Systematic Analysis Research Articles

Enhancing currency prediction in international e-commerce: Bayesian-optimized random forest approach using the Klarna dataset

In the ever-evolving landscape of global commerce, marked by the convergence of digital transformation and borderless markets, this research addresses the intricate challenges of currency exchange and risk management. Leveraging Bayesian optimization, the study fine-tunes the random forest algorithm using the extensive Klarna E-commerce dataset. Through systematic analysis, the research uncovers insights into managing currency prediction amid dynamic global markets. Emphasizing the role of Bayesian optimization parameters, the study reveals nuanced trade-offs in model performance. Notably, the optimal simulation, conducted with 14 iterations, 1 job, and a random state set to 684, exhibits a standout performance, showcasing a negative mean squared error (MSE) of approximately -0.9891 and an accuracy rate of 74.63%. The primary objective is to assess the impact of Bayesian optimization in enhancing the random forest algorithm's predictive capabilities, particularly in currency prediction within international e-commerce. These findings offer refined strategies for businesses navigating the intricate landscape of global finance, empowering decision-making through a comprehensive understanding of data, algorithms, and challenges in international commerce.

Monitoring bridge bearings based on rubber-based composite materials

To improve the real-time monitoring capability of bridge structure safety, intelligent bridge bearings were prepared based on rubber based composite materials. The study selected T4/3-5 intelligent rubber bearings and conducted a systematic experimental analysis on their response characteristics under different loading speeds and holding states. By measuring the displacement and resistance changes of the intelligent bearings under various load conditions, their sensitivity and stability were evaluated. These experiments confirmed that the T4/3-5 intelligent rubber bearings exhibited a total creep deformation of approximately 1 mm and 1.5 mm under loads of 60 kN and 45 kN, respectively. This suggests that the molecular chains reach a relatively stable configuration under high loads and are not prone to further changes. Moreover, the resistance reduction ratio under a load of 60 kN is approximately 0.57, while under a load of 45 kN it is approximately 0.62, indicating that nickel powder particles in the composite material form a more stable conductive network after molecular rearrangement. At different loading rates, the displacement-load and load-resistance curves of the bearings show consistent trends, and the creep and resistance relaxation behaviors exhibit certain regularity with the concentration of load size. Through the analysis of these data, the study elucidated the resistance change mechanism in the bearings and validated the feasibility of intelligent bearings as a real-time load monitoring tool.

CellSNAP: a fast, accurate algorithm for 3D cell segmentation in quantitative phase imaging.

Three-dimensional quantitative phase imaging (QPI) has rapidly emerged as a complementary tool to fluorescence imaging, as it provides an objective measure of cell morphology and dynamics, free of variability due to contrast agents. It has opened up new directions of investigation by providing systematic and correlative analysis of various cellular parameters without limitations of photobleaching and phototoxicity. While current QPI systems allow the rapid acquisition of tomographic images, the pipeline to analyze these raw three-dimensional (3D) tomograms is not well-developed. We focus on a critical, yet often underappreciated, step of the analysis pipeline that of 3D cell segmentation from the acquired tomograms. We report the CellSNAP (Cell Segmentation via Novel Algorithm for Phase Imaging) algorithm for the 3D segmentation of QPI images. The cell segmentation algorithm mimics the gemstone extraction process, initiating with a coarse 3D extrusion from a two-dimensional (2D) segmented mask to outline the cell structure. A 2D image is generated, and a segmentation algorithm identifies the boundary in the plane. Leveraging cell continuity in consecutive -stacks, a refined 3D segmentation, akin to fine chiseling in gemstone carving, completes the process. The CellSNAP algorithm outstrips the current gold standard in terms of speed, robustness, and implementation, achieving cell segmentation under 2s per cell on a single-core processor. The implementation of CellSNAP can easily be parallelized on a multi-core system for further speed improvements. For the cases where segmentation is possible with the existing standard method, our algorithm displays an average difference of 5% for dry mass and 8% for volume measurements. We also show that CellSNAP can handle challenging image datasets where cells are clumped and marred by interferogram drifts, which pose major difficulties for all QPI-focused AI-based segmentation tools. Our proposed method is less memory intensive and significantly faster than existing methods. The method can be easily implemented on a student laptop. Since the approach is rule-based, there is no need to collect a lot of imaging data and manually annotate them to perform machine learning based training of the model. We envision our work will lead to broader adoption of QPI imaging for high-throughput analysis, which has, in part, been stymied by a lack of suitable image segmentation tools.

