Search Results (334)

Cervical cancer remains a significant cause of cancer-related mortality among women, particularly in low- and middle-income countries. High-throughput technologies, such as microarrays, have facilitated the comprehensive analysis of gene expression profiles in cervical cancer, enabling the identification of key differentially expressed genes (DEGs) involved in their pathogenesis. The publicly available microarray datasets, including GSE39001, GSE9750, GSE7803, GSE6791, GSE63514, and GSE52903 in combination with bioinformatic database predictions, were used to identify differential expression genes, potential biomarkers, and therapeutic targets for cervical cancer; additionally, we undertook bioinformatic analysis to determine gene ontology and possible miRNA targets related to our DEGS. Our analysis revealed several DEGs significantly associated with cervical cancer progression, such as cell death, regulation of DNA replication, protein binding, processes, and transcription factors. The most relevant transcription factors (TFs) identified were SP1, ELF3, E2F1, TP53, RELA, HDAC, and FOXM1. Importantly, the DEGs with more important changes were 11 coding genes that were upregulated (KIF4A, MCM5, RFC4, PLOD2, MMP12, PRC1, TOP2A, MCM2, RAD51AP1, KIF20A, AIM2) and 14 that were downregulated (CXCL14, KRT1, KRT13, MAL, SPINK5, EMP1, CRISP3, ALOX12, CRNN, SPRR3, PPP1R3C, IVL, CFD, CRCT1), which were associated with cervical cancer. Interestingly, hub proteins KIF4A, NUSAP1, BUB1B, CEP55, DLGAP5, NCAPG, CDK1, MELK, KIF11, and KIF20A were found to be potentially regulated by several miRNAs, including miR-107, miR-124-3p, miR-147a, miR-16-5p, miR-34a-5p, miR-34c-5p, miR-126-3p, miR-10b-5p, miR-23b-3p, miR-200b-3p, miR-138-5p, miR-203a-3p, miR-214-3p, and let-7b-5p. The relationship between these genes highlights their potential as candidate biomarkers for further research in treatment, diagnosis, and prognosis. Full article

Poor diet is the leading preventable risk factor for death worldwide, associated with over 10 million premature deaths and USD 8 trillion related costs every year. Artificial intelligence or AI is rapidly emerging as the most historically disruptive, innovatively dynamic, rapidly scaled, cost-efficient, and economically productive technology (which is increasingly providing transformative countermeasures to these negative health trends, especially in low- and middle-income countries (LMICs) and underserved communities which bear the greatest burden from them). Yet widespread confusion persists among healthcare systems and policymakers on how to best identify, integrate, and evolve the safe, trusted, effective, affordable, and equitable AI solutions that are right for their communities, especially in public health nutrition. We therefore provide here the first known global, comprehensive, and actionable narrative review of the state of the art of AI-accelerated nutrition assessment and healthy eating for healthcare systems, generated by the first automated end-to-end empirical index for responsible health AI readiness and maturity: the Responsible Health AI readiness and Maturity Index (RHAMI). The index is built and the analysis and review conducted by a multi-national team spanning the Global North and South, consisting of front-line clinicians, ethicists, engineers, executives, administrators, public health practitioners, and policymakers. RHAMI analysis identified the top-performing healthcare systems and their nutrition AI, along with leading use cases including multimodal edge AI nutrition assessments as ambient intelligence, the strategic scaling of practical embedded precision nutrition platforms, and sovereign swarm agentic AI social networks for sustainable healthy diets. This index-based review is meant to facilitate standardized, continuous, automated, and real-time multi-disciplinary and multi-dimensional strategic planning, implementation, and optimization of AI capabilities and functionalities worldwide, aligned with healthcare systems’ strategic objectives, practical constraints, and local cultural values. The ultimate strategic objectives of the RHAMI’s application for AI-accelerated public health nutrition are to improve population health, financial efficiency, and societal equity through the global cooperation of the public and private sectors stretching across the Global North and South. Full article

Environmental factors motivate migration across the globe, calling for better planning. Although the US experienced such movements during the COVID-19 pandemic, literature on population mobility and outcomes for receiving communities in the US is scarce. We use a mixed-methods case study approach to explore the COVID-era population movement trends in the US Northeast (NE) Region and their outcomes for receiving communities to draw lessons for strategic regional planning aiming to achieve sustainable and equitable outcomes of disaster-induced movements. Utilizing the Statistics of Income data and focus group data collected from 27 local experts in 22 rural counties of NE, which experienced the highest relative numbers of in-movers between 2016 and 2020, the findings revealed the top receiving counties were predominantly rural areas where urbanites moved from within NE. This movement challenged the housing market and services, disproportionately burdening socioeconomically disadvantaged groups in receiving communities. The COVID-19 experience opened a window of opportunity for regional planning to prepare desirable outcomes of such mobilities by addressing existing issues in receiving communities while incorporating pulse and slow population movements into the agenda. The right policy timing and communication among communities are keys to building trust and ensuring integration of newcomers into receiving communities. Full article

