Elsevier Research Articles

Gene Therapy in Cardiovascular Disease: Recent Advances and Future Directions in Science: A Science Advisory From the American Heart Association.

Cardiovascular disease remains the foremost cause of morbidity and mortality globally, affecting millions of individuals. Recent discoveries illuminate the substantial role of genetics in cardiovascular disease pathogenesis, encompassing both monogenic and polygenic mechanisms and identifying tangible targets for gene therapies. Innovative strategies have emerged to rectify pathogenic variants that cause monogenic disorders such as hypertrophic, dilated, and arrhythmogenic cardiomyopathies and hypercholesterolemia. These include delivery of exogenous genes to supplement insufficient protein levels caused by pathogenic variants or genome editing to correct, delete, or modify mutant sequences to restore protein function. However, effective delivery of gene therapy to specified cells presents formidable challenges. Viral vectors, notably adeno-associated viruses and nonviral vectors such as lipid and engineered nanoparticles, offer distinct advantages and limitations. Additional risks and obstacles remain, including treatment durability, tissue-specific targeting, vector-associated adverse events, and off-target effects. Addressing these challenges is an ongoing imperative; several clinical gene therapy trials are underway, and many more first-in-human studies are anticipated. This science advisory reviews core concepts of gene therapy, key obstacles, patient risks, and ongoing research endeavors to enable clinicians to understand the complex landscape of this emerging therapy and its remarkable therapeutic potential to benefit cardiovascular disease.

NEW ASPECTS OF CORE MICROTOMOGRAPHY

Compared to many other technologies of laboratory studies of core samples, the modern «digital core» technology is a rather young scientific and industrial direction, but for decades it has attracted close attention of a wide range of scientists and specialists of the oil and gas industry (geologists, sedimentologists, lithologists, geophysicists, developers, geological modelers, hydrodynamicists, physicists). For many specialists of practical sphere new methodical techniques and methods of such researches remain little known. If earlier computer X-ray microtomographs in the Russian Federation were the prerogative of only academic structures or service organizations, nowadays many subsoil users have started to equip their own laboratory and research centers with the corresponding specialized equipment, understanding that without these data it is hardly possible to build high-quality geological, petrophysical, tectonic-physical, rheological, hydrodynamic and geological-field models of the studied sediments, as well as to prepare progragmatic models of the studied sediments. In this article the authors made an attempt to study and analyze the current state of development of «digital core» technology.Using extensive factual material the accumulated experience of special studies in the field of full-size core tomography is summarized, estimates of absolute permeability of low-permeability complex sediments are made, results of studying cores in frozen and unfrozen state are given, detailed analysis of fracturing at different stages of sample preparation and ongoing studies is carried out, and specific problems for which a solution using the technology is expected in the near future are formulated In this article the positive and negative sides of poroset and direct methods of modeling of filtration properties by digital core model are studied. Particular attention is paid to the need for large-scale studies to obtain statistically reliable data samples, which can be used to select the most qualitative methods of research and application of algorithms for processing of initial geological and geophysical information.

Decision Support System for the Design Process of Apatite Biopolymer Composite Parts

Abstract In connection with the increase in the number and severity of various types of bone tissue injuries received as a result of wounds during military operations in Ukraine, an important issue in orthopedics and traumatology is making informed decisions about the possibility of restoring the integrity and functions of bone tissue when using different types of composition, porosity and strength of apatite-biopolymer composites. The scientific direction of research is the development of principles and methods for making scientifically based decisions in the design and additive manufacturing of bone substitutes based on apatite-biopolymer composites with functional properties depending on the nature of the localization of the cavity bone defect and its size. A set of methods for analyzing images of bone tissue, taking into account its spatial structure, which are obtained by sensors of different physical nature, with the use of neural network models, development of methods of their design, optimization and training is proposed. The new knowledge obtained as a result of the project will become the necessary basis for making optimal decisions in practice for the introduction of the latest methods of treatment and prosthetics in trauma surgery, oncology, cranio-maxillofacial surgery, dentistry, taking into account the risks of biocompatibility of apatite-biopolymer composites. Software development of an intelligent decision support system will be used to design bone substitutes with controlled composition, structure, porosity and mechanical strength for the further selection of additive technology for its production from apatite-polymer composites, which will contribute to increasing the efficiency of treatment and prosthetics in orthopedics and traumatology.

