifs marked by diverse chromatin states in human cell lines.

Epigenetic modifications play critical roles in modulating gene expression, yet their roles in regulatory networks in human cell lines remain poorly characterized. We integrated multiomics data to construct directed regulatory networks with nodes and edges labeled with chromatin states in human cell lines. We observed extensive association of diverse chromatin states and network motifs. The gene expression analysis showed that diverse chromatin states of coherent type-1 feedforward loop (C1-FFL) and incoherent type-1 feedforward loops (I1-FFL) contributed to the dynamic expression patterns of targets. Notably, diverse chromatin state compositions could help C1- or I1-FFL to control a large number of distinct biological functions in human cell lines, such as four different types of chromatin state compositions cooperating with K562-associated C1-FFLs controlling "regulation of cytokinesis," "G1/S transition of mitotic cell cycle," "DNA recombination," and "telomere maintenance," respectively. Remarkably, we identified six chromatin state-marked C1-FFL instances (HCFC1-NFYA-ABL1, THAP1-USF1-BRCA2, ZNF263-USF1-UBA52, MYC-ATF1-UBA52, ELK1-EGR1-CCT4, and YY1-EGR1-INO80C) could act as prognostic biomarkers of acute myelogenous leukemia though influencing cancer-related biological functions, such as cell proliferation, telomere maintenance, and DNA recombination. Our results will provide novel insight for better understanding of chromatin state-mediated gene regulation and facilitate the identification of novel diagnostic and therapeutic biomarkers of human cancers.

Neural network fusion: a novel CT-MR Aortic Aneurysm image segmentation method.

Medical imaging examination on patients usually involves more than one imaging modalities, such as Computed Tomography (CT), Magnetic Resonance (MR) and Positron Emission Tomography(PET) imaging. Multimodal imaging allows examiners to benefit from the advantage of each modalities. For example, for Abdominal Aortic Aneurysm, CT imaging shows calcium deposits in the aorta clearly while MR imaging distinguishes thrombus and soft tissues better.1 Analysing and segmenting both CT and MR images to combine the results will greatly help radiologists and doctors to treat the disease. In this work, we present methods on using deep neural network models to perform such multi-modal medical image segmentation. As CT image and MR image of the abdominal area cannot be well registered due to non-affine deformations, a naive approach is to train CT and MR segmentation network separately. However, such approach is time-consuming and resource-inefficient. We propose a new approach to fuse the high-level part of the CT and MR network together, hypothesizing that neurons recognizing the high level concepts of Aortic Aneurysm can be shared across multiple modalities. Such network is able to be trained end-to-end with non-registered CT and MR image using shorter training time. Moreover network fusion allows a shared representation of Aorta in both CT and MR images to be learnt. Through experiments we discovered that for parts of Aorta showing similar aneurysm conditions, their neural presentations in neural network has shorter distances. Such distances on the feature level is helpful for registering CT and MR image.

The GapMap project: a mobile surveillance system to map diagnosed autism cases and gaps in autism services globally.

Although the number of autism diagnoses is on the rise, we have no evidence-based tracking of size and severity of gaps in access to autism-related resources, nor do we have methods to geographically triangulate the locations of the widest gaps in either the US or elsewhere across the globe. To combat these related issues of (1) mapping diagnosed cases of autism and (2) quantifying gaps in access to key intervention services, we have constructed a crowd-based mobile platform called "GapMap" (http://gapmap.stanford.edu) for real-time tracking of autism prevalence and autism-related resources that can be accessed from any mobile device with cellular or wireless connectivity. Now in beta, our aim is for this Android/iOS compatible mobile tool to simultaneously crowd-enroll the massive and growing community of families with autism to capture geographic, diagnostic, and resource usage information while automatically computing prevalence at granular geographical scales to yield a more complete and dynamic understanding of autism resource epidemiology.

GapMap: Enabling Comprehensive Autism Resource Epidemiology.