Sustainable urban water governance in rapidly transforming cities requires integrative decision-making frameworks capable of balancing social equity, economic efficiency, and environmental resilience. This study develops an Integrative Decision-Making Framework (IDMF) for optimizing urban water policy in Yerevan, Armenia, built upon AI- and GIS-assisted diagnostics and incorporating a Governance Readiness Index (GRI) together with spatial hotspot overlay analysis. The framework employs an AHP–TOPSIS multi-criteria structure to evaluate five policy alternatives—leakage reduction, demand-side management, decentralized reuse, green–blue infrastructure, and data-driven governance—based on normalized quantitative indicators across social, economic, and ecological domains. Results show that Leakage Reduction (A1) and Data-Driven Governance (A5) consistently rank as the top-performing strategies across both baseline and sensitivity scenarios, while equity-prioritized weightings enhance social outcomes without significantly compromising economic performance. The approach also demonstrates robustness under ±10–20% weight variations. Acknowledging limitations related to data availability and expert-based judgments, the study outlines the minimum governance and data-readiness conditions required for transferability. The IDMF thus advances decision-support science in urban water management by integrating governance feasibility with spatial diagnostics and provides adaptable guidance for mid-income cities facing institutional and environmental constraints. Full article

This paper examines how air pollution affects the distribution of labor income within firms. We build a within-firm incentive model and show that air pollution, treated as an exogenous shock, reduces production efficiency and increases operating uncertainty. In response, firms compress both employee and executive compensation. Because executive pay carries a larger weight on performance- and equity-based components and is therefore more sensitive to profit volatility, it declines by more, mechanically narrowing within-firm pay dispersion. At the same time, rank-and-file wages display downward rigidity. The result is a “synchronized decline with sharper cuts at the top,” a form of spurious equality. Using 2014–2022 data on non-financial A-share listed firms in China, we find that a 1% increase in air pollution is associated with a 0.37% average decline in labor income. Effects are stronger in labor-intensive firms and in firms with weaker unions. Two-stage least squares estimates indicate real consequences: talent outflows and reduced innovation. By linking air quality to wage setting, human capital, and innovation, our results reveal a sustainability channel through which pollution undermines decent work and inclusive growth—issues of global relevance for urban economies. The mechanisms we document are likely to generalize beyond China and inform integrated policies that combine environmental regulation with labor-market and innovation policy to support a just and sustainable transition. Full article

Introduction: Cardiovascular disease (CVD) remains a primary cause of mortality worldwide, with persistent geographic disparities driven by a complex interplay of risk factors. Continual updates of localized variations in CVD mortality are essential to develop targeted interventions for optimizing disease and healthcare management. Methods: This study investigated associations between CVD mortality and a comprehensive set of biological, environmental, behavioral, and socioeconomic factors across all U.S. counties, employing correlation, geospatial visualization, stepwise multiple regression, and machine learning models to evaluate the importance of risk associations. Results: Significant disparities in CVD mortality trend were observed across race, age, sex, and region, with elevated rates among older adults, men, and Blacks, particularly in southeastern states exhibiting severe social vulnerability. Correlation analysis identified disease management (e.g., COPD, hypertension, medication non-adherence), environmental factors (PM2.5), lifestyle behaviors (e.g., smoking, sleep duration), and socioeconomic status (e.g., poverty, single-parent households, education) as important contributors to CVD mortality. Conversely, higher household income, physical activity, and cardiac rehabilitation participation were strong protectors. Multiple regression explained 66.9% variance in CVD mortality, recognizing PM2.5, smoking, and medication non-adherence as top associated factors. Random Forest models underscored COPD’s predictive dominance, followed by medication non-adherence, smoking, and sleep duration. Conclusions: The findings highlight the geospatial connection of risk factors to CVD mortality disparities across U.S. counties. They emphasize the critical importance of data-driven strategies targeting air quality, tobacco control, social inequities, and chronic disease management to mitigate CVD burden and promote health equity. Full article