Cardiotoxicity following thoracic radiotherapy for lung cancer.

Radiotherapy is the standard of care treatment for unresectable NSCLC, combined with concurrent chemotherapy and adjuvant immunotherapy. Despite technological advances in radiotherapy planning and delivery, the risk of damage to surrounding thoracic tissues remains high. Cardiac problems, including arrhythmia, heart failure and ischaemic events, occur in 20% of patients with lung cancer who undergo radiotherapy. As survival rates improve incrementally for this cohort, minimising the cardiovascular morbidity of RT is increasingly important. Problematically, the reporting of cardiac endpoints has been poor in thoracic radiotherapy clinical trials, and retrospective studies have been limited by the lack of standardisation of nomenclature and endpoints. How baseline cardiovascular profile and cardiac substructure radiation dose distribution impact the risk of cardiotoxicity is incompletely understood. As Thoracic Oncology departments seek to expand the indications for radiotherapy, and as the patient cohort becomes older and more comorbid, there is a pressing need for cardiotoxicity to be comprehensively characterised with sophisticated oncology, physics and cardio-oncology evaluations. This review synthesises the evidence base for cardiotoxicity in conventional radiotherapy, focusing on lung cancer, including current data, unmet clinical needs, and future scientific directions.

Методические аспекты межкультурной коммуникации в преподавании русского языка в иранской аудитории

The article outlines the main stages of the formation and development of intercultural communication as a scientific direction and academic discipline in universities, clarifies some concepts. The difficulties of communication between Iranian students and Russians in the aspect of intercultural communication (based on a questionnaire) are revealed. The authors consider the issues of using comparative regional studies material with a single thematic content in the process of teaching RCT in an Iranian audience, pretext, pre-text and post-text tasks are presented. A typology of tasks and exercises on intercultural communication in the process of teaching Russian as a foreign language has been developed.

TEACHING A FOREIGN LANGUAGE IN A MASTER’S DEGREE PROGRAMME IN TERMS OF USING ARTIFICIAL INTELLIGENCE

The use of artificial intelligence by students learning a foreign language is becoming more common. The paper presents the results of a survey conducted among master’s students on the use of artificial intelligence in the educational process. The survey showed that 12 out of 17 undergraduates use neural networks, while seven master’s students use neural networks in preparation for foreign language classes. The article offers certain tasks: a critical review of a scientific paper in a certain field of study, writing an abstract/theses, as well as a presentation of a scientific direction.

Hydrogen production efficiency enhancement for alkaline water electrolyzers operating at varying temperature via an adaptive multi-mode control

The low-load efficiency performance of alkaline water electrolyzers (AWEs) is poor so its operation range is limited. It is unfeasible for AWEs to follow the fluctuant renewable energy sources (RESs). Besides, the power fluctuation will lead to the electrolyzer temperature variation. The efficiency performance is negatively influenced. Focusing on these problems, an efficiency enhancement control strategy for AWEs operating at varying temperature is introduced in this paper. First, the efficiency model of AWEs is established. Then, a multi-mode adaptive control strategy is proposed to improve the hydrogen production efficiency of AWEs. Based on the U–I curves of AWEs, parameter identification method is used to obtain the key parameters of proposed efficiency model. Finally, the feasibility of the control strategy is experimentally validated on a 10 kW AWE. The experimental outcome shows that by applying the proposed method, the low-load hydrogen production efficiency of AWEs can be improved at different temperatures. At 15% rated load, the electrolyzer efficiency increases from 21.54% to 42.73% at 80°C. The operation range of the electrolyzer can be extended from 30% to 100%–21%∼100% of rated load at 80°C.© 2024 The Authors. Published by Elsevier Ltd.

Ethical climate and turnover intention among nurses: A scoping review.

