Organization For Economic Cooperation Research Articles

Evaluation of Acute and Repeated Dose Oral Toxicity of Phoenix dactylifera L. Pollen Methanolic Extract in Rats

Pollen of the date palm (Phoenix dactylifera L.) has been used for thousands of years as a traditional herbal medicine for improving fertility. However, safety of plant-derived materials is an important concern to human health, and herbal remedies should be taken with adequate knowledge about their toxicity, adverse effects, purity, appropriate dosage and dose limits. The current work was undertaken to evaluate the safety of date palm pollen (DPP) and to classify it according to its oral toxicity in rats. The experiments were done according to the reported guidelines of the Organization for Economic Co-operation and Development (OECD) to assess the acute and repeated dose oral toxicity in rats. The obtained results showed neither death nor sign of toxicity or abnormality in any of the animals throughout the experimental period regarding acute and repeated dose oral toxicity. Also the results of the hematological and clinical biochemistry determinations showed no significant differences between the control and DPP-treated rat groups. It could be concluded that, the tested plant material is a relatively safe substance and its approximate LD50 defined by 2000mg/kg < LD50 < 5000mg/kg according to the OECD tests guidelines.

Does financial development and economic growth matter for services export? Cross-country evidence from OECD countries

The unprecedented growth in the export of services and its accompanying economic incentives has invited discussions on service export determinants. Against this background, the present article is an attempt to test whether the contemporary theories on causal links between exports, financial development and growth hold for services export. For realizing the objective, we employed the panel cointegration technique for Organization for Economic Co-operation and Development (OECD) countries over a period of 23 years (2000–22). Findings of both first and second generation cointegration test indicate a long run equilibrium between services export, financial development and growth. However, there are differences in the short and long association, as outcomes of panel auto autoregressive distributed lag (ARDL) approach show financial development to be significant determinant of services export only over long run. Additionally, granger causality test suggests a feedback mechanism prevalent between services export-financial development, services export-growth and financial development-growth. The empirical evidence offered in the study elucidates the significance of continuously developing the financial markets and institutions for achieving sustained growth in export of services. The bi-directional relationship between the variables also presents an opportunity to strengthen economic growth and financial development process through policies aimed at expanding services export.

Confirmation of the steroid hormone receptor-mediated endocrine disrupting potential of fenvalerate following the Organization for Economic Cooperation and Development test guidelines, and its estrogen receptor α-dependent effects on lipid accumulation

In this study, we focused on confirming the steroid hormone receptor-mediated endocrine-disrupting potential of the pyrethroid insecticide fenvalerate and unraveling the underlying mechanisms. Therefore, we assessed estrogen receptor-α (ERα)- and androgen receptor (AR)-mediated responses in vitro using a hormone response element-dependent transcription activation assay with a luciferase reporter following the Organization for Economic Cooperation and Development (OECD) test guidelines. We observed that fenvalerate acted as estrogen by inducing the translocation of cytosolic ERα to the nucleus via ERα dimerization, whereas it exhibited no AR-mediated androgen response element-dependent luciferase activity. Furthermore, we confirmed that fenvalerate-induced activation of ERα caused lipid accumulation, promoted in a fenvalerate-dependent manner in 3 T3-L1 adipocytes. Moreover, fenvalerate-induced lipid accumulation was inhibited in the presence of an ERα-selective antagonist, whereas it remained unaffected in the presence of a glucocorticoid receptor (GR)-specific inhibitor. In addition, fenvalerate was found to stimulate the expression of transcription factors that promote lipid accumulation in 3 T1-L1 adipocytes, and co-treatment with an ERα-selective antagonist suppressed adipogenic/ lipogenic transcription factors at both mRNA and protein levels. These findings suggest that fenvalerate exposure may lead to lipid accumulation by interfering with ERα activation-dependent processes, thus causing an ERα-mediated endocrine-disrupting effect.

A systematic review of recruitment and retention of ethnic minorities and migrants in obesity prevention randomised controlled trials.