Optimizing launch window opportunities for ESA's comet Interceptor mission using primer vector theory

Comet Interceptor (Comet-I) was selected in June 2019 as the first ESA F-Class mission and aims to explore a pristine comet, which will visit the inner Solar System for the first time. Comet-I will hitch a ride to a Sun-Earth L2 quasi-halo orbit, as a co-passenger in ESA's M4 ARIEL's launch. At the time of writing, this is scheduled for 2029. It will then remain there awaiting the right departure conditions to leave definitively and intercept a newly discovered comet. Comet-I will be the first mission to be designed and launched without an already identified target. Given the latter, the Comet-I Target Identification Working Group is tasked with the continuous follow-up of newly discovered comets, to analyse their characteristics as virtual targets for Comet-I. The goal of the paper is to inform the decision-making process that will eventually be necessary to commit Comet-I spacecraft to its final target.Within this context, this paper presents a trajectory optimization process based on Primer Vector theory that considers intercept trajectories with multiple revolutions and multiple deep space manoeuvres (DSM). The process is applied to 21 long period comets, which were discovered since July 1,1st July 2020 and whose characteristics are of interest for the Comet-I mission. In fact, one of the latest of these recently discovered comets, C/2023 H2, is the most accessible virtual target discovered at the time of writing, with delta-V intercept costs well within Comet-I capabilities. A complete launch window analysis is thus presented for C/2023 H2, which accounts not only for the most optimal delta-V intercept transfer, but also considers all launch options that satisfy all Comet-I's current boundary conditions and flyby constraints. A systematic launch window analysis is made possible by a grid search approach on which departure and fly-by dates are fixed and the remaining design variables, including the times for manoeuvres and their Δv impulse vector, are optimized via Primer Vector theory. This allows to understand the latest possible departure date, but also the sensitivity to departure and arrival dates of key encounter characteristics such as relative fly-by speed, the solar aspect angle and the Earth distance.

Development of a school-based programme for mental health promotion and prevention among adolescents in Nepal and South Africa

IntroductionAdolescence is a critical time for mental health promotion and prevention and establishing healthy behaviours. Implementing universal, school-based psychosocial interventions can improve short- and long-term health trajectories for adolescents. While these interventions may offer important opportunities for fostering skills and relationships, few school-based interventions have been developed for and tested in low- and middle-income countries (LMICs) where adolescent mental health needs may be significant and under-served. This manuscript details the development of a multi-component, universal school-based intervention, Health Action in ScHools for a Thriving Adolescent Generation (HASHTAG), for adolescents aged 12–15 years in Nepal and South Africa. Methods and resultsWe describe HASHTAG's development over four phases, combining methods and results as each phase was iteratively conducted between 2018 and 2021. Phase 1 included a systematic review and components analysis, building from WHO guidelines for adolescent mental health. Seven components were strongly supported by the evidence: emotional regulation, stress management, mindfulness, problem-solving, interpersonal skills, assertiveness training, and alcohol and drug education. Phase 2 encompassed site selection, theory of change development, and formative research engagements; research teams in each site engaged adolescents and key adult stakeholders to identify priorities for intervention. Stakeholders voiced preferences for external facilitators and key content and delivery for intervention sessions. These findings informed Phase 3, a draft manual of HASHTAG, including a whole-school component, called Thriving Environment in Schools, and a classroom-based, six-session component, Thrive Together. In Phase 4, participants engaged in consultative workshops to review and contextualise content by country, preparing HASHTAG for implementation in a feasibility trial. Minor adaptations were made in Nepal, including using school nurses and adjusting take-home materials; both country's workshops identified practical considerations for implementing activities. ConclusionsHASHTAG was designed around core evidence-based components to increase translatability across LMICs, while enabling country-specific tailoring to enhance feasibility. Future research will test whether this multi-component, whole-school approach can improve adolescent mental health.

