Sex-specific associations of comorbidome and pulmorbidome with mortality in chronic obstructive pulmonary disease: results from COSYCONET.

In patients with COPD, it has not been comprehensively assessed whether the predictive value of comorbidities for mortality differs between men and women. We therefore aimed to examine sex differences of COPD comorbidities in regard with prognosis by classifying comorbidities into a comorbidome related to extrapulmonary disorders and a pulmorbidome, referring to pulmonary disorders. The study population comprised 1044 women and 1531 men with the diagnosis of COPD from COSYCONET, among them 2175 of GOLD grades 1-4 and 400 at risk. Associations of comorbidities with mortality were studied using Cox regression analysis for men and women separately. During the follow-up (median 3.7 years) 59 women and 159 men died. In men, obesity, hypertension, coronary artery disease, liver cirrhosis, osteoporosis, kidney disease, anaemia and increased heart rate (HR) predict mortality, in women heart failure, hyperuricemia, mental disorders, kidney disease and increased HR (p < 0.05 each). Regarding the pulmorbidome, significant predictors in men were impairment in diffusion capacity and hyperinflation, in women asthma and hyperinflation. Similar results were obtained when repeating the analyses in GOLD 1-4 patients only. Gender differences should be considered in COPD risk assessment for a tailored approach towards the treatment of COPD.Clinical Trial Registration: ClinicalTrials.gov NCT01245933.

Calibration to Mitigate Near-Field Antennas Effects for a MIMO Radar Imaging System.

A calibration method for a high-resolution hybrid MIMO turntable radar imaging system is presented. A line of small metal spheres is employed as a test pattern in the calibration process to measure the position shift caused by undesired antenna effects. The unwanted effects in the antenna near-field responses are analysed, modelled and significantly mitigated based on the symmetry and differences in the responses of the MIMO configuration.

N1-methyl-pseudouridine in mRNA enhances translation through eIF2α-dependent and independent mechanisms by increasing ribosome density.

Certain chemical modifications confer increased stability and low immunogenicity to in vitro transcribed mRNAs, thereby facilitating expression of therapeutically important proteins. Here, we demonstrate that N1-methyl-pseudouridine (N1mΨ) outperforms several other nucleoside modifications and their combinations in terms of translation capacity. Through extensive analysis of various modified transcripts in cell-free translation systems, we deconvolute the different components of the effect on protein expression independent of mRNA stability mechanisms. We show that in addition to turning off the immune/eIF2α phosphorylation-dependent inhibition of translation, the incorporated N1mΨ nucleotides dramatically alter the dynamics of the translation process by increasing ribosome pausing and density on the mRNA. Our results indicate that the increased ribosome loading of modified mRNAs renders them more permissive for initiation by favoring either ribosome recycling on the same mRNA or de novo ribosome recruitment.

Hepatitis B virus genome replication triggers toll-like receptor 3-dependent interferon responses in the absence of hepatitis B surface antigen.

The hepatitis B virus (HBV) has been described as stealth virus subverting immune responses initially upon infection. Impaired toll-like receptor signaling by the HBV surface antigen (HBsAg) attenuates immune responses to facilitate chronic infection. This implies that HBV replication may trigger host innate immune responses in the absence of HBsAg. Here we tested this hypothesis, using highly replicative transgenic mouse models. An HBV replication-dependent expression of antiviral genes was exclusively induced in HBsAg-deficient mice. These interferon responses attributed to toll-like receptor 3 (TLR3)-activated Kupffer and liver sinusoidal endothelial cells and further controlled the HBV genome replication. However, activation of TLR3 with exogenous ligands indicated additional HBs-independent immune evasion events. Our data demonstrate that in the absence of HBsAg, hepatic HBV replication leads to Tlr3-dependent interferon responses in non-parenchymal liver cells. We hypothesize that HBsAg is a major HBV-mediated evasion mechanism controlling endogenous antiviral responses in the liver. Eradication of HBsAg as a therapeutic goal might facilitate the induction of endogenous antiviral immune responses in patients chronically infected with HBV.