Participants' recruitment and retention into community-based interventions can be challenging, especially in research involving ethnic minorities and migrants. Despite known challenges, there are limited reviews that probe recruitment and retention strategies involving ethnic minorities and migrants in the Organisation for Economic Cooperation and Development (OECD) countries. This systematic review aimed to measure recruitment and retention rates and identify the barriers and facilitators to effective recruitment and retention of ethnic minorities and migrants in community-based obesity prevention Randomised Control Trials (RCTs) in OECD countries. This review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Five databases (CINAHL, Cochrane, Embase, Medline and PsychInfo) were searched from January 2000 to March 2022, in addition to Google and Google Scholar. Methodological quality and risk of bias were assessed, and pooled analysis and meta-ethnographic analysis were conducted on the included studies. Twenty-five studies were included in the review. The pooled analysis found a 64% rate of recruitment of ethnic minorities in RCTs, with a retention rate of 71%. Key facilitators identified were-use of multiple communication channels, incentives, recruiting community champions, participant convenience and employing culturally sensitive strategies. Key barriers to participation were limited access to study sites, time constraints, limited trust, perceived fear, and anxiety. Findings suggest the importance of undertaking culturally appropriate recruitment and retention strategies to minimise barriers and facilitate effective recruitment and retention of low-income ethnic minorities and migrants in community-based research.

Constructing and implementing a green taxation system in China under the dual-carbon target

Reducing carbon dioxide emissions and achieving carbon neutrality have become hot topics of global concern. China has elevated its response to climate change to a national strategic level, where the green tax system plays a crucial role in implementing the dual-carbon strategy. This paper systematically analyzes the green tax systems of the Organisation for Economic Co-operation and Development (OECD)—encompassing environmental pollution taxes, energy taxes, vehicle and transportation taxes, and resource taxes. Based on this analysis, it proposes that China should establish a multifaceted green tax system including environmental protection tax, resource tax, and farmland occupation tax. Furthermore, it suggests an integrated approach combining systematic tax incentives such as corporate income tax, value-added tax, consumption tax, and vehicle purchase tax. This comprehensive green taxation framework, covering development, production, consumption, and emission stages, aims to drive high-quality green economic and social development through both tax incentives and restrictions, implementing a “dual-driving” mechanism. This study provides a basic framework and practical path for building a green tax system in China, especially studying the sources of different green taxes and their interrelationships, which enriches the theoretical and practical values related to taxation.

Evolving burden of lung cancer attributable to occupational carcinogens in 38 OECD countries: Global, national differences, age dynamics and implications for global health.

10576 Background: Lung cancer (LC) remains a predominant cause of global mortality and disability, particularly significant in Organization for Economic Cooperation and Development (OECD) nations. Understanding the evolving burden of LC attributable to occupational carcinogens (OC) is critical due to its prevalence in occupational settings. Methods: Employing a standardized approach from the Global Burden of Disease study, we estimated deaths and disability-adjusted life years (DALYs) caused by lung cancer linked to OC. Our analysis spans age, gender, year, and location across the 38 OECD countries from 1990-2019. Results were presented as absolute counts and age-standardized rates (ASR) per 100,000 person-years, with projections extended to 2040 using regression frameworks. Results: Deaths from LC attributable to OC increased from 120,862 (95%UI: 91,114-148,937) in 1990 to 167,917 (127,888-208,066) in 2019, with DALYs rising from 2.4 million (1.8-3.1) to 2.9 million (2.2-3.6) over the same period. The United States recorded the highest number of deaths (41,075), followed by Japan (20,496) and the United Kingdom (15,223) in 2019. Notably, the Netherlands exhibited the highest age-standardized mortality rate (ASMR) at 12.62 cases per 100,000, with Belgium and the United Kingdom following closely. Asbestos exposure accounted for the highest number of deaths (147,263), followed by silica (15,660), arsenic (3,172), and diesel engine exhaust (2,798). Males bore a significantly higher burden than females over the last three decades, with the 75-79 age group recording the highest number of deaths (32,477) and the 70-74 age group registering the highest DALYs (613,442). Conclusions: The study underscores the escalating burden of LC attributable to occupational carcinogens across OECD countries, highlighting the imperative for robust preventive measures and targeted interventions. Urgent action is warranted to mitigate exposures to known carcinogens in occupational settings, particularly among vulnerable populations, to curb the escalating public health challenge posed by occupational lung cancer.

