592 research outputs found

    An automated 13.5 hour system for scalable diagnosis and acute management guidance for genetic diseases

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    While many genetic diseases have effective treatments, they frequently progress rapidly to severe morbidity or mortality if those treatments are not implemented immediately. Since front-line physicians frequently lack familiarity with these diseases, timely molecular diagnosis may not improve outcomes. Herein we describe Genome-to-Treatment, an automated, virtual system for genetic disease diagnosis and acute management guidance. Diagnosis is achieved in 13.5 h by expedited whole genome sequencing, with superior analytic performance for structural and copy number variants. An expert panel adjudicated the indications, contraindications, efficacy, and evidence-of-efficacy of 9911 drug, device, dietary, and surgical interventions for 563 severe, childhood, genetic diseases. The 421 (75%) diseases and 1527 (15%) effective interventions retained are integrated with 13 genetic disease information resources and appended to diagnostic reports ( https://gtrx.radygenomiclab.com ). This system provided correct diagnoses in four retrospectively and two prospectively tested infants. The Genome-to-Treatment system facilitates optimal outcomes in children with rapidly progressive genetic diseases

    Predicting Single-Substance Phase Diagrams: A Kernel Approach on Graph Representations of Molecules

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    This work presents a Gaussian process regression (GPR) model on top of a novel graph representation of chemical molecules that predicts thermodynamic properties of pure substances in single, double, and triple phases. A transferable molecular graph representation is proposed as the input for a marginalized graph kernel, which is the major component of the covariance function in our GPR models. Radial basis function kernels of temperature and pressure are also incorporated into the covariance function when necessary. We predicted three types of representative properties of pure substances in single, double, and triple phases, i.e., critical temperature, vapor-liquid equilibrium (VLE) density, and pressure-temperature density. The data is collected from Knovel Data Analysis Beta: NIST ThermoDynamics Pure Compounds. The accuracy of the models is nearly identical to the precision of the experimental measurements. Moreover, the reliability of our predictions can be quantified on a per-sample basis using the posterior uncertainty of the GPR model. We compare our model against Morgan fingerprints and a graph neural network to further demonstrate the advantage of the proposed method. The marginalized graph kernel is computed using GraphDot package at https://github.com/yhtang/GraphDot. All codes used in this work can be found at https://github.com/Xiangyan93/Chem-Graph-Kernel-Machine.</p

    Impact of Deoxycholic Acid on Oesophageal Adenocarcinoma Invasion: Effect on Matrix Metalloproteinases

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    Bile acids (BAs) have been implicated in the development of oesophagitis, Barrett&rsquo;s oesophagus and oesophageal adenocarcinoma (OAC). However, whether BAs promote cancer invasiveness has not been elucidated. We evaluated the role of BAs, in particular deoxycholic acid (DCA), in OAC invasion. Migration and invasiveness in untreated and BA-treated oesophageal SKGT-4 cancer cells were evaluated. Activity and expression of different matrix metalloproteinases (MMPs) were determined by zymography, ELISA, PCR and Western blot. Finally, human OAC tissues were stained for MMP-10 by immunohistochemistry. It was found that SKGT-4 cells incubated with low concentrations of DCA had a significant increase in invasion. In addition, MMP-10 mRNA and protein expression were also increased in the presence of DCA. MMP-10 was found to be highly expressed both in-vitro and in-vivo in neoplastic OAC cells relative to non-neoplastic squamous epithelial cells. Our results show that DCA promotes OAC invasion and MMP-10 overexpression. This study will advance our understanding of the pathophysiological mechanisms involved in human OAC and shows promise for the development of new therapeutic strategies

    Matrix Metalloproteinases in Inflammatory Bowel Disease: An Update

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    Matrix metalloproteinases (MMPs) are known to be upregulated in inflammatory bowel disease (IBD) and other inflammatory conditions, but while their involvement is clear, their role in many settings has yet to be determined. Studies of the involvement of MMPs in IBD since 2006 have revealed an array of immune and stromal cells which release the proteases in response to inflammatory cytokines and growth factors. Through digestion of the extracellular matrix and cleavage of bioactive proteins, a huge diversity of roles have been revealed for the MMPs in IBD, where they have been shown to regulate epithelial barrier function, immune response, angiogenesis, fibrosis, and wound healing. For this reason, MMPs have been recognised as potential biomarkers for disease activity in IBD and inhibition remains a huge area of interest. This review describes new roles of MMPs in the pathophysiology of IBD and suggests future directions for the development of treatment strategies in this condition

    Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

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    By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal organic frameworks (MOFs). At present, we have libraries of over ten thousand synthesized materials and millions of in-silico predicted materials. The fact that we have so many materials opens many exciting avenues to tailor make a material that is optimal for a given application. However, from an experimental and computational point of view we simply have too many materials to screen using brute-force techniques. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We emphasize the importance of data collection, methods to augment small data sets, how to select appropriate training sets. An important part of this review are the different approaches that are used to represent these materials in feature space. The review also includes a general overview of the different ML techniques, but as most applications in porous materials use supervised ML our review is focused on the different approaches for supervised ML. In particular, we review the different method to optimize the ML process and how to quantify the performance of the different methods. In the second part, we review how the different approaches of ML have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. The range of topics illustrates the large variety of topics that can be studied with big-data science. Given the increasing interest of the scientific community in ML, we expect this list to rapidly expand in the coming years.Comment: Editorial changes (typos fixed, minor adjustments to figures

    On the molecules of numerical semigroups, Puiseux monoids, and Puiseux algebras

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    A molecule is a nonzero non-unit element of an integral domain (resp., commutative cancellative monoid) having a unique factorization into irreducibles (resp., atoms). Here we study the molecules of Puiseux monoids as well as the molecules of their corresponding semigroup algebras, which we call Puiseux algebras. We begin by presenting, in the context of numerical semigroups, some results on the possible cardinalities of the sets of molecules and the sets of reducible molecules (i.e., molecules that are not irreducibles/atoms). Then we study the molecules in the more general context of Puiseux monoids. We construct infinitely many non-isomorphic atomic Puiseux monoids all whose molecules are atoms. In addition, we characterize the molecules of Puiseux monoids generated by rationals with prime denominators. Finally, we turn to investigate the molecules of Puiseux algebras. We provide a characterization of the molecules of the Puiseux algebras corresponding to root-closed Puiseux monoids. Then we use such a characterization to find an infinite class of Puiseux algebras with infinitely many non-associated reducible molecules.Comment: 21 pages, 2 figure

    Integration of AI and Machine Learning in Radiotherapy QA.

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    The use of machine learning and other sophisticated models to aid in prediction and decision making has become widely popular across a breadth of disciplines. Within the greater diagnostic radiology, radiation oncology, and medical physics communities promising work is being performed in tissue classification and cancer staging, outcome prediction, automated segmentation, treatment planning, and quality assurance as well as other areas. In this article, machine learning approaches are explored, highlighting specific applications in machine and patient-specific quality assurance (QA). Machine learning can analyze multiple elements of a delivery system on its performance over time including the multileaf collimator (MLC), imaging system, mechanical and dosimetric parameters. Virtual Intensity-Modulated Radiation Therapy (IMRT) QA can predict passing rates using different measurement techniques, different treatment planning systems, and different treatment delivery machines across multiple institutions. Prediction of QA passing rates and other metrics can have profound implications on the current IMRT process. Here we cover general concepts of machine learning in dosimetry and various methods used in virtual IMRT QA, as well as their clinical applications

    Improved parent–child communication following a RCT evaluating a legacy intervention for children with advanced cancer

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    Although legacy-building is a priority for quality palliative care, research has rarely examined effects of legacy interventions in children, particularly their impact on parent-child communication.We examined the impact of a web-based legacy intervention on parent-child communication. We hypothesized that compared to usual care, legacy-making would improve quality of parent-child communication.Between 2015 and 2018, Facebook advertisements were used to recruit families of children (ages 7-17) with relapsed/refractory cancer. Parent-child dyads were randomly assigned to the intervention or usual care group. The intervention website guided children to create digital storyboards over 2 weeks by directing them to answer legacy questions about themselves and upload photographs, videos, and music. Families received a copy of the child’s final digital story. Children and parents completed the Parent-Adolescent Communication Scale pre- (T1) and post-intervention (T2). Linear regressions tested for differences in change from T1 to T2 between the groups controlling for T1 values using an alpha of p < .05. Intervention effects were measured using Cohen’s d. Ninety-seven parent-child dyads were included for analysis. Changes in parent-child communication were not statistically significantly different between the groups, yet meaningful intervention effects were observed. The strongest effects were observed for improving father-child communication (Cohen’s d = −0.22-0.33). Legacy-making shows promise to facilitate improved parent-child communication, particularly for fathers. Future studies should include fathers and measure expression of feelings and parent-child interaction. Providers should continue to facilitate family communication for children with advanced disease and realize that legacy interventions may impact mother-child versus father-child communication differently

    A Survey on the Local Invertibility of Ideals in Commutative Rings

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    Let D be an integral domain. We give an overview on connections between the (t)-finite character property of D (i.e., each nonzero element of D is contained in finitely many (t)-maximal ideals) and problems of local invertibility of ideals