Global, regional, and national burden of thalassemia, 1990–2021: a systematic analysis for the global burden of disease study 2021

Anemia is a significant contributor to the global disease burden, of which thalassemia is the most common hereditary anaemic disease. Previous estimates were based on data that were geographically limited and lacked comprehensive global analysis. This study provides the prevalence, incidence, mortality and disability-adjusted life years (DALYs) of thalassemia in 204 countries and regions of thalassemia between 1990 and 2021, focusing on the age structure and time trends of the disease burden. To provide effective information for health policy, allocation of medical resources and optimization of patient management programs. Using the standardised Global Burden of Disease (GBD) methodologies, we aimed to derive a more precise representation of the health burden posed by thalassemia by considering four distinct types of epidemiological data, namely the incidence at birth, prevalence, mortality and DALYs. The presented data were meticulously estimated and displayed both as numerical counts and as age-standardised rates per 100,000 persons of the population, accompanied by uncertainty interval (UI) to highlight potential statistical variability. The temporal trends spanning the years 1990-2021 were subjected to a rigorous examination utilizing Joinpoint regression analysis. This methodological approach facilitated the computation of the annual percentage change (APC) and the average annual percentage change (AAPC), along with their corresponding 95% confidence intervals (CIs). Globally, the age-standardized prevalence rates (ASPR), age-standardized incidence rates (ASIR), age-standardized mortality rates (ASMR), and age-standardized DALYs rates for thalassemia in 2021 were 18.28 per 100,000 persons (95% UI 15.29-22.02), 1.93 per 100,000 persons (95% UI 1.51-2.49), 0.15 per 100,000 persons(95% UI 0.11-0.20), and 11.65 per 100,000 persons (95% UI 8.24-14.94), respectively. Compared to 1990, these rates have decreased by 0.18 (95% UI-0.22 to-0.14), 0.25 (95% UI-0.30 to-0.19), 0.48 (95% UI-0.60 to-0.28), and 0.49 (95% UI-0.62 to-0.29) respectively. In 2021, the ASIR of thalassemia was highest in East Asia at 7.35 per 100,000 persons (95% UI 5.37-10.04), and ASMR was highest in Southeast Asia at 0.37 per 100,000 persons (95% UI 0.29-0.45).Gender comparisons showed negligible differences in disease burden, with the highest prevalence noted in children under five, decreasing with age. The global ASPR and ASMR declined from 1990 to 2021 overall, though an increasing trend in prevalence was found among the elderly. Joinpoint analysis revealed that the global ASPR increased between 2018 and 2021 (APC=9.2%, 95% CI: 4.8%-13.8%, P<0.001), ASIR decreased (APC=-7.68%, 95% CI:-10.88% to-4.36%, P<0.001), and there was a significant rise in ASMR from 2019 to 2021 (APC=4.8%, 95% CI: 0.1%-9.6%, P<0.05). Trends in ASPR and ASMR varied across regions, with notable changes in South Asia. The global burden of thalassemia, reflected in its prevalence, incidence, mortality, and DALYs, exhibits significant disparities. Geographic and demographic shifts in disease distribution have been observed from 1990 to 2021, with an overall decrease in burden, yet an increase in cases among the elderly population. Analysis of epidemiological trends over time highlights the influence of health policies and significant public health interventions on thalassemia outcomes. There data are crucial for healthcare professionals, policymakers, and researchers to refine and enhance management strategies, aiming to further mitigate thalassemia's global impact. National Natural Science Foundation of China; Guizhou Province Science and Technology Project; Guizhou Province Science and Technology Foundation of Health Commission.

Resource classification of coal bed methane and its contribution in energy transition and decarbonization path of oil and gas industry (A synopsis of CBM Life Cycle Analysis)

World economies are becoming increasingly more dependent on energy and countries have a voracious appetite for energy therefore, the need for an abundant clean energy is becoming a necessity. Energy companies are challenging environmental costs of coal bed methane as with advancement of new technology the impact from production of coal bed methane can reduce and to a certain extent avoided or offset. Production of coal bed methane results in changes to the land, to surface water and to ground water systems. Carbon quantification is required to understand, monitor and manage these changes. Previous life cycle analysis for this resource class has lack of process design details because of which there is a knowledge gap in understanding of emission profile of coal bed methane’s production. The objective of this study is to add value and reduce the scientific gap with respect to the production techniques of coal bed methane and its environmental impact. Removal of water continuously from coal seams depletes ground water and may eventually lower surface water flows (streams and rivers). It can also change the flow of groundwater drawing fresh water into the coal seams. To better understand this phenomenon the work establishes the Coal Bed Methane production foundation by in depth understanding of · Coal Bed Methane reservoir types; · Coal Bed Methane depositional environment, generation, and geological distributions; · Coal Bed Methane resource energy supply methods by understanding its construction, production and processing. The study then uses Life Cycle Assessment approach and understands previous emission numbers of Coal Bed Methane’s resource development. The work does systematic analysis and summarizes the previously published Life Cycle Analysis to understand further the potential environmental impacts of resource development of Coal Bed Methane. This will help energy companies to understand the field development, production, and operations of coal bed methane’s resource in terms of carbon numbers thereby optimizing the operations through carbon management thus reducing the potential impacts.