Quantifying public and private investment in European biopharmaceutical research and development.

Robust biopharmaceutical research and development (R&D) ecosystems require investment from both the public and private sectors. In Europe, there is an interest in growing biopharmaceutical R&D given its contribution to public health and the economy, which requires an understanding of current public and private investment. In addition, recent European draft legislation has focused on the public sector's contributions to biopharmaceutical R&D to inform pharmaceutical prices. However, there is little empirical evidence on the specifics of public and private funding for medicine R&D in Europe. This paper performs aggregative data collection to quantify 2019 investment in biopharmaceutical R&D by the public and private sectors in 6 countries: Belgium, France, Germany, Norway, Poland, and the United Kingdom. We find that, across these countries, the private sector accounts for just under two-thirds of investment. We contrast results to those obtained using high-level R&D indicators from the Organization for Economic Co-operation and Development (OECD) and contextualize differences. We then provide 2013-2019 estimates for Belgium, France, Germany, and the United Kingdom (countries with data to support such analysis), and show that total spending grew over those years, although proportions attributable to each sector remained stable. These findings should provide further evidence for debates on policies to effectively grow the biopharmaceutical R&D sector.

Deciphering the global landscape of breast cancer in 38 OECD countries from 1990-2019: A benchmarking global analysis.

1046 Background: Breast Cancer (BC) stands as the primary contributor to disability and the second leading cause of death among countries belonging to the Organization for Economic Cooperation and Development (OECD). A comprehensive cross-country analysis of BC is crucial to monitor the growing burden. Methods: Employing the global burden of disease framework, we have calculated the incidence, prevalence, mortality, disability-adjusted life years (DALYs), and years of life lost (YLL) attributed to BC, categorized by age, gender, year, and geographical location across the 38 OECD member countries. Results: The total number of BC cases has witnessed a notable increase, rising from 5.5 million (95%UI: 5.1-6) in 1990 to 9 million (8.2-9.8) in 2019. BC-related deaths have also risen from 172,148 (164,284-176,147) in 1990 to 214,458 (195,692-226,347) in 2019, while DALYs have increased from 4.6 (4.5-4.8) in 1990 to 5.1 (4.8-5.5) in 2019. The annual percentage change (APC) in incidence has surged by 59% (44-75%) from 1990 to 2019. The highest APC in age-standardized incidence rates was observed in South Korea at 150% (99-205), while the most substantial decrease was recorded in Switzerland at 3%. Regarding death rates, Japan saw an 11% increase, Costa Rica 10%, Mexico 9%, South Korea 8%, and Turkey 4%, whereas the remaining countries witnessed a decrease from 1990 to 2019. In terms of DALY rates, Mexico and Japan experienced a 10% increase, South Korea 5%, Turkey 3%, with the rest showing a decrease from 1990 to 2019. Males bore a higher burden compared to females, with an APC in total incidence of 109% versus 58%, deaths at 73% versus 24%, and DALYs at 57% versus 10% from 1990 to 2019. The highest overall burden was observed in the 70+ age group, while the <70 and <20 age groups exhibited a decreasing trend. The total number of deaths is projected to continue increasing. Conclusions: In 2019, BC accounted for 6.64% of all cancer-related deaths and 7.9% of DALYs among women. Based on these findings, low-resource countries should consider reallocating funding to combat this burden, and public health policymakers need to prioritize surveillance and early screening efforts.

Prediction of mass spectrometry ionization efficiency based on COSMO-RS and machine learning algorithms.

Non-targeted analysis of high-resolution mass spectrometry (MS) can identify thousands of compounds, which also gives a huge challenge to their quantification. The aim of this study is to investigate the impact of mass spectrometry ionization efficiency on various compounds in food at different solvent ratios and to develop a predictive model for mass spectrometry ionization efficiency to enable non-targeted quantitative prediction of unknown compounds. This study covered 70 compounds in 14 different mobile phase ratio environments in positive ion mode to analyze the rules of the matrix effect. With the organic phase ratio from low to high, most compounds changed by 1.0 log units in log IE. The addition of formic acid enhanced the signal but also promoted the matrix effect, which often occurred in compounds with strong ionization capacity. It was speculated that the matrix effect was mainly in the form of competitive charge and charged droplet' gasification sites during MS detection. Subsequently, we present a log IE prediction method built using the COSMO-RS software and the artificial neural network (ANN) algorithm to address this difficulty and overcome the shortcomings of previous models, which always ignore the matrix effect. This model was developed following the principles of QSAR modeling recommended by the Organization for Economic Cooperation and Development (OECD). Furthermore, we validated this approach by predicting the log IE of 70 compounds, including those not involved in the log IE model development. The results presented demonstrate that the method we put forward has an excellent prediction accuracy for log IE (R2pred = 0.880), which means that it has the potential to predict the log IE of new compounds without authentic standards.

