Search

Competing Interests: Disclosures The authors have no conflicts of interest to disclose.

Surgical correction of severe mitral regurgitation (MR) can reverse left ventricular (LV) remodeling in patients with mitral valve prolapse (MVP). However, whether this process is similar to the case in Barlow's Disease (BD) and Fibro-elastic Deficiency (FED) is currently unknown. The aim of this study is to evaluate post-operative LV reverse remodeling and function in patients with BD versus FED. In this study, 100 MVP patients (BD = 37 and FED = 63) with severe MR who underwent mitral valve surgery at three Belgian centers were retrospectively included. Transthoracic echocardiography was used to assess MR severity, LV volumes and function before surgery and 6 months thereafter. Baseline MR severity, LV ejection fraction (LVEF), indexed LV end-diastolic (LVEDVi) and end-systolic volumes (LVESVi) were not different between the groups. After a median follow-up of 278 days, there was a similar decrease in LVEDVi, but a trend towards a smaller decrease in LVESVi in BD compared to FED (-3.0 ± 11.2 mL/m 2 vs. -5.3 ± 9.0 mL/m 2 ; p = 0.154). This resulted in a significantly larger decrease in LVEF in BD (-8.3 ± 9.6%) versus FED (-3.9 ± 6.9%) after adjusting for baseline LVEF ( p < 0.001) and type of surgical intervention ( p = 0.01). These findings suggest that LV (reverse) remodeling in BD could be affected by other mechanisms beyond volume overload, potentially involving concomitant cardiomyopathy.

Aim: To provide a detailed description of the workflow at our telecardiology centre and to analyse the workload of real-world remote monitoring with the aim to assess the feasibility to outsource this service., Methods: A retrospective analysis was conducted on the telecardiology service provided at the University Hospitals of Leuven by extracting patient demographic data, general time usage and detailed information about the type of remote contacts. 10,869 contacts in 948 patients have been included. A 2-week prospective study was conducted on the same service by documenting and monitoring every action performed by specialised nurses when analysing and solving remote monitoring transmissions. 337 contacts in 262 patients were collected during this period., Results: Both analyses indicated similar numbers of events and interventions. Unplanned transmissions were more challenging and required more interventions than planned transmissions. Relatively little time (retrospective median: 1.83 min; prospective median: 1.56 min, per event) was spent on incoming non-actionable 'normal' transmissions (retrospective: 46%; prospective: 40% of all events). Retrospectively 54% and prospectively 60% of transmissions showed abnormalities and were responsible for most of the time expended. Disease-related issues were the most frequent cause for these 'abnormal' alerts. Contacting patients and physicians were key interventions undertaken. Interaction initiated by patients mainly involved the installation process (42%) and bedside monitoring problems (32%)., Conclusion: External data centres could deal with 40% of the transmissions, but the decline in workload would be negligible for the in-hospital remote monitoring team, because very little time is spent dealing with the many 'non-event' transmissions whereas most of the time is spent solving clinical problems. Providing sufficient resources and optimising communication protocols is necessary to aid in managing the workload of the remote monitoring team., Implications for practiceContacting patients and physicians are key interventions for specialist nurses in remote monitoring centres.Detailed timing confirmed that most time was spent on relevant disease-related clinical problems.Despite dealing with ∼40% of transmissions, outsourcing to external data centres would decrease the workload only by 15-25%.Patient initiated contacts with questions concerning remote monitoring form a high burden and should be countered by scaling the service and creating communication protocols.

This paper presents new high-frequency data on trade policy changes targeting medical and food products since the beginning of the COVID-19 pandemic, documenting how countries used trade policy instruments in response to the health crisis on a week-by-week basis. The data set reveals a rapid increase in trade policy activism in February and March 2020 in tandem with the rise in COVID-19 cases but also uncovers extensive heterogeneity across countries in both their use of trade policy and the types of measures used. Some countries acted to restrict exports and facilitate imports, others targeted only one of these margins, and many did not use trade policy at all. The observed heterogeneity suggests numerous research questions on the drivers of trade policy responses to COVID-19, on the effects of these measures on trade and prices of critical products, and on the role of trade agreements in influencing the use of trade policy., (© 2021 John Wiley & Sons Ltd.)

This paper analyses a prominent dimension of the initial policy response to the COVID-19 pandemic observed in many countries: the imposition of export restrictions and actions to facilitate imports. Using weekly data on the use of trade policy instruments during the first seven months of the COVID-19 pandemic (January-July, 2020), we assess the relationship between the use of trade policy instruments and attributes of pre-crisis public procurement regulation. Controlling for country size, government effectiveness and economic factors, we find that use of export restrictions targeting medical products is strongly positively correlated with the total number of steps and average time required to complete procurement processes in the pre-crisis period. Membership of trade agreements encompassing public procurement disciplines is associated with actions to facilitate trade in medical products. These findings suggest future empirical assessments of the drivers of trade policy during the pandemic should consider public procurement systems., (© 2021 John Wiley & Sons Ltd.)