Managing global supply chain teams: human resource strategies for effective collaboration and performance

This study examines the role of human resource (HR) strategies in enhancing collaboration and performance within global supply chains, an area of increasing importance in the context of globalized business operations. The primary objective is to identify and analyze current trends in HR practices that contribute to effective global supply chain management, focusing on adaptability, technological integration, sustainability, and ethical practices. Employing a systematic literature review and content analysis as its methodology, the study draws on academic journals, conference proceedings, and industry reports from databases such as Scopus, Web of Science, and Google Scholar. Key findings reveal that adaptability and flexible work arrangements, supported by digital HR technologies, are essential for operational resilience and employee engagement. The integration of sustainability and ethical considerations into HR practices is identified as crucial for maintaining competitive advantage and ensuring regulatory compliance. Furthermore, the development of cultural intelligence and inclusive leadership within global teams is highlighted as vital for fostering effective collaboration across diverse cultural landscapes. The study concludes that strategic HR management plays a pivotal role in navigating the challenges and leveraging the opportunities within global supply chains. Recommendations for practitioners emphasize the importance of continuous learning, embracing digital transformation, and integrating sustainability into HR practices. The study also outlines areas for further research, including the impact of emerging technologies on HR practices and the long-term effects of sustainable HR strategies on organizational performance. This research contributes valuable insights into optimizing HR strategies to enhance global supply chain collaboration and performance.

Human resource strategies for resilient supply chains in logistics and transportation: A critical review

This study systematically reviews and analyzes the literature on Human Resource Management (HRM) strategies for enhancing supply chain resilience in the logistics and transportation sector, identifying the pivotal role HRM plays in fortifying supply chains against disruptions. Utilizing a systematic literature review and content analysis, this research examines articles from peer-reviewed journals, focusing on publications from 2004 onwards to capture contemporary HRM practices and their impact on supply chain resilience. The methodology involves a detailed search strategy across multiple databases, employing specific inclusion and exclusion criteria to ensure the relevance and quality of the literature reviewed. Key findings reveal that workforce agility, technological integration in HRM practices, leadership development, and the cultivation of a resilient organizational culture are essential components of effective HRM strategies in this context. These elements collectively contribute to the resilience of supply chains, enabling organizations to maintain operational continuity during disruptions. Based on these insights, the study proposes strategic recommendations for industry leaders and policymakers, emphasizing the importance of investing in employee development, adopting technology-enhanced HRM practices, and fostering ethical and sustainable HR practices to support supply chain resilience. Concluding, the study highlights the need for further research, particularly empirical studies that examine the direct impact of HR strategies on supply chain performance. This research underscores the critical role of strategic HRM in enhancing supply chain resilience, offering a roadmap for future efforts in the logistics and transportation sector.

The external dimension of Italian migration policy in the wider Mediterranean

ABSTRACT This article provides the first systematic and comprehensive analysis of the external dimension of Italy’s migration policy (EXMIPO) in the broader Mediterranean, over the past three decades. Building on an original dataset spanning over 30 years and 125 instruments, it investigates how and to what extent Italy cooperated with countries of origin and transit in the management of migration flows. The article argues that the external dimension of Italy’s migration policy is far richer than initially expected. From the immediate neighbourhood, Italy’s EXMIPO has gradually extended well beyond its geographical borders. If initially, it relied on a strategy of issue-linkage between quotas and return agreements, this gradually faded away in favour of more informal tools. While governments’ political ideology did not play a key role in defining the direction of Italy’s EXMIPO tools, we find that the evolving dynamics of migratory flows, and the pursuit of flexible tools to promptly address rising numbers, were crucial aspects behind the country’s external migration policy.

