As the population ages worldwide, ovarian cancer (OC) poses a serious public health issue for elder women. The objective of this study is to outline the global impact of OC in individuals aged 60 and above from 1990 to 2021, thus guiding specific policies and approaches for prevention, screening, and treatment. A global secondary data analysis using the Global Burden of Disease (GBD) 2021 data was performed to estimate the burden and project future trends of OC in older women (aged 60 and above). We gathered data regarding incidence, mortality rates, and disability-adjusted life years (DALYs) from the GBD 2021. For trend analysis, we employed joinpoint regression to assess temporal variations, calculating the average annual percentage change (AAPC) to quantify these trends. Furthermore, we used the age-period-cohort (APC) model to examine the influences of age, time, and birth cohort on incidence, mortality, and DALY figures. Additionally, the Bayesian age-period-cohort (BAPC) model was applied to forecast changes extending to the year 2050. A worldwide assessment of the impact of OC on public health from 1990 to 2021 highlights notable patterns: the age-standardized incidence rate (ASIR) showed a net decrease; however, its estimated annual percentage change (EAPC) was positive, indicating an overall upward trend in annual rates over the period. In contrast, the age-standardized prevalence rate (ASPR), the age-standardized DALYs, age-standardized death rate (ASDR), and the age-standardized mortality rate (ASMR) have all shown a decline. The distribution of the disease burden shows considerable regional disparities, with uncertainty analyses indicating a sustained decline in regions with a high socio‑demographic index (SDI) and a corresponding increase in regions with low‑to‑medium SDI. Among older women (aged 60 and above), the disease burden demonstrates a disproportionate concentration in younger elderly subgroups: the 60-64 age group experiences the highest incidence and prevalence burden, while mortality peaks in the 65-69 age group. This pattern is particularly pronounced in regions such as South Asia and Central Europe. Risk factor analysis identifies high body mass index (BMI) at 21% and occupational asbestos exposure at 7% as the two major risk factors contributing to OC mortality. Projections based on the BAPC model indicate that by 2050, the global prevalence of OC will continue to rise, while incidence rates are expected to persistently decline. The global fight against OC confronts dual challenges: increasing incidence rates and a shifting disease burden toward low- and middle-income regions. Future efforts must embrace targeted global strategies that integrate improved prevention, diagnosis, treatment, and chronic disease management.

Association between polycyclic aromatic hydrocarbons exposure and gestational hypertension: Insights from global and dose metabolomics analysis.

Both energy transition and external financial support are vital in stimulating global biodiversity conservation.

Stage-adjusted forecasting of radiotherapy demand and outcome benefits across income groups: Estimating survival and local control gains by 2050.

Novel quantum tunneling and fractional calculus-based metaheuristic for robust global data optimization and its applications in engineering design

Greening orthopaedic surgery: Carbon footprint, waste generation, environmental impact, and mitigation strategies.

Abstract With the increasing scale of global data transmission, to meet the security data communication needs of the Internet of Things and 5G era, and to address traditional encryption algorithms being prone to cracking and having unsatisfactory communication efficiency, a new encryption algorithm - Advanced Encryption Standard/Secure Hash Algorithm 256-bit (AES/SHA-256) is proposed. The new algorithm optimizes the instantaneous key preprocessing process by introducing the Secure Hash Algorithm 256-bit (SHA-256), outputs the Proxy Re-Encryption (PRE) value, and generates the instantaneous key using PRE. Subsequently, the instantaneous key is introduced into the Advanced Encryption Standard (AES) to construct a more secure dynamic encryption framework. The results showed that the key preprocessing and key generation took 700 μs and 1,010 μs, respectively. The algorithm encryption and decryption processes took 1,000 μs and 800 μs, respectively. The highest number of request processes per second was 380, and the response time was 3,000 μs under a load of 50 users. The AES/SHA-256 performs better than other comparison algorithms. The application test results show that the algorithm has strong instantaneous dynamics and meets the requirements of information security transmission. The AES/SHA-256 encryption algorithm has positive significance for supporting the security of the digital economy ecosystem.