Development of a Zebrafish Embryo-Based Test System for Thyroid Hormone System Disruption: 3Rs in Ecotoxicological Research.

There is increasing concern regarding pollutants disrupting the vertebrate thyroid hormone (TH) system, which is crucial for development. Thus, identification of TH system-disrupting chemicals (THSDCs) is an important requirement in the Organisation for Economic Co-operation and Development (OECD) testing framework. The current OECD approach uses different model organisms for different endocrine modalities, leading to a high number of animal tests. Alternative models compatible with the 3Rs (replacement, reduction, refinement) principle are required. Zebrafish embryos, not protected by current European Union animal welfare legislation, represent a promising model. Studies show that zebrafish swim bladder inflation and eye development are affected by THSDCs, and the respective adverse outcome pathways (AOPs) have been established. The present study compared effects of four THSDCs with distinct molecular modes of action: Propylthiouracil (PTU), potassium perchlorate, iopanoic acid, and the TH triiodothyronine (T3) were tested with a protocol based on the OECD fish embryo toxicity test (FET). Effects were analyzed according to the AOP concept from molecular over morphological to behavioral levels: Analysis of thyroid- and eye-related gene expression revealed significant effects after PTU and T3 exposure. All substances caused changes in thyroid follicle morphology of a transgenic zebrafish line expressing fluorescence in thyrocytes. Impaired eye development and swimming activity were observed in all treatments, supporting the hypothesis that THSDCs cause adverse population-relevant changes. Findings thus confirm that the FET can be amended by TH system-related endpoints into an integrated protocol comprising molecular, morphological, and behavioral endpoints for environmental risk assessment of potential endocrine disruptors, which is compatible with the 3Rs principle. Environ Toxicol Chem 2024;00:1-18. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Particulate matter 2.5 air pollution mitigation strategy: the role of green investment, digitalization, and renewable energy in the organization for economic co-operation and development (OECD) countries

Particulate matter 2.5 air pollution mitigation strategy: the role of green investment, digitalization, and renewable energy in the organization for economic co-operation and development (OECD) countries

Comparative Analysis of Indoor Air Quality and Thermal Comfort Standards in School Buildings across New Zealand with Other OECD Countries

COVID-19 has improved awareness of the importance of appropriate indoor air quality (IAQ) in indoor spaces, particularly in classrooms where children are expected to learn. Research has shown that poor IAQ and temperature levels affect the cognitive performance of children. In this paper, we critically compare IAQ standards for New Zealand’s Designing Quality Learning Spaces (DQLS Document) against international benchmarks from the Organization for Economic Co-operation and Development (OECD) countries, including ASHRAE 62.1, CIBSE TM57, EN-15251, WHO AQGs, and Building Bulletins 99 and 101. The aim was to ascertain the robustness of New Zealand’s DQLS document, identify areas of superiority, and recommend the required improvement for appropriate IAQ and thermal comfort in classrooms. This comparison review focuses on IAQ parameters: CO2 levels, temperature, ventilation rates, room size, occupant density, and occupancy rates. The findings illuminate a slight lag in New Zealand’s DQLS standards compared to her international counterparts. For instance, while New Zealand’s standards align closely with WHO standards for IAQ concerning temperature and ventilation rates, the recommended CO2 range appears slightly inadequate (800 to 2000 ppm) along with occupancy and classroom size for effectively controlling classroom pollutant growth. This paper emphasises the need to align New Zealand’s IAQ and thermal comfort standards with optimal OECD benchmarks. The identified disparities present opportunities for improving learning spaces in terms of CO2 concentration, size of classroom, and occupant density in schools in New Zealand to meet globally recognised standards, ultimately creating a healthier and more conducive learning environment.