Nursing shortages and turnover pose significant challenges for health organizations worldwide, driven by various organizational and individual factors. Ethical climate has emerged as a critical aspect influencing nurses' well-being and retention within healthcare settings, reflecting organizational practices with moral implications. Understanding the relationship between ethical climate and turnover intention among nurses is paramount for practitioners, managers, and policymakers. This review aimed to examine evidence and synthesize findings from prior studies on the association between ethical climate and turnover intention among nurses and identify key variables influencing its dynamics. A scoping review methodology was employed, encompassing studies retrieved up to February 2024 from major scientific databases, including PUBMED/MEDLINE, WEB OF SCIENCE, SCOPUS, and SCIENCE DIRECT. This study was conducted following ethical considerations and sound scientific guidelines. Sixteen articles meeting the inclusion criteria were identified. Overall, a significant association between ethical climate and turnover intention was observed, with theoretical underpinnings demonstrated. Key variables frequently linked to both ethical climate and turnover intention included organizational commitment, job satisfaction, and moral distress, while organizational commitment, organizational cynicism, and organizational trust emerged as mediators. Despite demonstrating a clear association, results underscore the necessity for replication studies, qualitative and longitudinal approaches, and standardized measurement. Enhancing the ethical climate through regular assessments, education, and interprofessional collaboration can improve retention. Future research should focus on theory building, ethical management and leadership, associated factors analysis, and intervention studies to develop comprehensive models and effective strategies for improving the ethical climate and reducing nurse turnover.

Extending Shelf‐Life of Two‐Step Method Precursor Solutions through Targeted Regulation for Highly Efficient and Reproducible Perovskite Solar Cells

AbstractPrecursor solution aging process can cause significant influence on the photovoltaic performance of perovskite solar cells (PVSCs). Notably, we first observe that the aging phenomenon is more severe in the precursor of two‐step sequential method compared to that in one‐step method due to that the protic solvent isopropanol facilitates amine‐cation side reaction and iodide ions oxidation. Herein, we report a novel approach for selectively stabilizing both organic amine salt and lead iodide (PbI2) precursor solutions in two‐step method. The introduction of benzene‐1,3‐dithiol into organic amine salt solution can mitigate amine‐cation side reactions due to the formation of an acidic and reducing environment. Simultaneously, decamethylferrocene (FcMe10/FcMe+10) pair can act as a redox shuttle in PbI2 solution to concurrently oxidize Pb0 and reduce I2 in cyclic manner. Consequently, the PVSCs device fabricated from ameliorative precursor solutions demonstrates superior power conversion efficiency of 25.31 %, retaining 95 % of its efficiency after 21 days of solution aging. Moreover, the unencapsulated devices maintain 85 % of primitive efficiency for 1500 h at maximum power point tracking under continuous illumination. This work establishes a fundamental guidance and scientific direction for the stabilization of two‐step perovskite precursor solutions.

New Directions for Population Health Science and Scholarship

New Directions for Population Health Science and Scholarship

A Scoping Review of Implementation Science Studies in the Field of Traumatic Brain Injury: State of the Science and Future Directions.

Implementation research is essential to accelerating the public health benefits of innovations in health settings. However, the US National Academies of Sciences, Engineering, and Medicine 2022 report identified a lag in published implementation research applied to traumatic brain injury (TBI). Our objectives were to characterize implementation science studies published to date in TBI clinical care and rehabilitation and provide recommendations for future directions. A scoping review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. Articles published between 2011 and 2023 were identified (MEDLINE, PubMed, PsychInfo, and Web of Science), and included if the study focused on the TBI population, measured at least one Proctor (2011) implementation outcome and aligned with implementation research designs. Data were charted using an extraction template. Of the 38 articles, 76% were published between 2018 and 2023. About 37% of articles were in the pre-implementation phase, and 57.9% were in the implementation phase. Over half of articles used a theory, model, or framework to guide the research. Fifteen studies were descriptive, 10 were qualitative, 7 were mixed methods, and 4 were randomized controlled trials. Most studies investigated implementation outcomes regarding national guidelines following TBI or TBI symptom management. Adoption (42.1%) and fidelity (42.1%) were the most commonly studied implementation outcomes, followed by feasibility (18.4%), acceptability (13.2%), and penetration (10.5%). Only 55% of studies used or tested the effectiveness of one or more implementation strategies, with training and education used most commonly, followed by data warehousing techniques. Future research should prioritize the selection and investigation of implementation strategy effectiveness and mechanisms across contexts of care and use implementation research reporting standards to improve study rigor. Additionally, collaborative efforts between researchers, community partners, individuals with TBI, and their care partners could improve the equitable translation of innovations across service contexts.