Context region discovery for automatic motion compensation in fluoroscopy.

PURPOSE: Image-based tracking for motion compensation is an important topic in image-guided interventions, as it enables physicians to operate in a less complex space. In this paper, we propose an automatic motion compensation scheme to boost image guidence power in transcatheter aortic valve implantation (TAVI). METHODS: The proposed tracking algorithm automatically discovers reliable regions that correlate strongly with the target. These discovered regions can assist to estimate target motion under severe occlusion, even if target tracker fails. RESULTS: We evaluate the proposed method for pigtail tracking during TAVI. We obtain significant improvement (12 %) over the baseline in a clinical dataset. Calcification regions are automatically discovered during tracking, which would aid TAVI processes. CONCLUSION: In this work, we open a new paradigm to provide dynamic real-time guidance for TAVI without user interventions, specially in case of severe occlusion where conventional tracking methods are challenged.

Probabilistic sparse matching for robust 3D/3D fusion in minimally invasive surgery.

Classical surgery is being overtaken by minimally invasive and transcatheter procedures. As there is no direct view or access to the affected anatomy, advanced imaging techniques such as 3D C-arm computed tomography (CT) and C-arm fluoroscopy are routinely used in clinical practice for intraoperative guidance. However, due to constraints regarding acquisition time and device configuration, intraoperative modalities have limited soft tissue image quality and reliable assessment of the cardiac anatomy typically requires contrast agent, which is harmful to the patient and requires complex acquisition protocols. We propose a probabilistic sparse matching approach to fuse high-quality preoperative CT images and nongated, noncontrast intraoperative C-arm CT images by utilizing robust machine learning and numerical optimization techniques. Thus, high-quality patient-specific models can be extracted from the preoperative CT and mapped to the intraoperative imaging environment to guide minimally invasive procedures. Extensive quantitative experiments on 95 clinical datasets demonstrate that our model-based fusion approach has an average execution time of 1.56 s, while the accuracy of 5.48 mm between the anchor anatomy in both images lies within expert user confidence intervals. In direct comparison with image-to-image registration based on an open-source state-of-the-art medical imaging library and a recently proposed quasi-global, knowledge-driven multi-modal fusion approach for thoracic-abdominal images, our model-based method exhibits superior performance in terms of registration accuracy and robustness with respect to both target anatomy and anchor anatomy alignment errors.

6DoF catheter detection, application to intracardiac echocardiography.

Hybrid imaging systems, consisting of fluoroscopy and echocardiography, are increasingly selected for intra-operative support of minimally invasive cardiac interventions. Intracardiac echocardiograpy (ICE) is an emerging modality with the promise of removing sedation or general anesthesia associated with transesophageal echocardiography (TEE). We introduce a novel 6 degrees of freedom (DoF) pose estimation approach for catheters (equipped with radiopaque ball markers) in single X-Ray fluoroscopy projection and investigate the method's application to a prototype ICE catheter. Machine learning based catheter detection is implemented in a Bayesian hypothesis fusion framework, followed by refinement of ball marker locations through template matching. Marker correspondence and 3D pose estimation are solved through iterative optimization. The method registers the ICE volume to the C-arm coordinate system. Experiments are performed on synthetic and porcine in-vivo data. Target registration error (TRE), defined in the echo cone, is the basis of our preliminary evaluation. The method reached 8.06 ± 7.2 mm TRE on 703 cases. Potential uses of our hybrid system include structural heart disease interventions and electrophysiologycal mapping or catheter ablation procedures.

2D/3D registration of TEE probe from two non-orthogonal C-arm directions.

2D/3D registration is a well known technique in medical imaging for combining pre-operative volume data with live fluoroscopy. A common issue of this type of algorithms is that out-of-plane parameters are hard to determine. One solution to overcome this issue is the use of X-ray images from two angulations. However, performing in-plane transformation in one image destroys the registration in the other image, particularly if the angulations are smaller than 90 degrees apart. Our main contribution is the automation of a novel registration approach. It handles translation and rotation of a volume in a way that in-plane parameters are kept invariant and independent of the angle offset between both projections in a double-oblique setting. Our approach yields more robust and partially faster registration results, compared to conventional methods, especially in case of object movement. It was successfully tested on clinical data for fusion of transesophageal ultrasound and X-ray.

