Search Results (261)

Background/Objectives: The burden of early-onset cancer (cancer incidence <50 years) has increased globally. Early-onset cancer carries significant societal and economic consequences. We aim to provide updated estimates for incidence and mortality of early-onset cancer. Methods: We analyzed the Global Burden of Disease Study 2021 to describe the incidence, death, age-standardized incidence rate (ASIR), age-standardized death rate (ASDR) from early-onset cancer (i.e., cancer in people aged 15–49), and its changes from 2000 to 2021 (reflected as annual percent change [APC]), using Joinpoint regression analysis. Results: In 2021, there were 3.16 million new cases and 989,650 deaths from early-onset cancer. From 2000 to 2021, the ASIR of early-onset cancer increased (APC: 0.40%, 95% CI 0.32 to 0.47%), with the highest increase observed in the Eastern Mediterranean region (APC: 1.63%, 95% CI 1.53 to 1.72%), whereas the ASDR decreased across most regions. The ASIR for early-onset cancer had a higher increase in females (APC: 0.62%, 95% CI 0.51 to 0.73%) than males (APC: 0.14%, 95% CI 0.04 to 0.23%). Breast cancer (n = 567,900) constituted the highest burden of incident cases, followed by non-melanoma skin (n = 507,810) and cervical cancers (n = 307,430). The highest increase in the ASIR was observed in non-melanoma skin cancer (APC:2.18%, 95% CI 1.85–2.51%), thyroid (APC: 1.70%, 95% CI 1.60 to 1.79%), and testicular (APC:1.37%, 95% CI 0.96 to 1.78%) cancers. The greatest increases in the ASDR were observed in peripheral nervous system cancer (APC: 0.97%, 95% CI 0.82 to 1.11%) and multiple myeloma (APC: 0.62%, 95% CI 0.51 to 0.72%). Conclusions: From 2000 to 2021, the age-adjusted incidence of early-onset cancer increased, with variation across regions and cancer types. Immediate measures are required at a global, regional, and national level to mitigate the burden of early-onset cancer. Full article

Medicinal plant species are crucial biological resources, and yet their conservation in the Kingdom of Saudi Arabia remains insufficiently studied. This study conducts a comprehensive gap analysis of 74 priority medicinal plant species in the Kingdom of Saudi Arabia to assess their spatial distribution, identify conservation gaps and propose strategic recommendations. Occurrence records were collected from field surveys and global biodiversity databases, followed by ecogeographical land characterization and conservation gap analyses using the CAPFITOGEN3 tools. The results reveal significant disparities in in situ and ex situ conservation efforts, with two biodiversity hotspots, Asir and Jazan, containing the highest species diversity. While 66 species occur within protected areas, seven species are currently only recorded outside protected areas, indicating opportunities for expanding conservation efforts. Complementarity analysis identified 13 optimal protected areas for priority medicinal plants’ conservation, alongside 20 potential sites outside protected areas that could serve as other effective area-based conservation measures. Ex situ conservation remains critically limited for many species, with only 10 represented in genebanks and all accessions currently stored internationally, although some medicinal plant species may have broader global distributions. To bring about improved outcomes of conservation, the expansion of in situ conservation coverage, integration of other effective area-based conservation measures, strengthening of national genebanks and leverage of biotechnology and geospatial tools is recommended by this study. The findings of this study can be used to develop a more systematic and sustainable approach to the conservation of medicinal plants in the Kingdom of Saudi Arabia. Full article

This paper introduces a new unit-Burr distortion function constructed via a transformation of the Burr random variable. The distortion can be applied to existing base copulas to create new copula families. The relationships of tail dependence coefficients and tail orders between the base bivariate copula and the unit-Burr distorted copula are derived. The unit-Burr distortion-induced family of copulas includes well-known copula classes, such as the BB1, BB2, and BB4 copulas, as special cases. The unit-Burr distortion of existing bivariate copulas may result in a family of copulas with both lower and upper tail coefficients ranging from 0 to 1. An empirical application to the rates of return for Microsoft and Google stocks is presented. Full article

Background: Prostate cancer (PCa) is one of the most common malignancies among men in Saudi Arabia and contributes significantly to cancer-related morbidity and mortality. The objective of this survey was to evaluate community awareness and screening practices related to PCa among men in the Asir region of Saudi Arabia. Method: A cross-sectional study was conducted from 5 October to 25 December 2024 among men aged 40 and above in the Asir region, excluding those with a prior PCa diagnosis. Using convenience sampling, 399 participants were recruited via social media and community outreach. Data were collected through a self-administered online questionnaire covering demographics, medical history, PCa knowledge, information sources, prevention, screening awareness, and barriers. Informed consent was obtained from all participants. Results: The study comprised 399 male participants, with 37.09% aged 40–50, 36.34% aged 51–60, and 26.56% over 60. Most participants (363; 90.97%) were married, 245 (61.4%) had a university education, 282 (70.67%) lived in urban areas, and 180 (45.11%) were employed. Over half of the participants, 222 (55.63%), had a personal history of prostate problems. Additionally, 272 (68.17%) had health insurance, and 153 (38.34%) reported a monthly income between 10,000 and 14,999 SAR. The study found that 329 (82.5%) participants had good knowledge of PCa but only 197 (49.4%) had good awareness of screening methods. Key predictors of good awareness of PCa screening included a personal history of prostate problems (odds ratio—OR = 4.791, p = 0.000, confidence interval—CI 2.727–8.418) and health insurance (OR = 0.359, p = 0.000, CI 0.203–0.636). Common barriers to screening were affordability, n = 116 (29.07%), and perceived good health, n = 201 (50.37%). Additionally, 154 participants (38.59%) found screening uncomfortable, while 156 (39.59%) believed the Digital Rectal Exam (DRE) was harmful or embarrassing. Significant differences in perceived barriers were found based on age (F = 11.449, p < 0.001), education (F = 2.608, p = 0.051), occupation (F = 3.668, p = 0.026), family history (F = 17.407, p < 0.001), and income (F = 5.148, p = 0.006). Conclusions: The study highlights a significant gap between general knowledge and specific awareness of prostate cancer (PCa) screening among men in the Asir region. Although 82.5% demonstrated good overall knowledge, only 49.4% were aware of screening methods, and just 44.36% had undergone PSA testing. Common barriers included perceived good health, fear of diagnosis, embarrassment, and financial concerns. However, due to the use of convenience sampling, online distribution, and geographic restriction to the Asir region, the findings may not be generalizable to the broader male population in Saudi Arabia, particularly older men and those in rural areas. Addressing these gaps requires targeted education, empowerment of healthcare providers, and coordinated public health strategies to enhance early detection and reduce the PCa burden. Full article

