The diagnosis of hypertrophic cardiomyopathy by cardiovascular magnetic resonance

Hypertrophic cardiomyopathy (HCM) is the most common genetic disease of the heart. HCM is characterized by a wide range of clinical expression, ranging from asymptomatic mutation carriers to sudden cardiac death as the first manifestation of the disease. Over 1000 mutations have been identified, classically in genes encoding sarcomeric proteins. Noninvasive imaging is central to the diagnosis of HCM and cardiovascular magnetic resonance (CMR) is increasingly used to characterize morphologic, functional and tissue abnormalities associated with HCM. The purpose of this review is to provide an overview of the clinical, pathological and imaging features relevant to understanding the diagnosis of HCM. The early and overt phenotypic expression of disease that may be identified by CMR is reviewed. Diastolic dysfunction may be an early marker of the disease, present in mutation carriers prior to the development of left ventricular hypertrophy (LVH). Late gadolinium enhancement by CMR is present in approximately 60% of HCM patients with LVH and may provide novel information regarding risk stratification in HCM. It is likely that integrating genetic advances with enhanced phenotypic characterization of HCM with novel CMR techniques will importantly improve our understanding of this complex disease

Exploring Proteus mirabilis methionine tRNA synthetase active site: homology model construction, molecular dynamics, pharmacophore and docking validation

Currently, the treatment of Proteus mirabilis infections is considered to be complicated as the organism has become resistant to numerous antibiotic classes. Therefore, new inhibitors should be developed, targeting bacterial molecular functions. Methionine tRNA synthetase (MetRS), a member of the aminoacyl-tRNA synthetase family, is essential for protein biosynthesis offering a promising target for novel antibiotics discovery. In the context of computer-aided drug design (CADD), the current research presents the construction and analysis of a comparative homology model for P. mirabilis MetRS, enabling development of novel inhibitors with greater selectivity. Molecular Operating Environment (MOE) software was used to build a homology model for P. mirabilis MetRS using Escherichia coli MetRS as a template. The model was evaluated, and the active site of the target protein predicted from its sequence using conservation analysis. Molecular dynamic simulations were performed to evaluate the stability of the modeled protein structure. In order to evaluate the predicted active site interactions, methionine (the natural substrate of MetRS) and several inhibitors of bacterial MetRS were docked into the constructed model using MOE. After validation of the model, pharmacophore-based virtual screening for a systemically prepared dataset of compounds was performed to prove the feasibility of the proposed model, identifying possible parent compounds for further development of MetRS inhibitors against P. mirabilis

INS/GPS/LiDAR integrated navigation system for urban and indoor environments using hybrid scan matching algorithm

This paper takes advantage of the complementary characteristics of Global Positioning System (GPS) and Light Detection and Ranging (LiDAR) to provide periodic corrections to Inertial Navigation System (INS) alternatively in different environmental conditions. In open sky, where GPS signals are available and LiDAR measurements are sparse, GPS is integrated with INS. Meanwhile, in confined outdoor environments and indoors, where GPS is unreliable or unavailable and LiDAR measurements are rich, LiDAR replaces GPS to integrate with INS. This paper also proposes an innovative hybrid scan matching algorithm that combines the feature-based scan matching method and Iterative Closest Point (ICP) based scan matching method. The algorithm can work and transit between two modes depending on the number of matched line features over two scans, thus achieving efficiency and robustness concurrently. Two integration schemes of INS and LiDAR with hybrid scan matching algorithm are implemented and compared. Real experiments are performed on an Unmanned Ground Vehicle (UGV) for both outdoor and indoor environments. Experimental results show that the multi-sensor integrated system can remain sub-meter navigation accuracy during the whole trajectory

Adaptive covariance estimation method for LiDAR-Aided multi-sensor integrated navigation systems

