Intrauterine Misoprostol versus intravenous Oxytocin infusion during cesarean delivery to reduce intraoperative and postoperative blood loss: a randomised clinical trial

Background: The objective of the present study was to compare the efficacy of intrauterine misoprostol with intravenous oxytocin infusion in reducing blood loss during and after cesarean section (CS).Methods: An open, randomized, clinical trial, registered (ClinicalTrials.gov ID: NCT03148574) conducted between July 1, 2017 and April 1, 2018. The study included 240 pregnant females that were recruited at term (37-40 weeks) gestation scheduled for either elective or emergency CS. Eligible participants were randomly allocated into two equal groups: Group A: patients who receive intravenous infusion of 10 I.U diluted to 500ml of normal saline for 30 minute after delivery. Group B: patients received 400μg misoprostol intrauterine just after cord clamping and delivery of the placenta. Primary outcome measure was assessment of amount of intraoperative and postoperative blood loss.Results: The intraoperative and 2h postoperative blood loss in the misoprostol group was higher than oxytocin group (p<0.001). Hemoglobin level decreased significantly among both groups, manifested by the highly significant p value in comparison of pre and postoperative Hb level in the two groups (p<0.001). However, the blood loss in the misoprostol group was higher than oxytocin group (p=0.004). There was a statistical significant differences between both groups as regards the need for additional uterotonic drug (66% in misoprostol group vs 5% in oxytocin group, P<0.001). Shivering and pyrexia were more in common in the misoprostol group while vomiting, headache and giddiness were significantly higher among oxytocin group.Conclusions: Administration of misoprostol 400mcg through intrauterine route appears to be less effective than intravenous oxytocin infusion in reducing blood loss during and after CS

Methionine sulfoxide reductases of Aspergillus nidulans

Methionine sulfoxide reductases (Msr) are repair proteins, which provide an important part of the cellular antioxidant defense. Methionine oxidation results in a diasteromeric mixture of methionine-S-sulfoxide (Met-S-O) and methionine-R-sulfoxide (Met-R-O), requiring MsrA enzymes to reduce Met-S-O and MsrBs to reduce Met-R-O. Here we explored the filamentous fungus Aspergillus nidulans as a model organism for studies on Msr enzymes. The Msr system of A. nidulans was found to comprise three enzymes: AnMsrA, AnMsrB, and AnfRMsr. Single-knockout strains for all three enzymes showed reduced viability under oxidative stress conditions (H2O2, Chloramine-T, menadione). In addition, all three msr genes were transcriptionally upregulated by oxidative stress, underscoring the relevance of the Msr enzymes for the stress response of the fungus. Biochemical characterization of recombinant AnMsrs showed that MsrA reduces both, free and peptide-bound Met-S-O, while MsrB reduces Met-R-O with a strong preference for peptide substrates over free Met-R-O. By contrast, fRMsr is an MsrB-type enzyme that only accepts free Met-R-O as substrate. The predicted active centers for all three enzymes were confirmed by site-directed mutagenesis of the catalytic Cys residues. The peptide substrate specificity of MsrA and MsrB from A. nidulans and of the corresponding human Msrs was studied, using a set of peptides with systematic variation of the residues flanking the oxidized methionine. MsrA activity was strongly reduced by acidic residues in these neighboring positions whereas MsrBs were less sensitive to the type of flanking amino acids. Site-directed mutagenesis around the catalytic cysteine of AnMsrA identified two negatively charged residues (Glu99 and Asp134 in) to have strong impact on the MsrA peptide selectivity. A. nidulans proved as a suitable eukaryotic model organism for studies of Msr enzymes that allows easy genetic manipulation and functional analysis

The potentials for corporatization of public hospitals: The case of Egypt

With changing health landscape across the globe, increasing burden of chronic diseases, increasing citizens expectations that accompanied by cost limitations, health reform becomes inevitable for Egyptian health system to maximize benefits and overcome challenges. Reform might include healthcare service provision, health policies, workforce planning or public health programs strategies. New Public Management concepts and principles represent the basis for many of reform plans since its emerging in the early 1990s. Many countries have used New Public Management guiding principles to shape its health reform program. Egypt is undergoing a reform plan across the whole sectors through Egypt 2030 plan announced by Ministry of Planning and Administrative reform. The reform plan has identified a set of goals for the health sector to achieve and another set of indicators to measure the progress and level of achievement. This paper presents corporatization of public hospital as a tool that can fit into the new reform program. With the execution of universal health coverage, the autonomy of hospitals will help to achieve the targeted level of performance, efficiency, and quality of services. The main challenge facing implementation is the high percentage of poverty in Egypt and their dependency on the government hospitals to get healthcare services. Transforming these hospitals into revenue generating organization will affect accessibility except there is a social insurance scheme that can protect poor against the commercialization of healthcare services. Research question: is corporatization improving performance, increasing accessibility and enhancing the quality of healthcare services? Methodology: qualitative research where semi-structured interviews were conducted with healthcare professional and system experts locally and globally to get their views on the feasibility of implementation of such reform in Egypt. Conclusion: corporatization of public hospitals in Egypt represents a fair organizational reform strategy for Egyptian health system to increase efficiency and satisfaction. Yet, a rigorous readiness assessment of the system components (regulations, providers, payers, and beneficiaries) should be executed to measure the readiness for implementation