AfroPALM - Afrocentric Palm oil Adulteration Learning Models: an End-to-end Deep Learning Approach for Detection of Palm Oil Adulteration

In Africa, rapid population growth and a focus on increasing food production have often overlooked the crucial aspect of food safety, leading to the highest per capita incidences of foodborne illness globally. This underscores the imperative to address the food safety challenges on the continent. A critical concern is the widespread adulteration of red palm oil across West Africa, often involving harmful red azo dyes, posing significant health risks. Current detection methods, reliant on laboratory procedures, are not only time-consuming and expensive but also impractical for broad implementation in local markets. To address these limitations, we propose an end-to-end deep learning approach that bypasses the need for manual feature extraction and laboratory-based analyses. Utilizing high- resolution imaging technology, our approach can objectively and reliably detect palm oil adulteration directly from raw image data. In our study, we developed a deep convolutional neural network dubbed AfroPALM-Custom, specifically designed for detecting palm oil adulteration in African markets. AfroPALM-Custom was pivotal in refining our deep learning approach, achieving a test accuracy of 90.63% and an F1 score of 90.98%. We later adapted mobile-efficient pretrained models, namely SqueezeNet1.1 and GhostNetV1—mobile—fine-tuned and as well dubbed AfroPALM-GhostNet and AfroPALM-SqueezeNet. In performance, AfroPALM-GhostNet and AfroPALM-SqueezeNet achieved test accuracies of 96.29% and 91.16%, respectively, and F1-scores of 96.57% and 91.95%. This proficiency in identifying adulterated palm oil demonstrates a significant advancement in food safety solutions for African markets, offering a practical and scalable approach for implementation within local marketplaces where resources are limited and laboratory methods are impractical.© 2001 Elsevier Science. All rights reserved

Future directions for deep ocean climate science and evidence-based decision making

IntroductionA defining aspect of the Intergovernmental Panel on Climate Change (IPCC) assessment reports (AR) is a formal uncertainty language framework that emphasizes higher certainty issues across the reports, especially in the executive summaries and short summaries for policymakers. As a result, potentially significant risks involving understudied components of the climate system are shielded from view.MethodsHere we seek to address this in the latest, sixth assessment report (AR6) for one such component—the deep ocean—by summarizing major uncertainties (based on discussions of low confidence issues or gaps) regarding its role in our changing climate system. The goal is to identify key research priorities to improve IPCC confidence levels in deep ocean systems and facilitate the dissemination of IPCC results regarding potentially high impact deep ocean processes to decision-makers. This will accelerate improvement of global climate projections and aid in informing efforts to mitigate climate change impacts. An analysis of 3,000 pages across the six selected AR6 reports revealed 219 major science gaps related to the deep ocean. These were categorized by climate stressor and nature of impacts.ResultsHalf of these are biological science gaps, primarily surrounding our understanding of changes in ocean ecosystems, fisheries, and primary productivity. The remaining science gaps are related to uncertainties in the physical (32%) and biogeochemical (15%) ocean states and processes. Model deficiencies are the leading cited cause of low certainty in the physical ocean and ice states, whereas causes of biological uncertainties are most often attributed to limited studies and observations or conflicting results.DiscussionKey areas for coordinated effort within the deep ocean observing and modeling community have emerged, which will improve confidence in the deep ocean state and its ongoing changes for the next assessment report. This list of key “known unknowns” includes meridional overturning circulation, ocean deoxygenation and acidification, primary production, food supply and the ocean carbon cycle, climate change impacts on ocean ecosystems and fisheries, and ocean-based climate interventions. From these findings, we offer recommendations for AR7 to avoid omitting low confidence-high risk changes in the climate system.