Real-time ultrasound transducer localization in fluoroscopy images by transfer learning from synthetic training data.

The fusion of image data from trans-esophageal echography (TEE) and X-ray fluoroscopy is attracting increasing interest in minimally-invasive treatment of structural heart disease. In order to calculate the needed transformation between both imaging systems, we employ a discriminative learning (DL) based approach to localize the TEE transducer in X-ray images. The successful application of DL methods is strongly dependent on the available training data, which entails three challenges: (1) the transducer can move with six degrees of freedom meaning it requires a large number of images to represent its appearance, (2) manual labeling is time consuming, and (3) manual labeling has inherent errors. This paper proposes to generate the required training data automatically from a single volumetric image of the transducer. In order to adapt this system to real X-ray data, we use unlabeled fluoroscopy images to estimate differences in feature space density and correct covariate shift by instance weighting. Two approaches for instance weighting, probabilistic classification and Kullback-Leibler importance estimation (KLIEP), are evaluated for different stages of the proposed DL pipeline. An analysis on more than 1900 images reveals that our approach reduces detection failures from 7.3% in cross validation on the test set to zero and improves the localization error from 1.5 to 0.8mm. Due to the automatic generation of training data, the proposed system is highly flexible and can be adapted to any medical device with minimal efforts.

Multi-part modeling and segmentation of left atrium in C-arm CT for image-guided ablation of atrial fibrillation.

As a minimally invasive surgery to treat atrial fibrillation (AF), catheter based ablation uses high radio-frequency energy to eliminate potential sources of abnormal electrical events, especially around the ostia of pulmonary veins (PV). Fusing a patient-specific left atrium (LA) model (including LA chamber, appendage, and PVs) with electro-anatomical maps or overlaying the model onto 2-D real-time fluoroscopic images provides valuable visual guidance during the intervention. In this work, we present a fully automatic LA segmentation system on nongated C-arm computed tomography (C-arm CT) data, where thin boundaries between the LA and surrounding tissues are often blurred due to the cardiac motion artifacts. To avoid segmentation leakage, the shape prior should be exploited to guide the segmentation. A single holistic shape model is often not accurate enough to represent the whole LA shape population under anatomical variations, e.g., the left common PVs vs. separate left PVs. Instead, a part based LA model is proposed, which includes the chamber, appendage, four major PVs, and right middle PVs. Each part is a much simpler anatomical structure compared to the holistic one and can be segmented using a model-based approach (except the right middle PVs). After segmenting the LA parts, the gaps and overlaps among the parts are resolved and segmentation of the ostia region is further refined. As a common anatomical variation, some patients may contain extra right middle PVs, which are segmented using a graph cuts algorithm under the constraints from the already extracted major right PVs. Our approach is computationally efficient, taking about 2.6 s to process a volume with 256 × 256 × 245 voxels. Experiments on 687 C-arm CT datasets demonstrate its robustness and state-of-the-art segmentation accuracy.

Inosine-mediated modulation of RNA sensing by Toll-like receptor 7 (TLR7) and TLR8.