Roll-to-roll production of thin organic and large-area electronic (TOLAE) devices often involves a two-step process per functional layer: a continuous, un-pattered deposition of the film and subsequent structuring process, such as laser ablation. Thin-film organic devices should be protected using ultra-barrier films. To perform laser ablation of functional layers on top of such barrier films, in particular that of transparent electrodes, highly selective laser ablation is required to completely remove the layers without damaging the thin-film barrier layers underneath. When targeting highly selective laser ablation of indium tin oxide (ITO) on top of silicon nitride (SiN) barrier layers with a 1064 nm picosecond or 1030 nm femtosecond laser, we observed the emergence of visible large-scale patterns due to local variations in ablation quality. Our investigations using a very sensitive Raman spectroscopy setup show that the observed ablation variations stem from subtle differences in optical path length within the heat-stabilized plastic substrates. These variations are likely caused by minute, localized changes in the refractive index, introduced during the bi-axial stretching process used in film fabrication. Depending on the optical path length, these variations lead to either constructive or destructive interference between the incoming laser beam and the light reflected from the back surface of the substrate. By performing laser ablation under an angle such that the reflected and incoming laser beam do not spatially overlap, highly selective uniform laser ablation can be performed, even for two stacked optically transparent layers. Full article

Background: Despite growing recognition of vaccination social science as essential to immunization strategies, the field’s evolution, geographic distribution, and research patterns remain poorly characterized. This study provides the first comprehensive mapping of the social science literature on vaccination over eight decades. Methods: We conducted a bibliometric analysis of peer-reviewed publications indexed in PubMed from their inception, using a systematic search strategy that combined vaccination and social science terms. Publications were analyzed using the Bibliometrix R package (version 5.0) to examine temporal trends, author productivity, institutional contributions, geographic distribution, and thematic evolution globally. Results: We retrieved 8005 eligible publications. Analysis highlighted three chronological research phases: sporadic early work (1945–1980, n = 85), sustained growth (1981–2019, n = 2743), and unprecedented expansion since the COVID-19 era (2020–2024, n = 4563). Annual publications reached a peak in 2022 (n = 1686). Research spans 146 countries but remains concentrated in high-income countries, with the United States (n = 10,230), China (n = 3796), and Canada (n = 2288) leading production. The top 20 institutions were from the United States (n = 8), United Kingdom (n = 4), and Canada (n = 3), with a few institutions from African countries. International collaboration was moderate (19.44%). Thematic analysis revealed a clear evolution from biological science (1963–1999) to socio-behavioural science, with an emphasis on vaccine hesitancy, trust, communication, and health equity (2015–2024). Conclusions: Vaccination social science has grown steadily over the decades, with a sharp rise in research during the COVID-19 pandemic. Most studies were from high-income countries, underscoring the need for enhanced social science capacity in low- and middle-income countries. As the focus of immunization efforts shifts toward issues like vaccine hesitancy and trust, broader collaboration and inclusion will be key to improving vaccine uptake worldwide. Full article

The Inner Mongolia Cashmere goat (IMCG) industry is a major contributor to global cashmere production, with fleece traits serving as key economic indicators that directly impact both income and the long-term sustainability of the industry. When genome-wide SNPs are used to estimate kinship matrices, the traditional animal model implicitly assumes that all SNPs have the same effect-size distribution. However, in practice, there are differences in the genetic mechanisms and complexity of different traits. We conducted a genome-wide association study (GWAS) on 2299 IMCGs genotyped with 67,021 SNPs, which were obtained after imputation. The traits measured included cashmere yield (CY), wool length (WL), cashmere length (CL), and cashmere diameter (CD), with a total of 33,564 records collected. The top 5% to 20% of the significant SNPs from the GWAS were used as biological prior information. We then assigned proportional weights based on their contribution to the overall genetic variance and further integrated them with the remaining loci to construct a kinship relationship matrix for estimating genetic parameters and genomic breeding value. By incorporating prior marker information from the GWAS, it was found that the heritability estimates for CY, WL, CL, and CD were 0.26, 0.37, 0.09, and 0.35, respectively. For CY and CL, integrating the top 5% of prior SNP markers yielded the highest genomic prediction accuracies of 0.742 and 0.673, representing improvements of 16.67% and 19.75% over models that did not utilize prior information. In contrast, for WL and CD, the highest accuracies of 0.851 and 0.780 were achieved by integrating the top 10% of prior SNP markers, reflecting improvements of 9.81% and 10.14%, respectively. Compared with the conventional GBLUP method, this method of integrating GWAS-derived prior markers for genomic genetic evaluation can significantly improve the accuracy of genomic prediction for fleece traits in IMCGs. This approach facilitates accurate selection for fleece traits in IMCGs, enabling accelerated genetic progress through long-term breeding programs. Full article