Data processing forms an integral part of biomarker discovery and contributes significantly to the ultimate result. To compare and evaluate various publicly available open source label-free data processing workflows, we developed msCompare, a modular framework that allows the arbitrary combination of different feature detection/quantification and alignment/matching algorithms in conjunction with a novel scoring method to evaluate their overall performance. We used msCompare to assess the performance of workflows built from modules of publicly available data processing packages such as SuperHirn, OpenMS, and MZmine and our in-house developed modules on peptide-spiked urine and trypsin-digested cerebrospinal fluid (CSF) samples. We found that the quality of results varied greatly among workflows, and interestingly, heterogeneous combinations of algorithms often performed better than the homogenous workflows. Our scoring method showed that the union of feature matrices of different workflows outperformed the original homogenous workflows in some cases. msCompare is open source software (https://trac.nbic.nl/mscompare), and we provide a web-based data processing service for our framework by integration into the Galaxy server of the Netherlands Bioinformatics Center (http://galaxy.nbic.nl/galaxy) to allow scientists to determine which combination of modules provides the most accurate processing for their particular LC-MS data sets.

We present a new proteomics analysis pipeline focused on maximizing the dynamic range of detected molecules in liquid chromatography-mass spectrometry (LC-MS) data and accurately quantifying low-abundance peaks to identify those with biological relevance. Although there has been much work to improve the quality of data derived from LC-MS instruments, the goal of this study was to extend the dynamic range of analyzed compounds by making full use of the information available within each data set and across multiple related chromatograms in an experiment. Our aim was to distinguish low-abundance signal peaks from noise by noting their coherent behavior across multiple data sets, and central to this is the need to delay the culling of noise peaks until the final peak-matching stage of the pipeline, when peaks from a single sample appear in the context of all others. The application of thresholds that might discard signal peaks early is thereby avoided, hence the name TAPP: threshold-avoiding proteomics pipeline. TAPP focuses on quantitative low-level processing of raw LC-MS data and includes novel preprocessing, peak detection, time alignment, and cluster-based matching. We demonstrate the performance of TAPP on biologically relevant sample data consisting of porcine cerebrospinal fluid spiked over a wide range of concentrations with horse heart cytochrome c., (© 2011 American Chemical Society)

Unlabelled: Warp2D is a novel time alignment approach, which uses the overlapping peak volume of the reference and sample peak lists to correct misleading peak shifts. Here, we present an easy-to-use web interface for high-throughput Warp2D batch processing time alignment service using the Dutch Life Science Grid, reducing processing time from days to hours. This service provides the warping function, the sample chromatogram peak list with adjusted retention times and normalized quality scores based on the sum of overlapping peak volume of all peaks. Heat maps before and after time alignment are created from the arithmetic mean of the sum of overlapping peak area rearranged with hierarchical clustering, allowing the quality control of the time alignment procedure. Taverna workflow and command line tool are provided for remote processing of local user data., Availability: online data processing service is available at http://www.nbpp.nl/warp2d.html. Taverna workflow is available at myExperiment with title '2D Time Alignment-Webservice and Workflow' at http://www.myexperiment.org/workflows/1283.html. Command line tool is available at http://www.nbpp.nl/Warp2D&#95;commandline.zip., Contact: p.l.horvatovich@rug.nl, Supplementary Information: Supplementary data are available at Bioinformatics online.

Multidimensional chromatography coupled to mass spectrometry (LC(n)-MS) provides more separation power and an extended measured dynamic concentration range to analyse complex proteomics samples than one dimensional liquid chromatography coupled to mass spectrometry (1D-LC-MS). This review gives an overview of the most important aspects of LC(n)-MS with respect to optimizing peak capacity and evaluate orthogonality. We review recent developments in LC(n)-MS to analyse proteomics samples from the analyst point of view and give an overview over methods and future developments to process LC(n)-MS data for comprehensive differential protein expression profiling. Examples from our research, such as combining protein fractionation using high temperature reverse phase (RP) columns followed by analysis of the trypsin-digested fractions by RP LC-MS, serve to highlight possibilities and shortcomings of present-day approaches. Other LC(n)-MS systems that have been used to analyse highly complex shotgun proteomic samples, such as the combination of RP columns using low and high pH eluents or the combination of hydrophilic interaction liquid chromatography (HILIC) with RP-MS is discussed in detail.

We describe a platform for the comparative profiling of urine using reversed-phase liquid chromatography-mass spectrometry (LC-MS) and multivariate statistical data analysis. Urinary compounds were separated by gradient elution and subsequently detected by electrospray Ion-Trap MS. The lower limit of detection (5.7-21 nmol/L), within-day (2.9-19%) and between-day (4.8-19%) analytical variation of peak areas, linearity (R2: 0.918-0.999), and standard deviation for retention time (<0.52 min) of the method were assessed by means of addition of seven 3-8 amino acid peptides (0-500 nmol/L). Relating the amount of injected urine to the area under the curve (AUC) of the chromatographic trace at 214 nm better reduced the coefficient of variation (CV) of the AUC of the total ion chromatogram (CV = 10.1%) than relating it to creatinine (CV = 38.4%). LC-MS data were processed, and the common peak matrix was analyzed by principal component analysis (PCA) after supervised classification by the nearest shrunken centroid algorithm. The feasibility of the method to discriminate urine samples of differing compositions was evaluated by (i) addition of seven peptides at nanomolar concentrations to blank urine samples of different origin and (ii) a study of urine from kidney patients with and without proteinuria. (i) The added peptides were ranked as highly discriminatory peaks despite significant biological variation. (ii) Ninety-two peaks were selected best discriminating proteinuric from nonproteinuric samples, of which 6 were more intense in the majority of the proteinuric samples. Two of these 6 peaks were identified as albumin-derived peptides, which is in accordance with the early rise of albumin during glomerular proteinuria. Interestingly, other albumin-derived peptides were nondiscriminatory indicating preferential proteolysis at some cleavage sites.