Metaphor signalling constructions in discourse related to the experience of depersonalization/derealization

ABSTRACT In this study a systematic analysis of signaled metaphor is undertaken in naturally occurring discourse from an online forum relating to the experience of depersonalization/derealization, which has a specific relationship with metaphor. While it is relatively easy to locate pre-identified metaphor source terms in such large text corpora, finding singular metaphor that may express subjective experience is recognized as a difficult but important task, which signals of metaphor may support. It is vital to accurately represent, such discourse, rather than only impose pre-defined knowledge, in order that ethical concerns such as exclusion, demographic misrepresentation, and bias confirmation, may be addressed. It is shown that different signals of metaphor investigated perform different functions in relation to metaphor, with many of them supporting use of idiosyncratic metaphor that appears to express the subjective experience of forum participants. Analysis of nouns across all sentences identified as containing signaled metaphor yields previously unknown terms that are typically associated with metaphor in this context, via a method that is grounded in the data. In addition, the summary list of metaphor signaling constructions may support further such investigations in large text corpora.

Retraction Note: Systematic pan-cancer analysis identifies transmembrane protein 158 as a potential therapeutic, prognostic and immunological biomarker.

Retraction Note: Systematic pan-cancer analysis identifies transmembrane protein 158 as a potential therapeutic, prognostic and immunological biomarker.

Culture and international innovation: a theoretical approach

PurposeThe rapidly changing international business landscape, driven by dynamic factors such as technology, emerging markets, and unpredictable crises, demands that organizations innovate to survive while gaining and sustaining competitive advantages. Culture, an intricate multilevel construct, presents challenges for transnational enterprises and international business as a key “soft” element of organizational strategy.Design/methodology/approachThis paper employs a triangulated method combining a systematic literature search, machine learning, and qualitative thematic content analysis to explore the relationship between culture and innovation within the context of international business. The analysis involved scrutinizing 697 journal articles indexed in the Web of Science database.FindingsUsing k-means, which is an unsupervised machine-learning tool in Python, and hypertext preprocessor language scripting, we identified seven topic clusters and 94 keywords. Qualitative thematic content analysis facilitated the recognition of prevailing patterns in researchers' conceptualizations of the interplay between innovation and culture. We identified influential relationships between cultural configurations and innovation.Research limitations/implicationsOur analysis contributes to developing a comprehensive research field map encompassing international business, innovation, and culture.Originality/valueThis study significantly enhances our knowledge of culture and international innovation. Future research that recognizes culture as a dynamic configuration at multiple levels (e.g. national, organizational, professional, and individual) and employs more comprehensive measures of innovation and culture could substantially advance our understanding of the intersection of culture and innovation in international business.

Analyzing the drivers of the low-carbon construction supply chain based on an integrated DEMATEL–ANP approach

PurposeCarbon reduction in the construction supply chain can critically affect the construction industry’s transition to an environmentally sustainable one. However, implementing carbon reduction in all parties is restricted because of the poor understanding of the drivers influencing the low-carbon construction supply chain (LCCSC). The purpose of this paper is to systematically identify the drivers of LCCSC, analyze their causality, and prioritize the importance of their management.Design/methodology/approachA decision-making analysis process was developed using an integrated decision-making trial and evaluation laboratory (DEMATEL)–analytical network process (ANP). First, the hierarchical drivers of the LCCSC were identified through a literature review. The DEMATEL method was subsequently applied to analyze the interactions between the drivers, including the direction and strength of impact. Finally, the ANP analysis was used to obtain the drivers’ weights; consequently, their priorities were established.FindingsVarious factors with complex interactions drive LCCSC. With respect to their influence relationships, incentive policy, regulatory policy, consumers’ low-carbon preference, market competition, supply chain performance, and managers’ low-carbon awareness have more significant center degrees and are cause drivers. Their strong correlations and influence on other drivers should be noticed. In terms of weights in the driver system, regulatory policy, consumers’ low-carbon preference, supply chain performance, and incentive policy are the key drivers of LCCSC and require primary attention. Other drivers, such as supply chain collaboration, employee motivation, and public participation, play a minor driving role with less management priority.Originality/valueDespite some contributing studies with localized perspectives, the systematic analysis of LCCSC drivers is limited, especially considering their intricate interactions. This paper establishes the LCCSC driver system, explores the influence relationships among the drivers, and determines the key drivers. Hence, it contributes to the sustainable construction supply chain domain by enabling decision-makers and practitioners to systematically understand the drivers of LCCSC and gain management implications on priority issues with limited resources.

