99 research outputs found

    Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices

    Full text link
    The rapid development in representation learning techniques such as deep neural networks and the availability of large-scale, well-annotated medical imaging datasets have to a rapid increase in the use of supervised machine learning in the 3D medical image analysis and diagnosis. In particular, deep convolutional neural networks (D-CNNs) have been key players and were adopted by the medical imaging community to assist clinicians and medical experts in disease diagnosis and treatment. However, training and inferencing deep neural networks such as D-CNN on high-resolution 3D volumes of Computed Tomography (CT) scans for diagnostic tasks pose formidable computational challenges. This challenge raises the need of developing deep learning-based approaches that are robust in learning representations in 2D images, instead 3D scans. In this work, we propose for the first time a new strategy to train \emph{slice-level} classifiers on CT scans based on the descriptors of the adjacent slices along the axis. In particular, each of which is extracted through a convolutional neural network (CNN). This method is applicable to CT datasets with per-slice labels such as the RSNA Intracranial Hemorrhage (ICH) dataset, which aims to predict the presence of ICH and classify it into 5 different sub-types. We obtain a single model in the top 4% best-performing solutions of the RSNA ICH challenge, where model ensembles are allowed. Experiments also show that the proposed method significantly outperforms the baseline model on CQ500. The proposed method is general and can be applied to other 3D medical diagnosis tasks such as MRI imaging. To encourage new advances in the field, we will make our codes and pre-trained model available upon acceptance of the paper.Comment: Accepted for presentation at the 22nd IEEE Statistical Signal Processing (SSP) worksho

    Molecular weight assessment of proteins in total proteome profiles using 1D-PAGE and LC/MS/MS

    Get PDF
    BACKGROUND: The observed molecular weight of a protein on a 1D polyacrylamide gel can provide meaningful insight into its biological function. Differences between a protein's observed molecular weight and that predicted by its full length amino acid sequence can be the result of different types of post-translational events, such as alternative splicing (AS), endoproteolytic processing (EPP), and post-translational modifications (PTMs). The characterization of these events is one of the important goals of total proteome profiling (TPP). LC/MS/MS has emerged as one of the primary tools for TPP, but since this method identifies tryptic fragments of proteins, it has not generally been used for large-scale determination of the molecular weight of intact proteins in complex mixtures. RESULTS: We have developed a set of computational tools for extracting molecular weight information of intact proteins from total proteome profiles in a high throughput manner using 1D-PAGE and LC/MS/MS. We have applied this technology to the proteome profile of a human lymphoblastoid cell line under standard culture conditions. From a total of 1 × 10(7 )cells, we identified 821 proteins by at least two tryptic peptides. Additionally, these 821 proteins are well-localized on the 1D-SDS gel. 656 proteins (80%) occur in gel slices in which the observed molecular weight of the protein is consistent with its predicted full-length sequence. A total of 165 proteins (20%) are observed to have molecular weights that differ from their predicted full-length sequence. We explore these molecular-weight differences based on existing protein annotation. CONCLUSION: We demonstrate that the determination of intact protein molecular weight can be achieved in a high-throughput manner using 1D-PAGE and LC/MS/MS. The ability to determine the molecular weight of intact proteins represents a further step in our ability to characterize gene expression at the protein level. The identification of 165 proteins whose observed molecular weight differs from the molecular weight of the predicted full-length sequence provides another entry point into the high-throughput characterization of protein modification

    A Phase I and Pharmacologic Study of Weekly Gemcitabine in Combination with Infusional 5-fluorodeoxyuridine and Oral Calcium Leucovorin

    Get PDF
    Purpose: Since preclinical studies have shown more than additive cytotoxicity and DNA damage with the combination of gemcitabine and 5-fluoro-2′-deoxyuridine (FUDR), we studied this combination in a phase I trial. Methods: Gemcitabine alone was given in cycle 1 as a 24-h, 2-h or 1-h i.v. infusion weekly for 3 of 4 weeks; if tolerated, a 24-h i.v. infusion of FUDR was added with oral leucovorin. The cycle was aborted for grade 3 thrombocytopenia, grade 4 neutropenia, and grade 2 or worse nonhematologic toxicity. Results: During cycle 1, six of eight patients who received 150 or 100 mg/m2 over 24 h had dose-limiting neutropenia, thrombocytopenia, fatigue or mucositis. Six of seven patients treated with 1000 mg/m2 over 2 h required a gemcitabine dose reduction for cycle 2 (thrombocytopenia, neutropenia, fatigue). Of 25 assessable patients who received gemcitabine 1000 mg/m2 over 1 h, 7 did not complete cycle 1 due to thrombocytopenia (n = 6) or diarrhea (n = 1). Of 42 patients entered, 27 received at least one course of gemcitabine/FUDR (5-19.5 mg/m2 over 24 h) without appreciable toxicity. Due to a shortage of FUDR, the protocol was closed early. Gemcitabine plasma concentrations averaged 0.061 μM (24 h), 16.3 μM (2 h), and 31.9 μM (1 h). In 21 paired bone marrow mononuclear cell samples obtained before treatment and during FUDR infusion, thymidylate synthase ternary complex was only seen during FUDR infusion. Conclusions: Gemcitabine 100-150 mg/m2 over 24 h was poorly tolerated, whereas toxicity was acceptable with 800-1000 mg/m2 over 1 h. Inhibition of the target enzyme was demonstrated at all FUDR doses

    Disruption of reducing pathways is not essential for efficient disulfide bond formation in the cytoplasm of E. coli

    Get PDF
    <p>Abstract</p> <p>Background</p> <p>The formation of native disulfide bonds is a complex and essential post-translational modification for many proteins. The large scale production of these proteins can be difficult and depends on targeting the protein to a compartment in which disulfide bond formation naturally occurs, usually the endoplasmic reticulum of eukaryotes or the periplasm of prokaryotes. It is currently thought to be impossible to produce large amounts of disulfide bond containing protein in the cytoplasm of wild-type bacteria such as <it>E. coli </it>due to the presence of multiple pathways for their reduction.</p> <p>Results</p> <p>Here we show that the introduction of Erv1p, a sulfhydryl oxidase and FAD-dependent catalyst of disulfide bond formation found in the inter membrane space of mitochondria, allows the efficient formation of native disulfide bonds in heterologously expressed proteins in the cytoplasm of <it>E. coli </it>even without the disruption of genes involved in disulfide bond reduction, for example <it>trxB </it>and/or <it>gor</it>. Indeed yields of active disulfide bonded proteins were higher in BL21 (DE3) pLysSRARE, an <it>E. coli </it>strain with the reducing pathways intact, than in the commercial Δ<it>gor </it>Δ<it>trxB </it>strain rosetta-gami upon co-expression of Erv1p.</p> <p>Conclusions</p> <p>Our results refute the current paradigm in the field that disruption of at least one of the reducing pathways is essential for the efficient production of disulfide bond containing proteins in the cytoplasm of <it>E. coli </it>and open up new possibilities for the use of <it>E. coli </it>as a microbial cell factory.</p
    corecore