Machine learning has revolutionized scientific modeling, providing breakthroughs in fields ranging from weather prediction to protein folding. However, its adoption in physics-based domains remains limited due to the lack of interpretability in traditional black-box models. In environmental sciences, where understanding underlying mechanisms is critical, symbolic regression offers an alternative by discovering transparent mathematical expressions that can represent physical principles. In this work, we demonstrate the application of symbolic regression to physical modeling through shoreline prediction, a critical area for understanding coastal evolution under climate change and human influence. Unlike traditional physics-based models, which rely on assumptions that may not generalize across diverse coastal environments, our approach evolves a population of interpretable models directly from global observational data. By optimizing both predictive accuracy and model complexity, we uncover region-specific formulations that reveal the dominant physical drivers of shoreline change. This methodology enables data-driven discovery while maintaining alignment with physical intuition, providing new insights into physical dynamics across multiple spatial and temporal scales.

International Chinese education is entering a transitional stage of deep integration of language and culture. Cultural vocabulary is the core carrier of Chinese cultural connotations and national values, and its teaching quality is directly related to the overall effectiveness of international Chinese teaching, as well as profoundly affecting the actual effectiveness of cross-cultural communication of Chinese culture. This article adopts the literature research method and data analysis method to comprehensively analyze the empirical research results of Yang Lijiao's research group at the School of Chinese Culture, Beijing Normal University. In combination with the 2024 global Chinese education development data of the China National Center for Language Education and Cooperation of the Ministry of Education, it systematically sorts out the teaching status of cultural vocabulary, focuses on analyzing the specific impact and prominent problems of semantic transparency on cultural vocabulary teaching, and then proposes targeted optimization strategies. Research has found that current cultural vocabulary teaching has problems such as outdated textbook content, lack of cultural literacy among teachers, and rigid teaching methods. Differences in semantic transparency have a significant impact on learners' acquisition effectiveness; the acquisition accuracy of high-transparency cultural vocabulary is 31.2 percentage points higher than that of low-transparency ones. This study can provide practical reference for the standardized development of cultural vocabulary teaching in international Chinese education, and assist in the efficient dissemination of Chinese culture overseas.

Survivorship of modern total hip replacement to 30 years: systematic review, meta-analysis, and extrapolation of global joint registry data.

Introduction: This study investigates the demographic characteristics, clinical presentations, angiographic findings, and associated risk factors of coronary artery disease (CAD) among 120 patients undergoing coronary angiography at the National Institute of Cardiovascular Diseases. (NICVD). Objective: The primary objective was to analyze the pattern of CAD and assess its correlation with known risk factors in a Bangladeshi population. Methods: This study employed a cross-sectional observational design. It was conducted at the Cardiology Department of the National Institute of Cardiovascular Diseases. (NICVD). The study duration was one year from July 2024 to June 2025. The study aimed to evaluate the relationship between age, sex, and the severity of coronary artery disease (CAD) in patients concurrently diagnosed with type 2 diabetes mellitus and hypertension, using data derived from coronary angiography. Results: The analysis revealed that CAD predominantly affects middle-aged individuals, with a mean patient age of 54.53 years. Males comprised most of the sample, emphasizing a gender disparity in CAD prevalence. Among angiographic patterns, Single Vessel Disease (SVD) was the most common (46.67%), followed by Double Vessel Disease (33.33%) and Triple Vessel Disease (20.00%). The Left Anterior Descending (LAD) artery was most frequently involved (62.12%), underlining its critical role in ischemic heart disease. Hypertension (37.50%) and Diabetes Mellitus (28.33%) were the most prevalent comorbid conditions, both significantly associated with multi-vessel involvement. Most patients presented with effort-related chest pain, while a substantial portion required revascularization interventions including Percutaneous Coronary Intervention (PCI) and Coronary Artery Bypass Grafting (CABG). Conclusion: This study underscores the urgent need for early identification and control of modifiable risk factors such as hypertension and diabetes to prevent CAD progression. The findings are consistent with both regional and global data, offering valuable insights for clinicians and public health policymakers aiming to reduce the CAD burden in South Asia. Limitations include a single-center design and a modest sample size, suggesting the need for larger, multi-center longitudinal studies. Medicine Today 2026, Vol.38 (1): 14-18

Many global biodiversity datasets overlook or misrepresent the knowledge of Indigenous Peoples, Local Communities, and Afro-Descendants (IPLCAD). We propose minimum data and metadata standards for a global data infrastructure on biodiversity knowledge and use, co-designed with IPLCAD, including information on language, community attribution and consent, to ensure data traceability and ethical use. This initiative integrates ancestral and academic sciences to advance inclusive biodiversity governance, addressing historical inequities for global sustainability.