RNA-specific adenosine deaminase (ADAR)-mediated adenosine-to-inosine (A-to-I) editing is a critical arm of the antiviral response. However, mechanistic insights into how A-to-I RNA editing affects viral infection are lacking. We posited that inosine incorporation into RNA facilitates sensing of nonself RNA by innate immune sensors and accordingly investigated the impact of inosine-modified RNA on Toll-like receptor 7 and 8 (TLR7/8) sensing. Inosine incorporation into synthetic single-stranded RNA (ssRNA) potentiated tumor necrosis factor alpha (TNF-α) or alpha interferon (IFN-α) production in human peripheral blood mononuclear cells (PBMCs) in a sequence-dependent manner, indicative of TLR7/8 recruitment. The effect of inosine incorporation on TLR7/8 sensing was restricted to immunostimulatory ssRNAs and was not seen with inosine-containing short double-stranded RNAs or with a deoxy-inosine-modified ssRNA. Inosine-mediated increase of self-secondary structure of an ssRNA resulted in potentiated IFN-α production in human PBMCs through TLR7 recruitment, as established through the use of a TLR7 antagonist and Tlr7-deficient cells. There was a correlation between hyperediting of influenza A viral ssRNA and its ability to stimulate TNF-α, independent of 5'-triphosphate residues, and involving Adar-1. Furthermore, A-to-I editing of viral ssRNA directly enhanced mouse Tlr7 sensing, when present in proportions reproducing biologically relevant levels of RNA editing. Thus, we demonstrate for the first time that inosine incorporation into immunostimulatory ssRNA can potentiate TLR7/8 activation. Our results suggest a novel function of A-to-I RNA editing, which is to facilitate TLR7/8 sensing of phagocytosed viral RNA.

Chemical modifications on siRNAs avoid Toll-like-receptor-mediated activation of the hepatic immune system in vivo and in vitro.

OBJECTIVES: The therapeutic application of small interfering RNAs (siRNAs) is limited by the induction of severe off-target effects, especially in the liver. Therefore, we assessed the potential of differently modified siRNAs to induce the hepatic innate immune system in vitro and in vivo. METHODS: Primary isolated liver cells were transfected with siRNAs against apolipoprotein B1 (APOB1), luciferase (LUC) or galactosidase (GAL). For in vivo use, siRNAs were formulated in lipid nanoparticles (LNPs) and administered intravenously to C57BL/6 mice. Liver tissue was collected 6-48 h after injection and knock-down efficiency or immune responses were determined by quantitative reverse-transcription-linked PCR. RESULTS: Unmodified GAL siRNA transiently induced the expression of TNF-α, IL-6, IL-10, IFN-ß and IFN-sensitive gene 15 in vivo, whereas a formulation of 2'-O-methylated-LUC siRNA had no such effects. Formulation of unmodified APOB1-specific siRNA suppressed APOB1 mRNA levels by ~80% in the liver 48h after application. The results were paralleled in vitro, where transfection of liver cells with unmodified siRNAs, but not with chemically modified siRNAs, led to cell-type-specific induction of immune genes. These immune responses were not observed in MYD88-deficient mice or in chloroquine-treated cells in vitro. CONCLUSIONS: Our data indicate that siRNAs activate endosomal Toll-like receptors in different liver-derived cell types to various degrees, in vitro. LNP-formulated siRNA selectively leads to hepatic knock-down of target genes in vivo. Here, off-target immune responses are restricted to non-parenchymal liver cells. However, 2'-O-methyl modifications of siRNA largely avoid immune-stimulatory effects, which is a crucial prerequisite for the development of safe and efficient RNA-interference-based therapeutics.

Integrated spatiotemporal analysis for automatic contrast agent inflow detection on angiography and fluoroscopy during transcatheter aortic valve implantation.

PURPOSE: In this paper, the authors propose an integrated spatial and temporal analysis for automatic detection of contrast agent inflow at the aortic root on fluoroscopic and angiographic sequences during transcatheter aortic valve implantation procedures as a means to automatically trigger registration of 3D aortic models. METHODS: The proposed contrast agent inflow detection method is based on a contrast feature curve, calculated using histogram analysis and a likelihood ratio test. Several image preprocessing steps are performed to enhance the properties of the contrast feature curve. For sequences with a dominant peak on its contrast feature curve, a cascaded classifier is then applied to differentiate the contrast-enhanced aorta from contrast-enhanced balloons. Finally, a multilayer classifier based on sparse Gabor features is used to recognize sequences containing a faint contrast-enhanced aorta. RESULTS: The algorithm was evaluated using 105 sequences consisting of more than 12,000 frames, and achieved a detection accuracy of 99.1% (100% sensitivity and 98.5% specificity). The computation time for a typical sequence of 150 frames was ≈ 1 s on a single-core Dell PC with a 1 GHz Intel Xeon processor and 2 GB of RAM. CONCLUSIONS: The authors developed a novel, automatic method for contrast agent inflow detection on x-ray sequences. With the achieved efficiency and accuracy, the proposed method is potentially feasible for clinical use as it facilitates a seamless workflow in utilizing patient-specific 3D models to provide anatomical details during transcatheter aortic valve implantation procedures.

