844 research outputs found

    Computer-aided assessment of diagnostic images for epidemiological research

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    <p>Abstract</p> <p>Background</p> <p>Diagnostic images are often assessed for clinical outcomes using subjective methods, which are limited by the skill of the reviewer. Computer-aided diagnosis (CAD) algorithms that assist reviewers in their decisions concerning outcomes have been developed to increase sensitivity and specificity in the clinical setting. However, these systems have not been well utilized in research settings to improve the measurement of clinical endpoints. Reductions in bias through their use could have important implications for etiologic research.</p> <p>Methods</p> <p>Using the example of cortical cataract detection, we developed an algorithm for assisting a reviewer in evaluating digital images for the presence and severity of lesions. Available image processing and statistical methods that were easily implementable were used as the basis for the CAD algorithm. The performance of the system was compared to the subjective assessment of five reviewers using 60 simulated images. Cortical cataract severity scores from 0 to 16 were assigned to the images by the reviewers and the CAD system, with each image assessed twice to obtain a measure of variability. Image characteristics that affected reviewer bias were also assessed by systematically varying the appearance of the simulated images.</p> <p>Results</p> <p>The algorithm yielded severity scores with smaller bias on images where cataract severity was mild to moderate (approximately ≀ 6/16<sup><it>ths</it></sup>). On high severity images, the bias of the CAD system exceeded that of the reviewers. The variability of the CAD system was zero on repeated images but ranged from 0.48 to 1.22 for the reviewers. The direction and magnitude of the bias exhibited by the reviewers was a function of the number of cataract opacities, the shape and the contrast of the lesions in the simulated images.</p> <p>Conclusion</p> <p>CAD systems are feasible to implement with available software and can be valuable when medical images contain exposure or outcome information for etiologic research. Our results indicate that such systems have the potential to decrease bias and discriminate very small changes in disease severity. Simulated images are a tool that can be used to assess performance of a CAD system when a gold standard is not available.</p

    Fatal COVID-19 outcomes are associated with an antibody response targeting epitopes shared with endemic coronaviruses

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    The role of immune responses to previously seen endemic coronavirus epitopes in severe acute respiratory coronavirus 2 (SARS-CoV-2) infection and disease progression has not yet been determined. Here, we show that a key characteristic of fatal coronavirus disease (COVID-19) outcomes is that the immune response to the SARS-CoV-2 spike protein is enriched for antibodies directed against epitopes shared with endemic beta-coronaviruses, and has a lower proportion of antibodies targeting the more protective variable regions of the spike. The magnitude of antibody responses to the SARS-CoV-2 full-length spike protein, its domains and subunits, and the SARS-CoV-2 nucleocapsid also correlated strongly with responses to the endemic beta-coronavirus spike proteins in individuals admitted to intensive care units (ICU) with fatal COVID-19 outcomes, but not in individuals with non-fatal outcomes. This correlation was found to be due to the antibody response directed at the S2 subunit of the SARS-CoV-2 spike protein, which has the highest degree of conservation between the beta-coronavirus spike proteins. Intriguingly, antibody responses to the less cross-reactive SARS-CoV-2 nucleocapsid were not significantly different in individuals who were admitted to ICU with fatal and non-fatal outcomes, suggesting an antibody profile in individuals with fatal outcomes consistent with an original antigenic sin type-response

    Polymersome-Mediated Delivery of Combination Anticancer Therapy to Head and Neck Cancer Cells: 2D and 3D in Vitro Evaluation

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    Polymersomes have the potential to encapsulate and deliver chemotherapeutic drugs into tumor cells, reducing off-target toxicity that often compromises anticancer treatment. Here, we assess the ability of the pH-sensitive poly 2-(methacryloyloxy)ethyl phosphorylcholine (PMPC)- poly 2-(diisopropylamino)ethyl methacrylate (PDPA) polymersomes to encapsulate chemotherapeutic agents for effective combinational anticancer therapy. Polymersome uptake and ability to deliver encapsulated drugs into healthy normal oral cells and oral head and neck squamous cell carcinoma (HNSCC) cells was measured in two and three-dimensional culture systems. PMPC-PDPA polymersomes were more rapidly internalized by HNSCC cells compared to normal oral cells. Polymersome cellular uptake was found to be mediated by class B scavenger receptors. We also observed that these receptors are more highly expressed by cancer cells compared to normal oral cells, enabling polymersome-mediated targeting. Doxorubicin and paclitaxel were encapsulated into pH-sensitive PMPC-PDPA polymersomes with high efficiencies either in isolation or as a dual-load for both singular and combinational delivery. In monolayer culture, only a short exposure to drug-loaded polymersomes was required to elicit a strong cytotoxic effect. When delivered to three-dimensional tumor models, PMPC-PDPA polymersomes were able to penetrate deep into the center of the spheroid resulting in extensive cell damage when loaded with both singular and dual-loaded chemotherapeutics. PMPC-PDPA polymersomes offer a novel system for the effective delivery of chemotherapeutics for the treatment of HNSCC. Moreover, the preferential internalization of PMPC polymersomes by exploiting elevated scavenger receptor expression on cancer cells opens up the opportunity to target polymersomes to tumors