Low PISA Performance Students: Factors, Perceptions, and Improvement Strategies

The PISA assessment serves as a critical tool for evaluating and improving educational systems worldwide. The ethical implications of low performance in PISA are significant, requiring a comprehensive approach to address both immediate educational needs and broader systemic issues. By leveraging insights from PISA and implementing evidence-based interventions, policymakers and educators can work towards creating a more equitable and effective educational environment for all students. This study investigates the underlying factors contributing to the low achievement of Jordanian students in the Program for International Student Assessment (PISA) tests and proposes effective strategies to enhance their performance. PISA, developed by the Organization for Economic Co-operation and Development (OECD), evaluates 15-year-old students' skills in reading, mathematics, and science, focusing on their ability to apply knowledge in real-world contexts. Despite its global significance, Jordanian students have consistently underperformed in these assessments. The study employs a mixed-methods approach, including a comprehensive literature review, analysis of the Jordanian educational context, and surveys with educators and students. The findings reveal several key factors impacting performance: curriculum misalignment with PISA competencies, socio-economic disparities, high teacher turnover, and varying levels of teaching quality. Additionally, perceptions of PISA among educators and students highlight challenges related to unfamiliar test formats and applied knowledge requirements. Based on these insights, the study recommends a series of interventions to improve performance. These include aligning the national curriculum with PISA competencies, enhancing teacher training programs, integrating technology into learning processes, promoting extracurricular activities, and fostering active parental involvement. By addressing these areas, the study aims to provide a roadmap for policymakers, educators, and stakeholders to enhance Jordanian students' performance in future PISA assessments and overall educational outcomes.

Integrating human expertise & automated methods for a dynamic and multi-parametric evaluation of large language models’ feasibility in clinical decision-making

BackgroundRecent enhancements in Large Language Models (LLMs) such as ChatGPT have exponentially increased user adoption. These models are accessible on mobile devices and support multimodal interactions, including conversations, code generation, and patient image uploads, broadening their utility in providing healthcare professionals with real-time support for clinical decision-making. Nevertheless, many authors have highlighted serious risks that may arise from the adoption of LLMs, principally related to safety and alignment with ethical guidelines. ObjectiveTo address these challenges, we introduce a novel methodological approach designed to assess the specific feasibility of adopting LLMs within a healthcare area, with a focus on clinical nursing, evaluating their performance and thereby directing their choice. Emphasizing LLMs’ adherence to scientific advancements, this approach prioritizes safety and care personalization, according to the “Organization for Economic Co-operation and Development” frameworks for responsible AI. Moreover, its dynamic nature is designed to adapt to future evolutions of LLMs. MethodThrough integrating advanced multidisciplinary knowledge, including Nursing Informatics, and aided by a prospective literature review, seven key domains and specific evaluation items were identified as follows:1.State of the Art Alignment & Safety.2.Focus, Accuracy & Management of Prompt Ambiguity.3.Data Integrity, Data Security, Ethics & Sustainability, in accordance with OECD Recommendations for Responsible AI.4.Temporal Variability of Responses (Consistency)5.Adaptation to specific standardized terminology and Classifications for healthcare professionals.6.General Capabilities: Post User Feedback Self-Evolution Capability and Organization in Chapters.7.Ability to Drive Evolution in Healthcare.A Peer Review by experts in Nursing and AI was performed, ensuring scientific rigor and breadth of insights for an essential, reproducible, and coherent methodological approach. By means of a 7-point Likert scale, thresholds are defined in order to classify LLMs as “unusable”, “usable with high caution”, and “recommended” categories.Nine state of the art LLMs were evaluated using this methodology in clinical oncology nursing decision-making, producing preliminary results. Gemini Advanced, Anthropic Claude 3 and ChatGPT 4 achieved the minimum score of the State of the Art Alignment & Safety domain for classification as “recommended”, being also endorsed across all domains. LLAMA 3 70B and ChatGPT 3.5 were classified as “usable with high caution.” Others were classified as unusable in this domain. ConclusionThe identification of a recommended LLM for a specific healthcare area, combined with its critical, prudent, and integrative use, can support healthcare professionals in decision-making processes.