Wind and sand disaster prevention is a critical challenge for global environmental sustainability, with mechanical wind fences being key engineering measures. Current fences, including solid and permeable types, often struggle to balance environmental impact, windbreak efficiency, and stability. Solid fences provide effective sand control but have limited windbreak efficiency, while permeable fences improve airflow but require deep burial and are prone to erosion on uneven terrain. This study proposes a novel composite wind fence with a polylactic acid (PLA) sandbag base and a fiber mesh top, combining stability and permeability. We assessed windbreak performance using computational fluid dynamics simulations and verified results through wind tunnel experiments. Results show that the novel composite wind fence enhances windbreak efficiency and stability by optimizing airflow distribution, with the PLA sandbag base suppressing high–speed airflow and mesh fence weakening of leeward side vortices. Under wind speeds of 10 m/s, 18 m/s, and 28 m/s, the effective protection distance of the novel composite wind fence improved by 22.33% to 36.51%, 10.96% to 34.22%, and 0.94% to 28.98%, respectively, compared to single PLA and mesh wind fence. The optimal row spacing for the novel wind fences in three rows is 12 h when the incoming wind speed is 10 m/s, while the recommended spacings are 8 h and 6 h for wind speeds of 18 m/s and 28 m/s, respectively, ensuring continuous and effective protection. These findings present a novel wind fence technology with improved wind resistance, a more stable structure, and prolonged protective effects, offering an effective solution for environmental conservation initiatives aimed at preventing wind and sand disasters while promoting the sustainability of ecosystems. Full article

Background: Adolescent depression is highly prevalent in low- and middle-income countries (LMICs). Identifying top key risk factors is necessary to inform effective prevention program design. Machine learning (ML) offers a powerful approach to analyze complex multidomain of data to identify the most relevant predictors and estimate risks for mental health problems. This paper applies ML to study risks for adolescent depression to enhance adolescent depression prevention efforts in LMICs. Methods: Six ML approaches (e.g., Explainable Boosting Machine, random forests, and XGBoost) were applied to study the risks of depression. Data were drawn from a digital health intervention study conducted in Kenya (year 2024–2025, n = 269). Multiple domains of childhood and adolescent adversity and stress experiences were used to predict adolescent depression (using PHQ9-A). Findings: We found that ML was a valuable approach in the early identification of adolescents at risk for depression. Among the six ML approaches applied, the random forest approach outperformed other ML approaches, especially when multiple domains of risks were included. We also found that childhood adversity or home adversity alone were not strong predictors for depression. Adding adolescent stress experiences and community school adversity experiences significantly improves the accuracy and predictability of depression. Using the top-15 and top-20 ranking factors, we achieved 74.8% and 75.1% accuracy in depression prediction, which was similar to the accuracy when all 49 adverse/stress factors were included in the predictive model (78.3%). Conclusions: Innovative ML and modern predictive modeling approaches have the potential to transform modern preventive mental health care by better utilizing multidomain data to identify individuals at risk for developing depression early and identify top risk factors (for targeted individuals and/or populations). Findings from ML can inform tailored intervention design to better mitigate risks in order to prevent depression problem development. They can also inform the better utilization of resources to target high-need cases and key determinants, which is particularly useful for LMICs and low-resource settings. This paper illustrates an example of how to move toward this direction. Future research is needed to validate the approach. Full article

The economic viability of solar drying mainly depends on the appropriate design of air collectors, which are the main parts of a solar dryer. Although the V-groove collector has been reported to have one of the highest efficiencies, no comprehensive parameter analysis on this collector has been reported in the literature. This detailed study investigates the influence of different operating and design variables on the outlet temperature and the efficiency of the air collector. The parameter analysis also contributed to the development of the most effective design guidelines. The parameters examined include solar radiation, airflow rate, incoming air temperature, collector length, height of the vee, the spacing between the top of the vee and the transparent cover, number of such covers, and the thickness of the back insulation. The airflow rate is identified to be the essential operating parameter that affects the efficiency, and a better heat transfer rate is noticed in the intermediate flow state. It is also found that to achieve the best performance, it is necessary to maintain a mass airflow rate between 0.015 and 0.055 kg/m²s, to have incoming air at a near-atmospheric temperature, and to have two transparent covers on top. Full article

