45 research outputs found

    Critical Factors in Software Adoption

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    The life of plant mitochondrial complex I

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    The mitochondrial NADH dehydrogenase complex (complex I) of the respiratory chain has several remarkable features in plants: (i) particularly many of its subunits are encoded by the mitochondrial genome, (ii) its mitochondrial transcripts undergo extensive maturation processes (e.g. RNA editing, trans-splicing), (iii) its assembly follows unique routes, (iv) it includes an additional functional domain which contains carbonic anhydrases and (v) it is, indirectly, involved in photosynthesis. Comprising about 50 distinct protein subunits, complex I of plants is very large. However, an even larger number of proteins are required to synthesize these subunits and assemble the enzyme complex. This review aims to follow the complete "life cycle" of plant complex I from various molecular perspectives. We provide arguments that complex I represents an ideal model system for studying the interplay of respiration and photosynthesis, the cooperation of mitochondria and the nucleus during organelle biogenesis and the evolution of the mitochondrial oxidative phosphorylation system. © 2014 Elsevier B.V

    Why Are Outcomes Different for Registry Patients Enrolled Prospectively and Retrospectively? Insights from the Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF).

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    Background: Retrospective and prospective observational studies are designed to reflect real-world evidence on clinical practice, but can yield conflicting results. The GARFIELD-AF Registry includes both methods of enrolment and allows analysis of differences in patient characteristics and outcomes that may result. Methods and Results: Patients with atrial fibrillation (AF) and ≥1 risk factor for stroke at diagnosis of AF were recruited either retrospectively (n = 5069) or prospectively (n = 5501) from 19 countries and then followed prospectively. The retrospectively enrolled cohort comprised patients with established AF (for a least 6, and up to 24 months before enrolment), who were identified retrospectively (and baseline and partial follow-up data were collected from the emedical records) and then followed prospectively between 0-18 months (such that the total time of follow-up was 24 months; data collection Dec-2009 and Oct-2010). In the prospectively enrolled cohort, patients with newly diagnosed AF (≤6 weeks after diagnosis) were recruited between Mar-2010 and Oct-2011 and were followed for 24 months after enrolment. Differences between the cohorts were observed in clinical characteristics, including type of AF, stroke prevention strategies, and event rates. More patients in the retrospectively identified cohort received vitamin K antagonists (62.1% vs. 53.2%) and fewer received non-vitamin K oral anticoagulants (1.8% vs . 4.2%). All-cause mortality rates per 100 person-years during the prospective follow-up (starting the first study visit up to 1 year) were significantly lower in the retrospective than prospectively identified cohort (3.04 [95% CI 2.51 to 3.67] vs . 4.05 [95% CI 3.53 to 4.63]; p = 0.016). Conclusions: Interpretations of data from registries that aim to evaluate the characteristics and outcomes of patients with AF must take account of differences in registry design and the impact of recall bias and survivorship bias that is incurred with retrospective enrolment. Clinical Trial Registration: - URL: http://www.clinicaltrials.gov . Unique identifier for GARFIELD-AF (NCT01090362)

    Finishing the euchromatic sequence of the human genome

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    The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

    Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

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    Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting. Design, Setting, and Participants Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884]; P < .001). The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the pathologist WOTC). Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting

    Tolerogenic nanoparticles inhibit T cell-mediated autoimmunity through SOCS2.

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    Type 1 diabetes (T1D) is a T cell-dependent autoimmune disease that is characterized by the destruction of insulin-producing β cells in the pancreas. The administration to patients of ex vivo-differentiated FoxP3(+) regulatory T (Treg) cells or tolerogenic dendritic cells (DCs) that promote Treg cell differentiation is considered a potential therapy for T1D; however, cell-based therapies cannot be easily translated into clinical practice. We engineered nanoparticles (NPs) to deliver both a tolerogenic molecule, the aryl hydrocarbon receptor (AhR) ligand 2-(1'H-indole-3'-carbonyl)-thiazole-4-carboxylic acid methyl ester (ITE), and the β cell antigen proinsulin (NPITE+Ins) to induce a tolerogenic phenotype in DCs and promote Treg cell generation in vivo. NPITE+Ins administration to 8-week-old nonobese diabetic mice suppressed autoimmune diabetes. NPITE+Ins induced a tolerogenic phenotype in DCs, which was characterized by a decreased ability to activate inflammatory effector T cells and was concomitant with the increased differentiation of FoxP3(+) Treg cells. The induction of a tolerogenic phenotype in DCs by NPs was mediated by the AhR-dependent induction of Socs2, which resulted in inhibition of nuclear factor κB activation and proinflammatory cytokine production (properties of tolerogenic DCs). Together, these data suggest that NPs constitute a potential tool to reestablish tolerance in T1D and potentially other autoimmune disorders
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