    Rare coding variants in ten genes confer substantial risk for schizophrenia

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    Rare coding variation has historically provided the most direct connections between gene function and disease pathogenesis. By meta-analysing the whole exomes of 24,248 schizophrenia cases and 97,322 controls, we implicate ultra-rare coding variants (URVs) in 10 genes as conferring substantial risk for schizophrenia (odds ratios of 3-50, PPeer reviewe

    Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A

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    The major histocompatibility complex (MHC) on chromosome 6 is associated with susceptibility to more common diseases than any other region of the human genome, including almost all disorders classified as autoimmune. In type 1 diabetes the major genetic susceptibility determinants have been mapped to the MHC class II genes HLA-DQB1 and HLA-DRB1 (refs 1-3), but these genes cannot completely explain the association between type 1 diabetes and the MHC region. Owing to the region's extreme gene density, the multiplicity of disease-associated alleles, strong associations between alleles, limited genotyping capability, and inadequate statistical approaches and sample sizes, which, and how many, loci within the MHC determine susceptibility remains unclear. Here, in several large type 1 diabetes data sets, we analyse a combined total of 1,729 polymorphisms, and apply statistical methods - recursive partitioning and regression - to pinpoint disease susceptibility to the MHC class I genes HLA-B and HLA-A (risk ratios >1.5; Pcombined = 2.01 × 10-19 and 2.35 × 10-13, respectively) in addition to the established associations of the MHC class II genes. Other loci with smaller and/or rarer effects might also be involved, but to find these, future searches must take into account both the HLA class II and class I genes and use even larger samples. Taken together with previous studies, we conclude that MHC-class-I-mediated events, principally involving HLA-B*39, contribute to the aetiology of type 1 diabetes. ©2007 Nature Publishing Group

    Identification of a BRCA2-Specific modifier locus at 6p24 related to breast cancer risk

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    Common genetic variants contribute to the observed variation in breast cancer risk for BRCA2 mutation carriers; those known to date have all been found through population-based genome-wide association studies (GWAS). To comprehensively identify breast cancer risk modifying loci for BRCA2 mutation carriers, we conducted a deep replication of an ongoing GWAS discovery study. Using the ranked P-values of the breast cancer associations with the imputed genotype of 1.4 M SNPs, 19,029 SNPs were selected and designed for inclusion on a custom Illumina array that included a total of 211,155 SNPs as part of a multi-consortial project. DNA samples from 3,881 breast cancer affected and 4,330 unaffected BRCA2 mutation carriers from 47 studies belonging to the Consortium of Investigators of Modifiers of BRCA1/2 were genotyped and available for analysis. We replicated previously reported breast cancer susceptibility alleles in these BRCA2 mutation carriers and for several regions (including FGFR2, MAP3K1, CDKN2A/B, and PTHLH) identified SNPs that have stronger evidence of association than those previously published. We also identified a novel susceptibility allele at 6p24 that was inversely associated with risk in BRCA2 mutation carriers (rs9348512; per allele HR = 0.85, 95% CI 0.80-0.90, P = 3.9×10−8). This SNP was not associated with breast cancer risk either in the general population or in BRCA1 mutation carriers. The locus lies within a region containing TFAP2A, which encodes a transcriptional activation protein that interacts with several tumor suppressor genes. This report identifies the first breast cancer risk locus specific to a BRCA2 mutation background. This comprehensive update of novel and previously reported breast cancer susceptibility loci contributes to the establishment of a panel of SNPs that modify breast cancer risk in BRCA2 mutation carriers. This panel may have clinical utility for women with BRCA2 mutations weighing options for medical prevention of breast cancer
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