In this study, we provide, to our knowledge, one of the first multidimensional, data-driven evaluations of green education performance in Turkish higher education, combining the THE Education Score, THE Impact Score, and the UI GreenMetric Education & Research Score (GM-ED) with institutional characteristics, and situating the analysis within SDG 4 (Quality Education). While universities worldwide increasingly integrate sustainability into their missions, systematic evidence from middle-income systems remains scarce. To address this gap, we compile a dataset of 50 Turkish universities combining three global indicators—the Times Higher Education (THE) Education Score, THE Impact Score, and the UI GreenMetric Education & Research Score (GM-ED)—with institutional characteristics such as ownership and student enrollment. We employ descriptive statistics; correlation analysis; robust regression models; composite indices under equal, PCA, and entropy-based weighting; and exploratory k-means clustering. Results show that integration of sustainability into curricula and research is the most consistent predictor of SDG-oriented performance, while institutional size and ownership exert limited influence. In addition, we propose composite indices (GECIs). GECIs confirm stable top performers across methods, but mid-ranked universities are volatile, indicating that governance and strategic orientation matter more than structural capacity. The study contributes to international debates by framing green education as both a measurable indicator and a transformative institutional practice. For Türkiye, our findings highlight the need to move beyond symbolic initiatives toward systemic reforms that link accreditation, funding, and governance with green education outcomes. More broadly, we demonstrate how universities in middle-income contexts can institutionalize sustainability and provide a replicable framework for assessing progress toward SDG 4. Full article

This study estimates student loan borrowers’ repayment burdens (RBs) in South Korea. Using data from the Survey Report on Labor Conditions by Employment Type and novel administrative records, we estimate life-cycle earnings profiles by income quantile through RIF regression. These estimates are then used to derive RBs for hypothetical borrowers under income-contingent loans (ICLs) and mortgage-type loans, and to evaluate RBs for actual ICL borrowers by matching them with estimated income profiles. The findings suggest that Korea’s student loan system plays a positive role in expanding access to higher education, particularly through ICLs. Many low-income students who benefited from ICLs are later found in the top income deciles. However, raising the repayment threshold irrespective of borrower income may delay repayment and reduce system efficiency. These results underscore the importance of aligning repayment rules with borrowers’ earnings trajectories to ensure both equity and the long-term sustainability of the loan system. Full article

Background: Asthma is the most prevalent chronic illness in children worldwide, contributing to significant morbidity, health care utilization, and economic burden. In the United States, approximately five million children are affected by asthma. This review explores the environmental contexts and lifestyle determinants of pediatric asthma, with a focus on the Inland Empire (IE) region of Southern California. The IE’s unique geographic landscape and importance as a major transportation hub highlights its critical role for understanding how both environmental and structural factors exacerbate asthma burden within the pediatric population. Variables such as household income, parental education levels, and lack of community-based asthma programs were explored. Despite significant burdens, the IE remains under-represented in asthma research, contributing to persistent disparity. Methods: A narrative literature review and regional data analysis were conducted via PubMed, Scopus, and Google Scholar (2000–2025), alongside data from the CDC, CDPH, and American Lung Association. Key words used included “pediatric asthma, Inland Empire, air pollution, asthma disparity, emergency department utilization, socioeconomic status.” Inclusion criteria were: (1) studies or reports focusing on pediatric asthma (ages 0–17), (2) articles addressing environmental, socioeconomic, or health care-related risk factors, and (3) research with either national, state-level, or IE-specific data. Exclusion criteria were: (1) articles not in English, adult-only asthma studies, and (3) publications without original data or reference to pediatric asthma burden, management, or outcomes. Titles and abstracts were screened for relevance, and full texts were reviewed when abstracts met inclusion criteria. A total of 61 studies, reports, and data sources met this criterion and were included into this review. Results: The IE—comprised of San Bernardino (SB) and Riverside Counties— is home to four of the top five most polluted cities in North America. Vehicle emissions and industrial waste are concentrated in the region due to limited air circulation from surrounding mountains that entrap pollutants. Pediatric asthma ED visit rates in SB and Riverside were 60.5% and 59.3%, compared to California’s average of 56.7%. Hospitalization rates for children aged 0–4 were also higher in SB (24.4%) compared to the state average (17.3%). The elevated rates among school-aged children underscore the crucial need for interventions aimed at improving air quality, enhancing asthma management, and increasing access to preventive health care. Conclusions: Pediatric asthma in the IE reflects heightened environmental risks, socioeconomic barriers, and gaps in health care access. Addressing these disparities requires targeted interventions, policies, and region-specific research to enhance long-term management strategies and outcomes for vulnerable pediatric populations. Full article