Investigating the Correlation between Air Pollution and Housing Prices in Seoul, South Korea: Application of Explainable Artificial Intelligence in Random Forest Machine Learning

South Korea’s Particulate Matter (PM) concentration is among the highest among Organization for Economic Cooperation and Development (OECD) member countries. However, many studies in South Korea primarily focus on housing characteristics and the physical built environment when estimating apartment prices, often neglecting environmental factors. This study investigated factors influencing apartment prices using transaction data for Seoul apartments provided by the Ministry of Land, Infrastructure, and Transport (MOLIT) in 2019. For this purpose, the study compared and analyzed a traditional hedonic price model with a machine learning-based random forest model. The main findings are as follows: First, the evaluation results of the traditional hedonic price model and the machine learning-based random forest model indicated that the random forest model was found to be more suitable for predicting apartment prices. Second, an importance analysis using Explainable Artificial Intelligence (XAI) showed that PM is more important in determining apartment prices than access to education and bus stops, which were considered in this study. Finally, the study found that areas with higher concentrations of PM tend to have higher apartment prices. Therefore, when proposing policies to stabilize apartment prices, it is essential to consider environmental factors. Furthermore, it is necessary to devise measures such as assigning PM labels to apartments during the home purchasing process, enabling buyers to consider PM and obtain relevant information accordingly.

Wastewater disinfection with photodynamic treatment and evaluation of its ecotoxicological effects

Research has demonstrated the presence of viruses in wastewater (WW), which can remain viable for a long period, posing potential health risks. Conventional WW treatment methods involving UV light, chlorine and ozone efficiently reduce microbial concentrations, however, they produce hazardous byproducts and microbial resistance that are detrimental to human health and the ecosystem. Hence, there is a need for novel disinfection techniques. Antimicrobial Photodynamic Inactivation (PDI) emerges as a promising strategy, utilizing photosensitizers (PS), light, and dioxygen to inactivate viruses. This study aims to assess the efficacy of PDI by testing methylene blue (MB) and the cationic porphyrin TMPyP as PSs, along a low energy consuming white light source (LED) at an irradiance of 50 mW/cm2, for the inactivation of bacteriophage Phi6. Phi6 serves as an enveloped RNA-viruses surrogate model in WW. PDI experiments were conducted in a buffer solution (PBS) and real WW matrices (filtered and non-filtered). Considering the environmental release of the treated effluents, this research also evaluated the ecotoxicity of the resulting solution (post-PDI treatment effluent) on the model organism Daphnia magna, following the Organisation for Economic Cooperation and Development (OECD) immobilization technical 202 guideline. Daphnids were exposed to WW containing the tested PS at different concentrations and dilutions (accounting for the dilution factor during WW release into receiving waters) over 48 h. The results indicate that PDI with MB efficiently inactivated the model virus in the different aqueous matrices, achieving reductions superior to 8 log10 PFU/mL, after treatments of 5 min in PBS and of ca. 90 min in WW. Daphnids survival increased when subjected to the PDI-treated WW with MB, considering the dilution factor. Overall, the effectiveness of PDI in eliminating viruses in WW, the fading of the toxic effects on daphnids after MB’ irradiation and the rapid dilution effect upon WW release in the environment highlight the possibility of using MB in WW PDI-disinfection.

Analyzing the impact of fiscal conditions on private health expenditures in OECD countries: a quantile ARDL investigation.

Organization for Economic Co-operation and Development (OECD) countries have embraced the aim of universal health coverage, as established in Sustainable Development Goal (SDG) 3.8. This goal guarantees access to quality healthcare services without financial hardship or poverty. Additionally, it requires correct and adequate financing sources. A country with weak protection for its population tends to spend less on healthcare and experiences a high share of out-of-pocket payments (OOPs), increasing the likelihood of people falling into poverty. This study aims to understand the relationship and causal effects between macroeconomic and public fiscal conditions and private health expenditure in OECD countries between 1995 and 2019. We retrieved OECD data for 26 OECD countries for the period 1995-2019. Panel AutoRegressive Distributed Lag (PARDL) and panel quantile AutoRegressive Distributed Lag (PQARDL) models were estimated to examine the relationship between private health expenditures and macroeconomic and public fiscal variables. Our results reveal a positive influence of government debt and economic freedom on private health expenditures. They also show a negative influence of the government budget balance, government health expenditures, and economic growth on private health expenditures. These results collectively suggest that public fiscal conditions will likely impact private health expenditures. The findings of this study raise concerns about the equity and financial protection objectives of universal health coverage in OECD countries.