Case Study for Predicting Failures in Water Supply Networks Using Neural Networks

This study deals with the prediction of recurring failures in water supply networks, a complex and costly task, but essential for the effective maintenance of these vital infrastructures. Using historical failure data provided by Companhia de Água e Esgotos da Paraíba (CAGEPA), the research focuses on predicting the time until the next failure at specific points in the network. The authors divided the failures into two categories: Occurrences of New Faults (ONFs) and Recurrences of Faults (RFs). To perform the predictions, they used predictive models based on machine learning, more specifically on MLP (Multi-Layer Perceptron) neural networks. The investigation unveiled that through the analysis of historical failure data and the consideration of variables including altitude, number of failures on the same street, and days between failures, it is possible to achieve an accuracy greater than 80% in predicting failures within a 90-day interval. This demonstrates the feasibility of using fault history to predict future water supply outages with significant accuracy. These forecasts allow water utilities to plan and optimize their maintenance, minimizing inconvenience and losses. The article contributes significantly to the field of water infrastructure management by proposing the applicability of a data-driven approach in diverse urban settings and across various types of infrastructure networks, including those pertaining to energy or communication. These conclusions underscore the paramount importance of systematic data collection and analysis in both averting failures and optimizing the allocation of resources within water utilities.

Systemic Risk Analysis of Safety, Progress, and Investment in the Construction of a Water Transfer Project and the Importance of Common Cause Failure

Safety, progress, and investment risks are correlated during the construction period of large-scale water transfer projects. However, previous studies have only considered individual risk factors, overlooking the potential systemic risk posed to safety, progress, and investment, as well as any underlying common cause failures. Since traditional risk analysis methods are ill-suited to addressing common cause failure, this paper’s objective was to establish a comprehensive evaluation index framework and to identify the basic events of common cause failure. To do that, we developed a risk analysis method that models common cause failure based on a Bayesian network for assessing that systemic risk. The Henan Section of the Yangtze-to-Huaihe River Water Diversion Project in China was then used as a case study. The results show that a variety of common cause failure events, such as epidemic disease, design alteration, lagged approval process, heavy rain in the flood season, renewal material and failing equipment, construction accidents, and external interference, can significantly impact the safety, progress, and investment systemic risk. Design alteration poses the greatest risk, with renewal material and failing equipment exerting the strongest influence among all common cause failure events. It is also possible to elucidate the predominant causal chains; specifically, the contributing influence of each basic failure event to the systemic risk can be clarified by adjusting their respective initial state. The failure of renewal material and failing equipment was found to significantly increase the safety risk. This study effectively simulated the complex causal relationships and uncertainties of pertinent risk factors, thereby enhancing our understanding of the systemic risk associated with safety, progress, and investment in large-scale water transfer projects.

ANALYSIS OF REGULATORY AND LEGAL SUPPORT FOR THE PREVENTION OF DIABETES MELLITUS IN CHILDREN

Diabetes mellitus has become one of the most prevalent chronic diseases in childhood today. International forecasts suggest that the incidence of diabetes will continue to rise in the coming decades, leading to a lifelong burden for millions of children worldwide. Therefore, prediction, prevention, and optimal treatment of diabetes, as well as early prevention of serious long-term complications, remain essential. Providing highly qualified medical care to diabetic patients is a crucial step in preventing the development of complications. The purpose of this article is to review existing research on diabetes prevention strategies for children, considering the different levels of medical care support available. Materials and Methods. This study investigates the organization of preventive care for children with diabetes in Ukraine. Researchers employed three key methods: bibliosemantic analysis, content analysis focused on analyzing Ukraine relevant regulations and legal frameworks, and the method of systemic approach and analysis to gain a comprehensive understanding of how preventive care is currently organized for children with diabetes in Ukraine. Results. In Ukraine, preventive measures and the provision of medical care to pediatric patients with diabetes in the past years and today are regulated by a number of documents and orders of the Ministry of Health. As part of the Comprehensive Program “Diabetes Mellitus” (1999) and in accordance with the Order of the Ministry of Health of Ukraine “On Improving the Organization of Providing Endocrinological Care to the Population of Ukraine” (2006), the State Register of Patients with Diabetes Mellitus (SYNADIAB) was created. The next step was the approval of the State Target Program “Diabetes Mellitus”. Subsequently, the Project of the national program “Health 2020: Ukrainian Dimension” became an effective tool, which outlined the main principles of the program implementation in the “Endocrinology” section for 2013-2020. It defined measures at all stages of diabetes prevention, such as primary prevention, which consists of maintaining and strengthening the general state of health and preventing or delaying the development of diabetes; secondary prevention should improve early detection of diabetes mellitus; tertiary prevention, which ensures the effectiveness and safety of treatment of children with diabetes to prevent the development of long-term complications. Today, the guidelines for the management of children with diabetes mellitus have been continued in such documents as “Diabetes Mellitus in Children”, Evidence-Based Clinical Guidelines (2023) and Standards of Care (2023). Conclusion. Diabetes mellitus is a chronic disease that ranks third in prevalence after cardiovascular and cancerous diseases and second in the structure of endocrine diseases after thyroid gland pathology. Today, the prevalence of diabetes mellitus among children is constantly increasing and, therefore, solving the issues associated with this disease is one of the priority tasks for the national health care system. This is because diabetes mellitus is associated with a high risk of complications leading to loss of work capacity, disability, and mortality across various population groups. Organizing high-quality medical care and establishing an effective prevention system based on the latest medical technologies is crucial. Diabetes demands attention. Understanding the risks and prevention of the disease can significantly increase the chances of protection and a long, healthy life through timely treatment.