The secondary scientific use of routine laboratory data will increasingly rely on LOINC as a semantic standard, particularly within the European Health Data Space (EHDS). LOINC enables the aggregation of large data sets from clinical care for research purposes, particularly in epidemiology. However, current approaches to semantic standardisation largely neglect metrological aspects-in particular, the considerable limitations of analytical standardisation for many fundamental analytes and the resulting scatter of result values across different assays that use common LOINC codes. This incomplete harmonisation leads to statistical uncertainty that must be taken into account when quantitative conclusions-such as diagnostic thresholds for analytes-are derived from aggregated, LOINC-derived data sets. In this opinion piece, we propose using the extensive global data pool generated by external quality assessment (EQA) programs to finally annotate LOINC codes with a sound and useful uncertainty metric. This represents secondary scientific use of EQA data that is analogous to and supports the secondary use of routine diagnostic data from patient care for research. With a proof-of-concept analysis, we demonstrate the feasibility of this approach, which offers a wide range of design options. We suggest that consortia of EQA providers, coding institutions, scientific societies, and the IVD industry could advance precision research through this concept. It is noteworthy that the proposed annotation strategy-linking semantic test codes to uncertainty metrics based on EQA data-is not limited to LOINC as a semantic coding system.

While cigarettes remain the most common form of tobacco product used, the global tobacco landscape is changing with the introduction and marketing of newer products. We examined the latest two waves of Global Youth Tobacco Survey data from 13 countries that implemented the survey between 2013 and 2023 to assess changes in exclusive, dual and poly-tobacco product use over time among students aged 13-15 years. Current tobacco use, defined as using any form of tobacco in the past 30 days, was categorised into six mutually exclusive variables: exclusive smoked tobacco, exclusive smokeless tobacco, exclusive heated tobacco, exclusive electronic cigarettes (e-cigarettes), dual use and poly-tobacco use. Multivariable logistic regression models adjusted for survey year, sex and age were used to estimate adjusted prevalence differences (aPDs) between survey rounds, accounting for the complex sampling design. Exclusive smoked tobacco declined significantly in six countries, with aPDs ranging from -8.8 percentage points (95% CI -11.1 to -6.6) in Latvia to -2.8 points (95% CI -4.1 to -1.4) in Paraguay.Exclusive e-cigarette use increased in nine countries, ranging from 1.2 points (95% CI 0.3 to 2.1) in Albania to 6.6 points (95% CI 4.4 to 8.8) in the Czech Republic.Dual and/or poly-tobacco use also increased in eight countries, from 0.5 points (95% CI 0.0 to 0.9) in Nicaragua to 6.6 points (95% CI 3.0 to 10.2) in San Marino. There has been a shift in tobacco use patterns, moving from smoked tobacco to e-cigarettes and poly-tobacco use. Comprehensive implementation of tobacco control strategies may contribute to addressing emerging trends in tobacco use.

The exponential growth of medical data and advancements in artificial intelligence (AI) have accelerated the development of data-driven health care. However, the secure and efficient sharing of sensitive medical data across institutions remains a major challenge due to privacy concerns, data silos, and regulatory restrictions. Traditional centralized systems are prone to data breaches and single points of failure, while existing privacy-preserving techniques face high computational and communication costs. This study aims to provide a comprehensive review of the recent advances in blockchain-based federated learning (BCFL) within the medical field. By exploring the synergistic integration of federated learning and blockchain, this review evaluates how BCFL enhances data security, supports privacy-preserving cross-institutional collaboration, and facilitates practical applications in health care, including medical data sharing, Internet of Medical Things, public health surveillance, and telemedicine. We conducted a systematic literature review using databases such as PubMed, IEEE Xplore, Web of Science, and Google Scholar. Boolean logic and domain-specific keywords were used to retrieve studies from 2018 to 2025. After automated deduplication and multistage manual screening, over 100 high-quality papers were included. These works cover BCFL's theoretical foundations, system architectures, application domains, limitations, and future directions. BCFL frameworks combine the decentralized trust and auditability of blockchain with the privacy-preserving collaborative learning capabilities of federated learning. This integration mitigates risks such as model tampering, data leakage, and a lack of incentives in federated systems. Applications span across cross-institutional medical data sharing, Internet of Medical Things, epidemic forecasting, and telemedicine. Architectures including fully coupled, flexibly coupled, and loosely coupled models offer varying trade-offs between efficiency, scalability, and security. BCFL represents a transformative paradigm for secure, collaborative, and privacy-preserving medical AI. By combining decentralized trust, incentive-driven participation, and privacy-enhancing machine learning, BCFL paves the way for next-generation smart health care systems. Despite current technical and practical challenges, BCFL demonstrates strong potential to support precision medicine, global health data collaboration, and large-scale AI deployment in health care.