BACKGROUND: For individuals with autism spectrum disorder (ASD), finding resources can be a lengthy and difficult process. The difficulty in obtaining global, fine-grained autism epidemiological data hinders researchers from quickly and efficiently studying large-scale correlations among ASD, environmental factors, and geographical and cultural factors. OBJECTIVE: The objective of this study was to define resource load and resource availability for families affected by autism and subsequently create a platform to enable a more accurate representation of prevalence rates and resource epidemiology. METHODS: We created a mobile application, GapMap, to collect locational, diagnostic, and resource use information from individuals with autism to compute accurate prevalence rates and better understand autism resource epidemiology. GapMap is hosted on AWS S3, running on a React and Redux front-end framework. The backend framework is comprised of an AWS API Gateway and Lambda Function setup, with secure and scalable end points for retrieving prevalence and resource data, and for submitting participant data. Measures of autism resource scarcity, including resource load, resource availability, and resource gaps were defined and preliminarily computed using simulated or scraped data. RESULTS: The average distance from an individual in the United States to the nearest diagnostic center is approximately 182 km (50 miles), with a standard deviation of 235 km (146 miles). The average distance from an individual with ASD to the nearest diagnostic center, however, is only 32 km (20 miles), suggesting that individuals who live closer to diagnostic services are more likely to be diagnosed. CONCLUSIONS: This study confirmed that individuals closer to diagnostic services are more likely to be diagnosed and proposes GapMap, a means to measure and enable the alleviation of increasingly overburdened diagnostic centers and resource-poor areas where parents are unable to diagnose their children as quickly and easily as needed. GapMap will collect information that will provide more accurate data for computing resource loads and availability, uncovering the impact of resource epidemiology on age and likelihood of diagnosis, and gathering localized autism prevalence rates.

Systemically identifying and prioritizing risk lncRNAs through integration of pan-cancer phenotype associations.

LncRNAs have emerged as a major class of regulatory molecules involved in normal cellular physiology and disease, our knowledge of lncRNAs is very limited and it has become a major research challenge in discovering novel disease-related lncRNAs in cancers. Based on the assumption that diverse diseases with similar phenotype associations show similar molecular mechanisms, we presented a pan-cancer network-based prioritization approach to systematically identify disease-specific risk lncRNAs by integrating disease phenotype associations. We applied this strategy to approximately 2800 tumor samples from 14 cancer types for prioritizing disease risk lncRNAs. Our approach yielded an average area under the ROC curve (AUC) of 80.66%, with the highest AUC (98.14%) for medulloblastoma. When evaluated using leave-one-out cross-validation (LOOCV) for prioritization of disease candidate genes, the average AUC score of 97.16% was achieved. Moreover, we demonstrated the robustness as well as the integrative importance of this approach, including disease phenotype associations, known disease genes and the numbers of cancer types. Taking glioblastoma multiforme as a case study, we identified a candidate lncRNA gene SNHG1 as a novel disease risk factor for disease diagnosis and prognosis. In summary, we provided a novel lncRNA prioritization approach by integrating pan-cancer phenotype associations that could help researchers better understand the important roles of lncRNAs in human cancers.

Nifurtimox Is Effective Against Neural Tumor Cells and Is Synergistic with Buthionine Sulfoximine.

Children with aggressive neural tumors have poor survival rates and novel therapies are needed. Previous studies have identified nifurtimox and buthionine sulfoximine (BSO) as effective agents in children with neuroblastoma and medulloblastoma. We hypothesized that nifurtimox would be effective against other neural tumor cells and would be synergistic with BSO. We determined neural tumor cell viability before and after treatment with nifurtimox using MTT assays. Assays for DNA ladder formation and poly-ADP ribose polymerase (PARP) cleavage were performed to measure the induction of apoptosis after nifurtimox treatment. Inhibition of intracellular signaling was measured by Western blot analysis of treated and untreated cells. Tumor cells were then treated with combinations of nifurtimox and BSO and evaluated for viability using MTT assays. All neural tumor cell lines were sensitive to nifurtimox, and IC50 values ranged from approximately 20 to 210 µM. Nifurtimox treatment inhibited ERK phosphorylation and induced apoptosis in tumor cells. Furthermore, the combination of nifurtimox and BSO demonstrated significant synergistic efficacy in all tested cell lines. Additional preclinical and clinical studies of the combination of nifurtimox and BSO in patients with neural tumors are warranted.