Research in the scientific directions of allergen management and the use of antioxidants from plant raw materials in the meat industry

Research in the scientific directions of allergen management and the use of antioxidants from plant raw materials in the meat industry

Review of ESG Information Disclosure under the New Development Concept

Abstract: With continuous development in the ESG (Environmental, Social, and Governance) field in China, the understanding of the concept and role of ESG by Chinese enterprises and related scholars has been deepening. However, issues such as the lack of uniform standards and frameworks for information disclosure, poor quality of information, and even greenwashing have gradually emerged in ESG information disclosure. The new development concept serves as a guiding principle for Chinas development, providing scientific direction for economic growth. The three major components of ESG closely align with the five principles of the new development concept, and the deficiencies and problems in ESG information disclosure profoundly affect the new development concept. Therefore, this paper aims to comprehensively review related research on ESG information disclosure and propose solutions to the identified problems based on the context of the new development concept.

Experimental Study on Horizontal Resistance Parameter of Gravelly Soil Considering Slope Gradient Factor

Horizontal resistance can be significantly different for gravelly soil slope sites with different gradients. The impact of slope gradient on the horizontal resistance of soil based on a three-dimensional physical simulation test has been investigated, and the displacement of the pile top and soil pressure for four piles under various gradients (slope gradient 0–45°) was analyzed. The research reveals that ① The soil resistance exhibits a linear increase stage, non-linear steep increase stage, and gradually stabilizing stage with the increase in load. ② The ultimate soil resistance is significantly affected by depth and displacement, and hyperbolic variation characteristics related to the displacement stage. It has a slope weakening effect, and the steeper the slope gradient, the lower the ultimate soil resistance of the pile sides, which effect is negligible when the depth exceeds 0.6–0.7 times that of the pile buried depth. ③ Based on the characteristics of horizontal ultimate resistance-displacement-depth variation in soil, a gradient factor, which is only related to the slope gradient, was used to describe the impact of gradient on the soil resistance modulus (kini) and ultimate resistance (pu). kini is reduced close to the ground surface and gradually increases with depth until it becomes equal to the value of level ground. The ratio between pu in slope ground and level ground was determined for slope angles. The above horizontal resistance parameters were expressed as a function based on the test data to calculate the lateral ultimate bearing capacity of gravelly soil considering the slope gradient factor. The research results developed the theory of foundation design for building structures, promoting the reliability evaluation of pile soil systems towards a more scientific and rigorous direction.

Transportation of Liquefied Natural Gas (LNG) via Long-Distance Low-Temperature Pipeline and Cascade Utilization of Cold Energy

In addition to methane, liquefied natural gas (LNG) typically contains valuable light hydrocarbons such as ethane, propane, and butane. Establishing LNG receiving stations for imported LNG can effectively alleviate regional energy supply-demand conflicts, optimize energy consumption structures, promote sustainable regional economic development, and improve ecological environments. The technology for long-distance low-temperature pipeline transportation of LNG facilitates the efficient transfer and cascading utilization of the cold energy contained in LNG, allowing for deep coupling with cold networks and natural gas pipelines, thereby promoting the integrated development of LNG, cold energy, and natural gas transportation. This paper systematically summarizes the current status of LNG long-distance low-temperature pipeline transportation technology, cold energy cascading utilization technology, pipeline regulation technology, and multi-network integration technology. It analyzes the key challenges these technologies face under new strategies and looks ahead to future development trends, aiming to provide scientific insights and directions for the application of LNG long-distance low-temperature transportation and multi-network integration in China.

Studying the Defects in Spinel Compounds: Discovery, Formation Mechanisms, Classification, and Influence on Catalytic Properties.