Toxicity of the model protein 3×GFP arises from degradation overload, not from aggregate formation.

While protein aggregation can cause cytotoxicity, it also forms to mitigate cytotoxicity from misfolded proteins, though the nature of these contrasting aggregates remains unclear. We previously found that overproduction (op) of a three green fluorescent protein linked protein (3×GFP) induces giant aggregates, and is detrimental to growth. Here, we investigated the mechanism of growth inhibition by 3×GFP-op using non-aggregative 3×MOX-op as a control. The 3×GFP aggregates were induced by misfolding, and 3×GFP-op had higher cytotoxicity than 3×MOX-op because it perturbs the ubiquitin-proteasome system. Static aggregates formed by 3×GFP-op dynamically trapped Hsp70, causing the heat shock response. Systematic analysis of mutants deficient in the protein quality control suggested that 3×GFP-op did not cause critical Hsp70 depletion and aggregation functioned in the direction of mitigating toxicity. Artificial trapping of essential cell cycle regulators into 3×GFP aggregates caused abnormalities in the cell cycle. In conclusion, the formation of the giant 3×GFP aggregates itself is not cytotoxic, as it does not entrap and deplete essential proteins. Rather, it is productive, inducing the heat shock response while preventing an overload to the degradation system.

Implications of social network structures on socially influenced decision-making

AbstractIndividuals often make decisions in a social environment where social influence can impact on people’s decision-making domains such as online purchasing, political voting and voluntary vaccine uptake. Social influence can be recognised as the intentional or unintentional change in an individual’s belief, perception, or behaviours caused by an information diffusion process embedded in a social network. However, there is limited research on how this diffusion process is shaped by the topology or structure of the social network. This work provides an exploratory and systematic analysis of how decision-making outcomes in a population can be affected by both the structure of the social network and the starting node of where new information starts to diffuse. Simulation results considering three common network structures highlight how social networks with clear community structures lead to a larger absolute impact on decision-making outcomes and networks where the social connections follow a preferential attachment rule show the largest relative impact than the others. The results also suggest scenarios in which introducing new pieces of information to the social network can facilitate the information diffusion process and produce a more significant impact in terms of the overall population decision-making process.

Role of Culinary Indian Spices in the Regulation of TGF-β Signaling Pathway in Inflammation-Induced Liver Cancer.

Hepatocellular carcinoma (HCC) results from various etiologies, such as Hepatitis B and C, Alcoholic and Non-alcoholic fatty liver disorders, fibrosis, and cirrhosis. About 80 to 90% of HCC cases possess cirrhosis, which is brought on by persistent liver inflammation. TGF-β is a multifunctional polypeptide molecule that acts as a pro-fibrogenic marker, inflammatory cytokine, immunosuppressive agent, and pro-carcinogenic growth factor during the progression of HCC. The preclinical and clinical evidence illustrates that TGF-β can induce epithelial-to-mesenchymal transition, promoting progression and hepatocyte immune evasion. Therefore, targeting the TGF-β pathway can be a promising therapeutic option against HCC. We carry out a systemic analysis of eight potentially selected culinary Indian spices: Turmeric, Black pepper, Ginger, Garlic, Fenugreek, Red pepper, Clove, Cinnamon, and their bioactives in regulation of the TGF-β pathway against liver cancer. Turmeric and its active constituent, curcumin, possess the highest therapeutic potential in treating inflammation-induced HCC and they also have the maximum number of ongoing in-vivo and in-vitro studies.