Purpose This paper aims to advance an original intersubjective framework for data sovereignty governance. Grounded in constructivist concepts of shared understanding and critical realist principles of structure and agency, it moves beyond state-centric models to develop the concept of “interactive data sovereignty.” The study thereby offers a more robust theoretical and practical pathway for rebalancing power among states, platforms and individuals, contributing to a more equitable and effective global data governance. Design/methodology/approach This paper uses a conceptual-theoretical methodology rooted in intersubjective and structural analysis. The approach reconstructs data sovereignty through an intersubjective lens, shifting it from a state-centric attribute to a relational status formed through multi-actor communication. It then integrates this perspective with a structural analysis of how power embeds within data architectures. The synthesis of these two lenses directly informs the interactive data sovereignty framework, deriving its core principles – interactivity, relativity and justice – and positioning data architecture as the central governance object. Findings This study reveals that governance of data architecture fundamentally shapes power relations in dataspace. The proposed model of interactive data sovereignty demonstrates how participatory mechanisms can rebalance asymmetric relationships, with multilateral institutionalized consultations emerging as the most effective approach for reconciling data circulation and protection imperatives. This framework successfully bridges theoretical insights with practical governance solutions. Originality/value This study pioneers the integration of intersubjectivity theory with data sovereignty, proposing the novel concept of interactive data sovereignty. It innovatively identifies data architecture as the core governance object and develops participatory architecture as practical solution, offering new theoretical frameworks for rebalancing digital power disparities.

The wildlife–livestock interface (WLI) serves as a critical hotspot for zoonotic spillover of emerging viral pathogens, facilitating the transmission of viruses from wildlife reservoirs to livestock (as intermediate or bridge hosts) and subsequently to humans. This review synthesizes the multi-scale mechanisms underpinning viral spillover, including ecological drivers (such as habitat fragmentation, deforestation, intensive farming, wildlife trade, and climate change), genetic plasticity of RNA viruses (high mutation rates, quasi-species diversity, and recombination), cellular compatibility (receptor usage and tissue tropism), and viral evasion of innate immune responses (interferon antagonism via proteins like NS1, V, VP35). Special emphasis is placed on how livestock act as amplification or mixing vessels, enabling adaptation before human infection. Key case studies illustrate these processes: Henipaviruses (Nipah and Hendra viruses) demonstrate bat-to-pig/horse-to-human pathways or direct bat-to-human routes influenced by strain-specific pathogenesis and cultural practices; MERS-CoV highlights source-sink dynamics in dromedary camels with seasonal spillover linked to calving cycles. The document also explores the debated biodiversity-disease relationship (dilution vs. amplification effects), network ecology for identifying bridge hosts (rodents in human-dominated landscapes), and anthropogenic factors amplifying risk. Effective mitigation requires a One Health approach integrating surveillance in biodiversity hotspots, biosecurity at interfaces, predictive AI modeling, and global data sharing to anticipate and prevent future spillovers of viruses with pandemic potential.

Abstract This paper presents a three-stage in-depth review of multi-topic household surveys to assess whether existing data could be used to estimate a truly global moderate Multidimensional Poverty Index (MPI) that is frequently updated and extensively disaggregated. Drawing on previous studies it first summarises data sources presently used for international or national MPIs, setting out some advantages and drawbacks. The review then sets out criteria used for selection and assessment of reviewed datasets, and schematically presents survey contents and their cross-national comparability. It then assesses potential indicators for comparable multidimensional poverty indices, and identifies data gaps according to reviewed datasets. Additionally, the paper highlights some common shortcomings and comparability issues found in multi-topic household surveys, and proposes feasible adjustments. While this paper covers 152 suitable household surveys and 3 cross-country datasets, it cannot be an exhaustive review. Nor does it claim to assess the suitability of the reviewed surveys for any other purposes. It assesses data availability for a set of concrete structures of internationally comparable, frequently updated, and subnationally disaggregated moderate MPIs, in the hope of closing the gap towards global coverage. It uncovers a large data gap and sets out recommendations to address it.