The accurate estimation of measurements covariance is a fundamental problem in sensors fusion algorithms and is crucial for the proper operation of filtering algorithms. This paper provides an innovative solution for this problem and realizes the proposed solution on a 2D indoor navigation system for unmanned ground vehicles (UGVs) that fuses measurements from a MEMS-grade gyroscope, speed measurements and a light detection and ranging (LiDAR) sensor. A computationally efficient weighted line extraction method is introduced, where the LiDAR intensity measurements are used, such that the random range errors and systematic errors due to surface reflectivity in LiDAR measurements are considered. The vehicle pose change is obtained from LiDAR line feature matching, and the corresponding pose change covariance is also estimated by a weighted least squares-based technique. The estimated LiDAR-based pose changes are applied as periodic updates to the Inertial Navigation System (INS) in an innovative extended Kalman filter (EKF) design. Besides, the influences of the environment geometry layout and line estimation error are discussed. Real experiments in indoor environment are performed to evaluate the proposed algorithm. The results showed the great consistency between the LiDAR-estimated pose chan

Contraception use among Muslim women in Alexandria, Egypt: a descriptive pilot study

Background: This pilot study aimed to create a questionnaire survey directed to understand knowledge gaps related to contraception among Muslim women in Alexandria, Egypt, so potential interventions could be designed to enable more informed decision-making. The project was a mixed-method, cross-sectional study using a questionnaire survey.Methods: Participants were randomly selected at outpatient clinics at Alexandria university hospitals in September and October 2020. The inclusion criteria were to be an 18 year old or older woman and able to give consent. The recruitment goal for this pilot study was 100 participants. The consented participants were interviewed about demographics, socio-structural and contraception use. The questionnaire was tested using a focus group of 11 women. The study data was collected using KoBoToolbox and exported to the SPSS software for descriptive analysis. The primary outcome was to validate the survey questionnaire and the secondary outcome to assess knowledge regarding contraception methods and emergency contraception.Results: The age of study participants ranged from 18-60 with a mean of 34 years. Almost all participants had previously heard of various contraceptive methods and 75% used them before. The majority did not know about emergency contraception. Most respondents had a favorable attitude toward family planning, and their primary sources of information were family and friends.Conclusions: Preliminary findings show that most women knew about contraception methods, though few of them heard of emergency contraception. Because of the patriarchal nature of Egyptian society, family planning education should target the whole population

The merit of high-frequency data in portfolio allocation

This paper addresses the open debate about the usefulness of high-frequency (HF) data in large-scale portfolio allocation. Daily covariances are estimated based on HF data of the S&P 500 universe employing a blocked realized kernel estimator. We propose forecasting covariance matrices using a multi-scale spectral decomposition where volatilities, correlation eigenvalues and eigenvectors evolve on different frequencies. In an extensive out-of-sample forecasting study, we show that the proposed approach yields less risky and more diversified portfolio allocations as prevailing methods employing daily data. These performance gains hold over longer horizons than previous studies have shown

STROCSS 2021: Strengthening the reporting of cohort, cross-sectional and case-control studies in surgery

Introduction: Strengthening The Reporting Of Cohort Studies in Surgery (STROCSS) guidelines were developed in 2017 in order to improve the reporting quality of observational studies in surgery and updated in 2019. In order to maintain relevance and continue upholding good reporting quality among observational studies in surgery, we aimed to update STROCSS 2019 guidelines. / Methods: A STROCSS 2021 steering group was formed to come up with proposals to update STROCSS 2019 guidelines. An expert panel of researchers assessed these proposals and judged whether they should become part of STROCSS 2021 guidelines or not, through a Delphi consensus exercise. / Results: 42 people (89%) completed the DELPHI survey and hence participated in the development of STROCSS 2021 guidelines. All items received a score between 7 and 9 by greater than 70% of the participants, indicating a high level of agreement among the DELPHI group members with the proposed changes to all the items. / Conclusion: We present updated STROCSS 2021 guidelines to ensure ongoing good reporting quality among observational studies in surgery