Hepatocyte-targeted RNAi therapeutics for the treatment of chronic hepatitis B virus infection.

RNA interference (RNAi)-based therapeutics have the potential to treat chronic hepatitis B virus (HBV) infection in a fundamentally different manner than current therapies. Using RNAi, it is possible to knock down expression of viral RNAs including the pregenomic RNA from which the replicative intermediates are derived, thus reducing viral load, and the viral proteins that result in disease and impact the immune system's ability to eliminate the virus. We previously described the use of polymer-based Dynamic PolyConjugate (DPC) for the targeted delivery of siRNAs to hepatocytes. Here, we first show in proof-of-concept studies that simple coinjection of a hepatocyte-targeted, N-acetylgalactosamine-conjugated melittin-like peptide (NAG-MLP) with a liver-tropic cholesterol-conjugated siRNA (chol-siRNA) targeting coagulation factor VII (F7) results in efficient F7 knockdown in mice and nonhuman primates without changes in clinical chemistry or induction of cytokines. Using transient and transgenic mouse models of HBV infection, we show that a single coinjection of NAG-MLP with potent chol-siRNAs targeting conserved HBV sequences resulted in multilog repression of viral RNA, proteins, and viral DNA with long duration of effect. These results suggest that coinjection of NAG-MLP and chol-siHBVs holds great promise as a new therapeutic for patients chronically infected with HBV.

Conformational rearrangements of RIG-I receptor on formation of a multiprotein:dsRNA assembly.

The retinoic acid inducible gene-I (RIG-I)-like family of receptors is positioned at the front line of our innate cellular defence system. RIG-I detects and binds to foreign duplex RNA in the cytoplasm of both immune and non-immune cells, and initiates the induction of type I interferons and pro-inflammatory cytokines. The mechanism of RIG-I activation by double-stranded RNA (dsRNA) involves a molecular rearrangement proposed to expose the N-terminal pair of caspase activation recruitment domains, enabling an interaction with interferon-beta promoter stimulator 1 (IPS-1) and thereby initiating downstream signalling. dsRNA is particularly stimulatory when longer than 20 bp, potentially through allowing binding of more than one RIG-I molecule. Here, we characterize full-length RIG-I and RIG-I subdomains combined with a stimulatory 29mer dsRNA using multi-angle light scattering and size-exclusion chromatography-coupled small-angle X-ray scattering, to build up a molecular model of RIG-I before and after the formation of a 2:1 protein:dsRNA assembly. We report the small-angle X-ray scattering-derived solution structure of the human apo-RIG-I and observe that on binding of RIG-I to dsRNA in a 2:1 ratio, the complex becomes highly extended and flexible. Hence, here we present the first model of the fully activated oligomeric RIG-I.

Image-based computational models for TAVI planning: from CT images to implant deployment.

Transcatheter aortic valve implantation (TAVI) is becoming the standard choice of care for non-operable patients suffering from severe aortic valve stenosis. As there is no direct view or access to the affected anatomy, accurate preoperative planning is crucial for a successful outcome. The most important decision during planning is selecting the proper implant type and size. Due to the wide variety in device sizes and types and non-circular annulus shapes, there is often no obvious choice for the specific patient. Most clinicians base their final decision on their previous experience. As a first step towards a more predictive planning, we propose an integrated method to estimate the aortic apparatus from CT images and compute implant deployment. Aortic anatomy, which includes aortic root, leaflets and calcifications, is automatically extracted using robust modeling and machine learning algorithms. Then, the finite element method is employed to calculate the deployment of a TAVI implant inside the patient-specific aortic anatomy. The anatomical model was evaluated on 198 CT images, yielding an accuracy of 1.30 +/- 0.23 mm. In eleven subjects, pre- and post-TAVI CT images were available. Errors in predicted implant deployment were of 1.74 +/- 0.40 mm in average and 1.32 mm in the aortic valve annulus region, which is almost three times lower than the average gap of 3 mm between consecutive implant sizes. Our framework may thus constitute a surrogate tool for TAVI planning.