Abstract Introduction Approximately 5-10% of breast cancer cases are linked to genetic predisposition, primarily involving pathogenic germline variants in BRCA1 and BRCA2, which confer a lifetime risk of 80-90% and 60-85%, respectively. Other genes like CHEK2, BRIP1, and PALB2 are associated with moderate risks (20-40%). While pathogenic variants can predispose to triple-negative breast cancer, most cases occur without these mutations. High-risk patients may opt for genetic screening and prophylactic mastectomy, which reduces breast cancer risk and improves disease-free survival, though it does not eliminate risk or increase overall survival. Objective This study aims to analyze genetic mutations and variants of uncertain significance in breast cancer cases treated at Clínica Alemana, Santiago, over the past ten years, and compare them with global data. It also examines how these findings influence clinical management, including prophylactic mastectomy. Methods and materials Retrospective descriptive study of genetic mutations in 200 women aged ≤40 with breast cancer over 10 years at Clínica Alemana, Santiago, Chile. Results In this study, 47.4% of patients received genetic counseling and all underwent testing. Pathogenic mutations were found in 18 patients, mostly in BRCA1/2, while 28 had variants of uncertain significance, primarily in CHEK2. Discussion Genetics are implicated in about 10% of breast cancer cases, consistent with our finding of 9.4% positive genetic tests. Variants of uncertain significance (VUS) occur in 2-7% of BRCA1/2-only tests and up to 10-41% in multigene panels; our study found 14.6% VUS after multigene testing. BRCA1/2 pathogenic variants were present in 4.2% of cases, aligning with reported rates of 3-4%. Prophylactic mastectomy rates vary between 40-80% in carriers of high-penetrance mutations, and are lower for moderate-penetrance genes. In our cohort, 63% of BRCA1/2 carriers and 10% with moderate-risk gene mutations underwent prophylactic mastectomy. Citation Format: M. E. Gonzalez, M. Mullins, C. Barriga, M. Bravo, D. Carvajal, J. Camacho. Genetic Mutations in a Young Population in Chile with Breast Cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS3-05-20.

Background/Objectives: To evaluate whether medical cannabis (MC) use following dysuria diagnosis is associated with increased risk of developing substance use disorder (SUD), given rising cannabis prescriptions for urologic symptoms and concerns about long-term consequences. Methods: We conducted a retrospective cohort study using the TriNetX Research Network, a federated electronic health record database with over 120 million patients. Adult patients newly diagnosed with dysuria between 2003 and 2024 were identified and stratified by subsequent cannabis exposure. MC users were defined by a cannabis-related diagnostic code within 90 days of dysuria diagnosis. Propensity score matching (PSM) was performed 1:1 by age, sex, and race. The primary outcome was a new diagnosis of SUD (cannabis, opioid, or cocaine use disorders) within 12 months. Secondary analysis included Kaplan–Meier (KM) survival estimates over 5 years. Risk ratios (RR), odds ratios (OR), and hazard ratios (HR) were calculated. OR and RR estimated the likelihood of SUD within 12 months, and HR reflected relative hazard over 5 years. Results: After excluding patients with prior SUD, the final sample included 60,544 MC patients and 98,715 general dysuria (GD) patients. The MC group had a significantly higher incidence of new SUD diagnoses (11.13%) than the GD group (2.28%), yielding a risk difference of −8.85% (95% CI: −9.11 to −8.58; p < 0.0001), relative risk 0.205, and OR 0.186. KM analysis showed lower SUD-free survival in MC (80.96%) versus GD (96.35%; log-rank p < 0.0001). MC exposure was associated with nearly fivefold increased odds of SUD within 12 months (OR = 0.186) and sixfold higher hazard over 5 years (HR = 0.163). Conclusions: Medical cannabis use after dysuria is linked to markedly increased risk and earlier onset of SUD. Careful patient selection, counseling, and monitoring are essential when prescribing MC for urologic symptoms.