165 research outputs found

    Association between gut microbiota and pan-dermatological diseases: a bidirectional Mendelian randomization research

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    BackgroundGut microbiota has been associated with dermatological problems in earlier observational studies. However, it is unclear whether gut microbiota has a causal function in dermatological diseases.MethodsThirteen dermatological diseases were the subject of bidirectional Mendelian randomization (MR) research aimed at identifying potential causal links between gut microbiota and these diseases. Summary statistics for the Genome-Wide Association Study (GWAS) of gut microbiota and dermatological diseases were obtained from public datasets. With the goal of evaluating the causal estimates, five acknowledged MR approaches were utilized along with multiple testing corrections, with inverse variance weighted (IVW) regression serving as the main methodology. Regarding the taxa that were causally linked with dermatological diseases in the forward MR analysis, reverse MR was performed. A series of sensitivity analyses were conducted to test the robustness of the causal estimates.ResultsThe combined results of the five MR methods and sensitivity analysis showed 94 suggestive and five significant causal relationships. In particular, the genus Eubacterium_fissicatena_group increased the risk of developing psoriasis vulgaris (odds ratio [OR] = 1.32, pFDR = 4.36 × 10−3), family Bacteroidaceae (OR = 2.25, pFDR = 4.39 × 10−3), genus Allisonella (OR = 1.42, pFDR = 1.29 × 10−2), and genus Bacteroides (OR = 2.25, pFDR = 1.29 × 10−2) increased the risk of developing acne; and the genus Intestinibacter increased the risk of urticaria (OR = 1.30, pFDR = 9.13 × 10−3). A reverse MR study revealed insufficient evidence for a significant causal relationship. In addition, there was no discernible horizontal pleiotropy or heterogeneity.ConclusionThis study provides novel insights into the causality of gut microbiota in dermatological diseases and therapeutic or preventive paradigms for cutaneous conditions

    Malignancy‐associated ischemic stroke : implications for diagnostic and therapeutic workup

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    Background: Patients with malignancies have an increased risk of suffering ischemic stroke via several mechanisms such as coagulation dysfunction and other malignancy-related effects as well as iatrogenic causes. Moreover, stroke can be the first sign of an occult malignancy, termed as malignancy-associated ischemic stroke (MAS). Therefore, timely diagnostic assessment and targeted management of this complex clinical situation are critical. Findings: Patients with both stroke and malignancy have atypical ages, risk factors, and often exhibit malignancy-related symptoms and multiple lesions on neuroimaging. New biomarkers such as eicosapentaenoic acid and blood mRNA profiles may help in distinguishing MAS from other strokes. In terms of treatment, malignancy should not be considered a contraindication, given comparable rates of recanalization and complications between stroke patients with or without malignancies. Conclusion: In this review, we summarize the latest developments in diagnosing and managing MAS, especially stroke with occult malignancies, and provide new recommendations from recently emerged clinical evidence for diagnostic and therapeutic workup strategies

    Automatic Evaluation for Engineering Pathway Premier Award Winners

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    Abstract: Educating the K-Gray engineering community in today’s digital world requires straightforward yet flexible access to highquality educational resources. Inspires by this, we propose an automatic evaluation system for learning resources ranking in a real world digital library, Engineering Pathway (EP). The Engineering Pathway is a portal to high-quality teaching and learning resources in engineering, applied science and math, computer science/information technology, and engineering technology, which is designed for use by K-12 and university educators and students. We model the best and most popular leaning resource objects from Premier Award Winners to recognize high-quality and non-commercial courseware designed to enhance the engineering education. We adopt the D-S evidence theory to model our problem. After giving effective definition of the mass function, the model can be transferred into multinomial regression model. We try three different models: linear regression, quadratic regression and sextic regression to get the most practicable model. With the help of this model, it will be much more simple and precise to help our domain experts to select the most valuable learning resources in our EP digital library. Experiments show that out proposed model performs well through training and optimization

    A Self-adaptive and Real-time panoramic video mosaicing system

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    Dynamic image mosaic via SIFT and dynamic programming

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