Servicification in Global Value Chains in Emerging and Developing Asian Economies

Servicification in global value chains (GVCs) in emerging and developing Asian economies has become a trend recently. However, there have been no scientific studies to elucidate the mechanism of servicification in GVCs. To fill this gap, this study aims to investigate the involvement of service sectors in GVCs in Asian economies in terms of the quantitative interactions between service inputs and manufacturing exports and inputs and between service inputs and service exports. For this purpose, a panel vector-autoregressive model and the Trade in Value Added database of the Organization for Economic Cooperation and Development (OECD) were used for the empirical analysis during 1995–2018. The estimation results find that, first, there exist reciprocal interactions between the business services and manufacturing sectors; foreign business service inputs are induced by manufacturing exports, whereas manufacturing inputs are induced by business service exports. Second, foreign manufacturing inputs facilitate foreign business service inputs. Third, business service inputs are promoted by business service exports. These trends in the involvement of business services’ involvement in GVCs have accelerated since the mid-2000s. To enhance the role of services in GVCs, Asian economies should facilitate the removal of explicit restrictions in service trade and address regulatory divergence across countries.

Boosting Sinh Cosh Optimizer and arithmetic optimization algorithm for improved prediction of biological activities for indoloquinoline derivatives

Quantitative Structure Activity Relation (QSAR) models are mathematical techniques used to link structural characteristics with biological activities, thus considered a useful tool in drug discovery, hazard evaluation, and identifying potentially lethal molecules. The QSAR regulations are determined by the Organization for Economic Cooperation and Development (OECD). QSAR models are helpful in discovering new drugs and chemicals to treat severe diseases. In order to improve the QSAR model's predictive power for biological activities of naturally occurring indoloquinoline derivatives against different cancer cell lines, a modified machine learning (ML) technique is presented in this paper. The Arithmetic Optimization Algorithm (AOA) operators are used in the suggested model to enhance the performance of the Sinh Cosh Optimizer (SCHO). Moreover, this improvement functions as a feature selection method that eliminates superfluous descriptors. An actual dataset gathered from previously published research is utilized to evaluate the performance of the suggested model. Moreover, a comparison is made between the outcomes of the suggested model and other established methodologies. In terms of pIC50 values for different indoloquinoline derivatives against human MV4-11 (leukemia), human HCT116 (colon cancer), and human A549 (lung cancer) cell lines, the suggested model achieves root mean square error (RMSE) of 0.6822, 0.6787, 0.4411, and 0.4477, respectively. The biological application of indoloquinoline derivatives as possible anticancer medicines is predicted with a high degree of accuracy by the suggested model, as evidenced by these findings.

Comparative Analysis between Quality of Life and Human Labor in Countries Belonging to G7 and BRICS Blocks: Proposition of Discriminant Analysis Model

The aim of the present research is to identify and analyze the variables which help to effectively differentiate Quality of Life (QoL) and human labor in the G7 (Germany, France, Italy, Canada, Japan, United Kingdom, and United States of America—USA) and BRICS countries (Brazil, Russia, India, China, and South Africa) through a discriminant analysis. A discriminant analysis model is developed to classify countries as having a low, mid, or high QoL based on QoL and human labor variables. The variables used in the discriminant analysis were obtained between 2010 and 2022 from two platforms: NUMBEO variables capable of relating QoL to socioeconomic aspects and OECD’s (Organization for Economic Cooperation and Development) human-labor-related variables. Based on the results, the three variables that most discriminate the groups in order of importance are employed women in relation to the female population, the female labor force participation rate, and the female unemployment rate. Countries are classified as having a low, mid, or high QoL. The adopted technique will allow researchers and managers to classify and draw goals for action reorganization and investment in QoL and labor.