Evaluation of serum transferrin receptor level in children undergoing regular hemodialysis

Background: Patients with chronic kidney disease (CKD) are at risk for anemia as a result of a variety of factors. Blood ferritin levels are an indicator of iron, while blood transferrin receptor (sTfR) levels are an indicator of how much iron is available for cells to use.Objective: It was the goal of this work to determine the diagnostic value of serum soluble transferrin receptor (sTfR) in children undergoing regular hemodialysis in Pediatric Nephrology Unit in Zagazig University.Patients and Methods: Our study was applied on 44 children admitted to Pediatric Nephrology Unit at the Pediatric Department in Zagazig University Children Hospital for hemodialysis, during the period from January 2019 to July 2019. Iron profile (serum Iron, ferritin, total iron-binding capacity (TIBC) and serum transferrin) as well as transferrin saturation-sTfRs TfR/log ferritin index was done to all children.Results: Statistically significant positive association between iron, ferritin, total saturation of transferrin (TSAT) and hemoglobin (Hb) as well as statistically significant negative correlation between TSAT, iron, ferritin and sTfR were found. Anemia of chronic disease (ACD) patients' dialysis time was much longer than that of (Iron deficiency anemia) IDA patients, while hypertension was significantly higher in IDA patients than in ACD patients. The optimal cutoff value for sTfR was (1.75) with a sensitivity of 82% and a specificity of 73.6 %.Conclusion: STfR is a valuable metric for distinguishing between ACD and IDA, as well as between ACD in patients who get regular hemodialysis. In HD patients, sTfR can be utilized to check iron levels

Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework

This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.Peer ReviewedPostprint (published version

Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses

Variational dimensionality reduction methods are known for their high accuracy, generative abilities, and robustness. We introduce a framework to unify many existing variational methods and design new ones. The framework is based on an interpretation of the multivariate information bottleneck, in which an encoder graph, specifying what information to compress, is traded-off against a decoder graph, specifying a generative model. Using this framework, we rederive existing dimensionality reduction methods including the deep variational information bottleneck and variational auto-encoders. The framework naturally introduces a trade-off parameter extending the deep variational CCA (DVCCA) family of algorithms to beta-DVCCA. We derive a new method, the deep variational symmetric informational bottleneck (DVSIB), which simultaneously compresses two variables to preserve information between their compressed representations. We implement these algorithms and evaluate their ability to produce shared low dimensional latent spaces on Noisy MNIST dataset. We show that algorithms that are better matched to the structure of the data (in our case, beta-DVCCA and DVSIB) produce better latent spaces as measured by classification accuracy, dimensionality of the latent variables, and sample efficiency. We believe that this framework can be used to unify other multi-view representation learning algorithms and to derive and implement novel problem-specific loss functions

STR-867: FLEXURAL BEHAVIOUR OF REINFORCED SCC BEAMS CONTAINING RECYCLED CRUMB RUBBER

This study aimed to investigate the effect of crumb rubber (CR) on the flexural behavior and cracking characteristics of self-consolidating concrete beams. Four full-scale self-consolidating rubberized concrete (SCRC) beams containing recycled CR particles as a partial replacement for fine aggregate with percentage ranging from 0% to 15% (by volume of sand) were tested. The performance of some design codes was evaluated in predicting the cracking moment and crack widths of the tested beams. The results indicated that increasing the CR content noticeably reduced the compressive strength, tensile strength, and first cracking moment of all SCRC beams. However, up to 15% replacement of CR, the flexural capacity of the tested beams was shown to be slightly decreased. In addition, increasing the CR content appeared to improve the beams’ ductility and limit the flexural crack widths. In general, the results of flexural loading tests indicated a promising potential for using SCRC in structural applications

Simultaneous Dimensionality Reduction: A Data Efficient Approach for Multimodal Representations Learning

We explore two primary classes of approaches to dimensionality reduction (DR): Independent Dimensionality Reduction (IDR) and Simultaneous Dimensionality Reduction (SDR). In IDR methods, of which Principal Components Analysis is a paradigmatic example, each modality is compressed independently, striving to retain as much variation within each modality as possible. In contrast, in SDR, one simultaneously compresses the modalities to maximize the covariation between the reduced descriptions while paying less attention to how much individual variation is preserved. Paradigmatic examples include Partial Least Squares and Canonical Correlations Analysis. Even though these DR methods are a staple of statistics, their relative accuracy and data set size requirements are poorly understood. We introduce a generative linear model to synthesize multimodal data with known variance and covariance structures to examine these questions. We assess the accuracy of the reconstruction of the covariance structures as a function of the number of samples, signal-to-noise ratio, and the number of varying and covarying signals in the data. Using numerical experiments, we demonstrate that linear SDR methods consistently outperform linear IDR methods and yield higher-quality, more succinct reduced-dimensional representations with smaller datasets. Remarkably, regularized CCA can identify low-dimensional weak covarying structures even when the number of samples is much smaller than the dimensionality of the data, which is a regime challenging for all dimensionality reduction methods. Our work corroborates and explains previous observations in the literature that SDR can be more effective in detecting covariation patterns in data. These findings suggest that SDR should be preferred to IDR in real-world data analysis when detecting covariation is more important than preserving variation.Comment: 12 pages, 6 figures in the main text. 6 pages, 6 figures in the supplementary materia

Structural behavior of rubberized concrete containing synthetic fibers

This research program aims to investigate the combining effect of crumb rubber (CR) and synthetic/metal fibers (SFs/MFs) in the development of concrete suitable for structural applications subjected to monotonic and cyclic loading. The research also aims to overcome the challenge of optimizing the strength and stability of self-consolidating concrete (SCC) containing CR and SFs/MFs. Five comprehensive experimental studies were conducted on both small-scale and large-scale concrete samples to meet the research objectives. The first study aimed to develop and optimize a number of successful self-consolidating rubberized concrete (SCRC) and synthetic fiber SCRC (SFSCRC) mixtures with a maximized percentage of CR and minimized reduction in strength. The variables in this study included various supplementary cementing materials (SCMs) specifically metakaolin (MK), silica fume (SLF), fly ash (FA), and ground granulated blast-furnace slag (GGBS), different binder contents (500, 550, and 600 kg/m³), varying percentages of CR (0% to 30%), different types of SFs specifically micro-synthetic fibers (MISFs), and macro-synthetic fibers (MASFs), different lengths of SFs (19mm, 27mm, 38mm, 50mm, and 54mm), and different SFs volume fractions (0%, 0.2%, and 1%). The second and third studies evaluated the flexural and shear behavior of large-scale reinforced concrete beams made with SCRC, vibrated rubberized concrete (VRC), SFSCRC, and synthetic fiber VRC (SFVRC). The fourth study investigated the structural performance of rubberized beam-column joints reinforced with SFs/MFs under reverse cyclic loading. This study consisted of three stages: the first stage contained a total of six SCRC mixtures selected to cast six beam-column joints with varied percentages of CR (0-25%). The second stage included eight rubberized concrete mixtures with different coarse aggregate sizes and different MFs lengths and volumes selected to pour eight beam-column joints to be tested under cyclic loading. The third stage contained seven rubberized concrete beam-column joints reinforced with different types, lengths, and volumes of SFs to be tested under cyclic loading. The fifth study evaluated the cyclic behavior of engineering cementitious composite (ECC) beam-column joints made with different percentages of CR, different SCMs, and different sand types. In this study a total of eight beam-column joints were cast and tested under reverse cyclic loading. The main results drawn from the first study indicated that the addition of SFs reduced the fresh properties, which limited the maximum percentage of CR that could be used in SCRC mixtures to 20%, compared to a 30% maximum percentage of CR used in developing successful SCRC mixtures without SFs. However, using SFs in SCRC mixtures increased the impact resistance and appeared to alleviate the reduction in splitting tensile strength (STS) and flexural strength (FS) that resulted from adding CR. The main results of the flexural testing conducted in study 2 indicated that using MISFs slightly enhanced the deformability, flexural stiffness, ductility, energy absorption, first cracking moment, and bending moment capacity, while this enhancement significantly increased when MASFs were used. Combining high percentage of MASFs (1%) with high percentage of CR (30%) compensated for the reduction in the bending moment capacity that resulted from using high percentage of CR, and helped to develop semi-lightweight concrete beams. The inclusion of CR in study 3 negatively affected the ultimate shear load, post-diagonal cracking resistance, and first cracking moment of the tested beams while it improved the deformation capacity, self-weight, and cracking pattern. Combining CR with MISFs or MASFs, further improved the deformation capacity, self-weight, and narrowed the crack widths of the tested beams. The results of this study also indicated that the use of a relatively higher percentage of fibers (1% compared to 0.2%) in VRC beams significantly compensated for the reduction in shear strength resulting from a high CR percentage (30%). The results of the fourth study revealed that the optimum percentage of CR to be used in beam-column joint mixtures is 15%. Although using this percentage slightly reduced the load carrying capacity, it greatly enhanced the ductility, brittleness index, deformability, and energy dissipation. The results also revealed that using MISFs slightly improved the structural performance of beam-column joints, while using MASFs had a significant effect on enhancing the load carrying capacity, ductility, stiffness, and energy dissipation of tested joints. The main results of the fifth study reported that increasing the percentage of CR up to 15% significantly increased the deformability, cracking behavior, ductility, and energy dissipation of ECC joints, while the initial stiffness, first crack load, and ultimate load were decreased