Identification of proteins that mediate the pro-viral functions of the interferon stimulated gene 15 in hepatitis C virus replication.

In previous studies we identified the interferon stimulated gene 15 (ISG15) as a pro-viral host factor in the pathogenesis of hepatitis C virus (HCV) infection. However, the functional link between ISG15 and the HCV replication cycle is not well understood. Aim of the present study was to functionally analyze the role of ISG15 and to identify possible HCV promoting effector molecules. Isg15 suppression was investigated in the murine subgenomic HCV replicon (MH1) transfected with Isg15-specific siRNA and in C57BL/6 mice intravenously injected with lipid nanoparticles (LNP)-formulated siRNA. Interestingly, the LNP-formulated siRNA led to hepatocyte-specific knockdown of Isg15 in vivo, which mediated a hypo-responsiveness to endogenous and exogenous interferon. A label free proteome analysis accompanied by western blot and quantitative RT-PCR techniques led to identification of five candidate proteins (Heterogeneous nuclear ribonucleoprotein A3 (HnrnpA3), Heterogeneous nuclear ribonucleoprotein K (HnrnpK), Hydroxymethylglutaryl-CoA synthase (Hmgcs1), Isocitrate dehydrogenase cytoplasmic (Idh1) and Thioredoxin domain-containing protein 5 (Txndc5)) that are either involved in lipid metabolism or belong to the family of Heterogeneous nuclear ribonucleoprotein (Hnrnp). All candidate proteins are likely to be associated with the HCV replication complex. Furthermore treatment with HnrnpK-specific siRNA directly suppressed HCV replication in vitro. Taken together these data suggest that targeting Isg15 may represent an attractive novel therapeutic option for the treatment of chronic HCV infection.

Robust model-based 3d/3D fusion using sparse matching for minimally invasive surgery.

Classical surgery is being disrupted by minimally invasive and transcatheter procedures. As there is no direct view or access to the affected anatomy, advanced imaging techniques such as 3D C-arm CT and C-arm fluoroscopy are routinely used for intra-operative guidance. However, intra-operative modalities have limited image quality of the soft tissue and a reliable assessment of the cardiac anatomy can only be made by injecting contrast agent, which is harmful to the patient and requires complex acquisition protocols. We propose a novel sparse matching approach for fusing high quality pre-operative CT and non-contrasted, non-gated intra-operative C-arm CT by utilizing robust machine learning and numerical optimization techniques. Thus, high-quality patient-specific models can be extracted from the pre-operative CT and mapped to the intra-operative imaging environment to guide minimally invasive procedures. Extensive quantitative experiments demonstrate that our model-based fusion approach has an average execution time of 2.9 s, while the accuracy lies within expert user confidence intervals.

Learning without labeling: domain adaptation for ultrasound transducer localization.

The fusion of image data from trans-esophageal echography (TEE) and X-ray fluoroscopy is attracting increasing interest in minimally-invasive treatment of structural heart disease. In order to calculate the needed transform between both imaging systems, we employ a discriminative learning based approach to localize the TEE transducer in X-ray images. Instead of time-consuming manual labeling, we generate the required training data automatically from a single volumetric image of the transducer. In order to adapt this system to real X-ray data, we use unlabeled fluoroscopy images to estimate differences in feature space density and correct covariate shift by instance weighting. An evaluation on more than 1900 images reveals that our approach reduces detection failures by 95% compared to cross validation on the test set and improves the localization error from 1.5 to 0.8 mm. Due to the automatic generation of training data, the proposed system is highly flexible and can be adapted to any medical device with minimal efforts.