Spinel ferrites demonstrate extensive applications in different areas, like electrodes for electrochemical devices, gas sensors, catalysts, and magnetic adsorbents for environmentally important processes. However, defects in the real spinel structure can change the many physical and chemical properties of spinel ferrites. Although the number of defects in a crystal spinel lattice is small, their influence on the vast majority of physical properties could be really decisive. This review provides an overview of the structural characteristics of spinel compounds (e.g., CoFe2O4, NiFe2O4, ZnFe2O4, Fe3O4, γ-Fe2O3, Co3O4, Mn3O4, NiCo2O4, ZnCo2O4, Co2MnO4, etc.) and examines the influence of defects on their properties. Attention was paid to the classification (0D, 1D, 2D, and 3D defects), nomenclature, and the formation of point and surface defects in ferrites. An in-depth description of the defects responsible for the physicochemical properties and the methodologies employed for their determination are presented. DFT as the most common simulation approach is described in relation to modeling the point defects in spinel compounds. The significant influence of defect distribution on the magnetic interactions between cations, enhancing magnetic properties, is highlighted. The main defect-engineering strategies (direct synthesis and post-treatment) are described. An antistructural notation of active centers in spinel cobalt ferrite is presented. It is shown that the introduction of cations with different charges (e.g., Cu(I), Mn(II), Ce(III), or Ce(IV)) into the cobalt ferrite spinel matrix results in the formation of various point defects. The ability to predict the type of defects and their impact on material properties is the basis of defect engineering, which is currently an extremely promising direction in modern materials science.

Espécies vegetais descritas em área de reserva da biosfera da Mata Atlântica como fonte para novos compostos bioativos

This work consists of a systematic review of the literature related to the plant species cataloged in Parque das Dunas, Salvador-BA, a region acclaimed by the title of Atlantic Forest biosphere reserve by UNESCO (United Nations Educational, Scientific and Cultural Organization). We included studies containing data on pharmacological activity, phytochemical and toxic profile of the species, from PUBMED, SCIELO and SCIENCE DIRECT databases, in Portuguese and English, without a delimited period. The scientific names of the thirty cataloged species were used as descriptors. The physicochemical parameters and prediction in silico toxicity were evaluated for the isolated and active compounds. As a result, it was verified that of the thirty species, twelve were investigated for pharmacological activity and/or phytochemical and toxic profile, and 33% of the studies involving Chrysobalanus icaco L. (Chrysobalanaceae). Twenty-five bioactive compounds isolated predominantly of the terpene and flavonoid classes, and in eighteen studies the safety of seven species was evaluated. The in silico analysis showed ten compounds did not violate the Lipinski and Veber rules, and only three with high toxic potential. This review gathers data that point to the use of natural products from Brazilian territory in research that may culminate in development of innovative drugs.

Efficient virtual machine placement in cloud computing environment using BSO-ANN based hybrid technique

Cloud computing has revolutionized the way businesses and individuals access and utilize computing resources. Efficient virtual machine placement is a critical aspect of optimizing resource utilization, reducing operational costs, energy consumption, service level agreement and minimum virtual machine migrations, execution time, and ensuring the overall performance of cloud services. This manuscript introduces a novel approach that combines the creative problem-solving capabilities of brainstorming with the computational power of Artificial Neural Networks (ANN) to address the virtual machine placement problem in cloud environments. In this study, we propose a hybrid technique that leverages the collective intelligence of human brainstorming to generate a diverse set of placement strategies. These strategies are then evaluated, refined, and optimized using an ANN model trained on historical cloud resource allocation workload logs. By integrating the human creative process with the data-driven predictive capabilities of ANN, our approach aims to overcome the limitations of traditional virtual machine placement algorithms, which often struggle to adapt to dynamic workloads and changing resource requirements. The manuscript provides a detailed description of the hybrid technique, including the process of brainstorming for generating placement strategies, data collection and preprocessing, ANN model development, and the integration of these components into an efficient placement system. We present experimental results demonstrating the effectiveness of our approach in optimizing resource allocation, improving service performance, and optimizing resource utilization, reducing energy consumption, service level agreement and minimum virtual machine migrations, reducing execution time compared to existing static, and meta-heuristic methods. The proposed Brain Storming with ANN based Hybrid Technique offers a promising solution for enhancing the efficiency of virtual machine placement in cloud computing environments. BSO-ANN outperforms the existing techniques using performance metrics (energy consumption(Kwh), execution time(ms), SLA violations, and number of migrations). It combines human ingenuity with data-driven insights to adapt to the ever-changing dynamics of cloud workloads. This manuscript contributes to the ongoing research in cloud resource management, offering a practical approach for cloud service providers and organizations to better utilize their resources and enhance the overall quality of cloud-based services. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd.