10,063 research outputs found

    ANEMIA PADA GAGAL JANTUNG

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    Sepertiga dari semua pasien dengan gagal jantung mengalami anemia, dan kehadirannya dikaitkan dengan keparahan gejala, peningkatan angka rawat inap, dan peningkatan angka kematian. Etiologi anemia bersifat multifaktorial, kompleks, dan bervariasi antar pasien. Penyebab utama terjadinya anemia pada gagal jantung adalah faktor inflamasi. Kadar hemoglobin berkorelasi negatif dengan penanda inflamasi seperti tumor necrosis factor-alpha, interleukin-6, dan protein C-reaktif, yang semuanya meningkat pada gagal jantung. Pengobatan anemia pada pasien gagal jantung dengan agen pemicu eritropoiesis telah dievaluasi secara intensif selama beberapa tahun terakhir. Sayangnya, agen ini tidak meningkatkan hasil dan dikaitkan dengan risiko efek samping tromboemboli yang lebih tinggi. Oleh karena itu, penggunaan ESA (Eritropoiesis-Stimulating Agents) untuk pengobatan anemia pada gagal jantung tidak dapat direkomendasikan. Terapi lain sedang dipelajari dan termasuk agen yang menargetkan reseptor eritropoietin, jalur hepsidin, atau penambah zat besi. Artikel ini berfokus pada patofisiologi anemia pada gagal jantung, evaluasi efektivitas modalitas terapi yang tersedia, dan terapi yang sedang diteliti yang dapat direkomendasikan sebagai pengobatan anemia pada gagal jantun

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    The role of artificial intelligence-driven soft sensors in advanced sustainable process industries: a critical review

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    With the predicted depletion of natural resources and alarming environmental issues, sustainable development has become a popular as well as a much-needed concept in modern process industries. Hence, manufacturers are quite keen on adopting novel process monitoring techniques to enhance product quality and process efficiency while minimizing possible adverse environmental impacts. Hardware sensors are employed in process industries to aid process monitoring and control, but they are associated with many limitations such as disturbances to the process flow, measurement delays, frequent need for maintenance, and high capital costs. As a result, soft sensors have become an attractive alternative for predicting quality-related parameters that are ‘hard-to-measure’ using hardware sensors. Due to their promising features over hardware counterparts, they have been employed across different process industries. This article attempts to explore the state-of-the-art artificial intelligence (Al)-driven soft sensors designed for process industries and their role in achieving the goal of sustainable development. First, a general introduction is given to soft sensors, their applications in different process industries, and their significance in achieving sustainable development goals. AI-based soft sensing algorithms are then introduced. Next, a discussion on how AI-driven soft sensors contribute toward different sustainable manufacturing strategies of process industries is provided. This is followed by a critical review of the most recent state-of-the-art AI-based soft sensors reported in the literature. Here, the use of powerful AI-based algorithms for addressing the limitations of traditional algorithms, that restrict the soft sensor performance is discussed. Finally, the challenges and limitations associated with the current soft sensor design, application, and maintenance aspects are discussed with possible future directions for designing more intelligent and smart soft sensing technologies to cater the future industrial needs

    Bilateral lung transplantation for pediatric pulmonary arterial hypertension: perioperative management and one-year follow-up

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    BackgroundBilateral lung transplantation (LuTx) remains the only established treatment for children with end-stage pulmonary arterial hypertension (PAH). Although PAH is the second most common indication for LuTx, little is known about optimal perioperative management and midterm clinical outcomes.MethodsProspective observational study on consecutive children with PAH who underwent LuTx with scheduled postoperative VA-ECMO support at Hannover Medical School from December 2013 to June 2020.ResultsTwelve patients with PAH underwent LuTx (mean age 11.9 years; age range 1.9–17.8). Underlying diagnoses included idiopathic (n = 4) or heritable PAH (n = 4), PAH associated with congenital heart disease (n = 2), pulmonary veno-occlusive disease (n = 1), and pulmonary capillary hemangiomatosis (n = 1). The mean waiting time was 58.5 days (range 1–220d). Three patients were bridged to LuTx on VA-ECMO. Intraoperative VA-ECMO/cardiopulmonary bypass was applied and VA-ECMO was continued postoperatively in all patients (mean ECMO-duration 185 h; range 73–363 h; early extubation). The median postoperative ventilation time was 28 h (range 17–145 h). Echocardiographic conventional and strain analysis showed that 12 months after LuTx, all patients had normal biventricular systolic function. All PAH patients are alive 2 years after LuTx (median follow-up 53 months, range 26–104 months).ConclusionLuTx in children with end-stage PAH resulted in excellent midterm outcomes (100% survival 2 years post-LuTx). Postoperative VA-ECMO facilitates early extubation with rapid gain of allograft function and sustained biventricular reverse-remodeling and systolic function after RV pressure unloading and LV volume loading

    Assessing Atmospheric Pollution and Its Impacts on the Human Health

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    This reprint contains articles published in the Special Issue entitled "Assessing Atmospheric Pollution and Its Impacts on the Human Health" in the journal Atmosphere. The research focuses on the evaluation of atmospheric pollution by statistical methods on the one hand, and on the other hand, on the evaluation of the relationship between the level of pollution and the extent of its effect on the population's health, especially on pulmonary diseases

    Advances in Binders for Construction Materials

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    The global binder production for construction materials is approximately 7.5 billion tons per year, contributing ~6% to the global anthropogenic atmospheric CO2 emissions. Reducing this carbon footprint is a key aim of the construction industry, and current research focuses on developing new innovative ways to attain more sustainable binders and concrete/mortars as a real alternative to the current global demand for Portland cement.With this aim, several potential alternative binders are currently being investigated by scientists worldwide, based on calcium aluminate cement, calcium sulfoaluminate cement, alkali-activated binders, calcined clay limestone cements, nanomaterials, or supersulfated cements. This Special Issue presents contributions that address research and practical advances in i) alternative binder manufacturing processes; ii) chemical, microstructural, and structural characterization of unhydrated binders and of hydrated systems; iii) the properties and modelling of concrete and mortars; iv) applications and durability of concrete and mortars; and v) the conservation and repair of historic concrete/mortar structures using alternative binders.We believe this Special Issue will be of high interest in the binder industry and construction community, based upon the novelty and quality of the results and the real potential application of the findings to the practice and industry

    Modeling and Simulation in Engineering

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    The Special Issue Modeling and Simulation in Engineering, belonging to the section Engineering Mathematics of the Journal Mathematics, publishes original research papers dealing with advanced simulation and modeling techniques. The present book, “Modeling and Simulation in Engineering I, 2022”, contains 14 papers accepted after peer review by recognized specialists in the field. The papers address different topics occurring in engineering, such as ferrofluid transport in magnetic fields, non-fractal signal analysis, fractional derivatives, applications of swarm algorithms and evolutionary algorithms (genetic algorithms), inverse methods for inverse problems, numerical analysis of heat and mass transfer, numerical solutions for fractional differential equations, Kriging modelling, theory of the modelling methodology, and artificial neural networks for fault diagnosis in electric circuits. It is hoped that the papers selected for this issue will attract a significant audience in the scientific community and will further stimulate research involving modelling and simulation in mathematical physics and in engineering

    Treatment of hepatitis C virus infection: From therapy with interferon to direct-acting antivirals

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    Hepatitis C virus (HCV) infection is a global public health concern. An estimated of 58 million people with chronic HCV infection are at risk of developing cirrhosis or hepatocellular carcinoma. Since the discovery of HCV in 1989, the search for a treatment to achieve viral clearance has been one of the main research goals, which is why this review focuses on the different treatments for HCV infection. In the 80s, therapies with interferon-a, and later pegylated interferon-a in monotherapy or combination with ribavirin began. However, sustained viral response (SVR) was achieved in a limited percentage of patients. First-generation direct-acting antivirals (DAAs) were approved in 2011, and new DAAs with SVR rates greater than 95% were developed in the last 10 years. However, mutations associated with resistance to DAAs have recently been reported, especially in HCV genotypes 1, subgenotype 1a, and 3. Finally, it is important to note that viral clearance after DAA treatment does not eliminate the risk of hepatocellular carcinoma in patients with liver cirrhosis

    Securing wider EU commitment to the elimination of hepatitis C virus

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    In 2016, the Hepatitis B and C Public Policy Association (HepBCPPA), gathered all the main stakeholders in the field of hepatitis C virus (HCV) to launch the now landmark HCV Elimination Manifesto, calling for the elimination of HCV in the EU by 2030. Since then, many European countries have made progress towards HCV elimination. Multiple programmes—from the municipality level to the EU level—were launched, resulting in an overall decrease in viremic HCV infections and liver-related mortality. However, as of 2021, most countries are not on track to reach the 2030 HCV elimination targets set by the WHO. Moreover, the COVID-19 pandemic has resulted in a decrease in HCV diagnoses and fewer direct-acting antiviral treatment initiations in 2020. Diagnostic and therapeutic tools to easily diagnose and treat chronic HCV infection are now well established. Treating all patients with chronic HCV infection is more cost-saving than treating and caring for patients with liver-related complications, decompensated cirrhosis or hepatocellular carcinoma. It is more important than ever to reinforce and scale-up action towards HCV elimination. Yet, efforts urgently need the dedicated commitment of policymakers at all governmental and policy levels. Therefore, the third EU Policy Summit, held in March 2021, featured EU parliamentarians and other key decision makers to promote dialogue and take strides towards securing wider EU commitment to advance and achieve HCV elimination by 2030. We have summarized the key action points and reported the ‘Call-to-Action’ statement supported by all the major relevant European associations in the field.info:eu-repo/semantics/publishedVersio

    Information Geometrically Generalized Covariate Shift Adaptation

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    Many machine learning methods assume that the training and test data follow the same distribution. However, in the real world, this assumption is very often violated. In particular, the phenomenon that the marginal distribution of the data changes is called covariate shift, one of the most important research topics in machine learning. We show that the well-known family of covariate shift adaptation methods is unified in the framework of information geometry. Furthermore, we show that parameter search for geometrically generalized covariate shift adaptation method can be achieved efficiently. Numerical experiments show that our generalization can achieve better performance than the existing methods it encompasses
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