Background: With a high human immunodeficiency virus (HIV) adult prevalence, people living with HIV (PLHIV) in Botswana continue to experience a high burden of comorbid HIV and cancer. We sought to investigate the trends of acquired immunodeficiency syndrome (AIDS) defining cancers (ADCs), non-AIDS defining cancers (NADCs), and associated risk factors in PLHIV compared with those without HIV. Methods: We analyzed data from adults aged ≥18 years reported in Botswana National Cancer Registry and National Data Warehouse. The crude, age-standardized incidence rate (ASIR), standardized incidence ratios (SIRs) of cancers and time trends were computed. Risk factors were determined using the Cox-regression model. Results: Over a 30-year period, 27,726 cases of cancer were documented. Of these, 13,737 (49.5%) were PLHIV and 3505 (12.6%) were people without HIV and 10,484 (37.8%) had an unknown HIV status. Compared to the HIV-uninfected, the PLHIV had higher and increasing trends in the cancer incidence overall during the study period (from 44.2 to 1047.6 per 100,000; p-trend < 0.001) versus (from 1.4 to 27.2 per 100,000; p-trend < 0.001). The ASIRs also increased in PLHIV for overall ADCs, NADCs and other sub-types like cervical, lung, breast, and conjunctiva cancers (p-trend < 0.001). Further, PLHIV had elevated SIRs for cervical cancer, Kaposi sarcoma in males and some NADCs. The most common risk factors were HIV infection and female sex for ADCs incidence and advanced age and being HIV-uninfected for NADCs incidence. Conclusions: Increasing trends of ADCs and NADCs during ART expansion were observed among PLHIV compared to those without HIV highlighting a greater need for targeted effective prevention and screening strategies including the provision of access to timely HIV and cancer treatment. Full article

Sparse borehole sampling at contaminated sites results in sparse and unevenly distributed data on soil pollutants. Traditional interpolation methods may obscure local variations in soil contamination when applied to such sparse data, thus reducing the interpolation accuracy. We propose an adaptive graph convolutional network with spatial autocorrelation (ASI-GCN) model to overcome this challenge. The ASI-GCN model effectively constrains pollutant concentration transfer while capturing subtle spatial variations, improving soil pollution characterization accuracy. We tested our model at a coking plant using 215 soil samples from 15 boreholes, evaluating its robustness with three pollutants of varying volatility: arsenic (As, non-volatile), benzo(a)pyrene (BaP, semi-volatile), and benzene (Ben, volatile). Leave-one-out cross-validation demonstrates that the ASI-GCN_RC_G model (ASI-GCN with residual connections) achieves the highest prediction accuracy. Specifically, the R for As, BaP, and Ben are 0.728, 0.825, and 0.781, respectively, outperforming traditional models by 58.8% (vs. IDW), 45.82% (vs. OK), and 53.78% (vs. IDW). Meanwhile, their RMSE drop by 36.56% (vs. Bayesian_K), 38.02% (vs. Bayesian_K), and 35.96% (vs. IDW), further confirming the model’s superior precision. Beyond accuracy, Monte Carlo uncertainty analysis reveals that most predicted areas exhibit low uncertainty, with only a few high-pollution hotspots exhibiting relatively high uncertainty. Further analysis revealed the significant influence of pollutant volatility on vertical migration patterns. Non-volatile As was primarily distributed in the fill and silty sand layers, and semi-volatile BaP concentrated in the silty sand layer. At the same time, volatile Ben was predominantly found in the clay and fine sand layers. By integrating spatial autocorrelation with deep graph representation, ASI-GCN redefines sparse data 3D mapping, offering a transformative tool for precise environmental governance and human health assessment. Full article

As a re-emerging disease, the worldwide burden and trends of yellow fever (YF) remain inadequately quantified. This study aims to assess the incidence of YF both globally and in major endemic regions from 1990 to 2021. Utilizing data from the Global Burden of Disease (GBD) database, we evaluated the burden of YF. We employed an age–period–cohort model to assess the influence of age, period, and cohort on the incidence of YF from 1992 to 2021. A secondary data analysis based on GBD database showed the following: in 2021, there were 86,509 incident cases of YF. Between 1990 and 2021, the number of incident cases decreased by 74.7%, while the age-standardized incidence rate (ASIR) declined at an EAPC of −4.76% (95% confidence interval: −5.10 to −4.42). In 2021, the highest ASIRs of YF were observed in Western Sub-Saharan Africa, Central Sub-Saharan Africa, and Eastern Sub-Saharan Africa. The analysis of age effects indicates that children aged 5–10 years old exhibit the highest incidence rate. Both period and cohort effects demonstrated a decline in morbidity risk. The decomposition analysis identified epidemiological changes as the primary factor contributing to the global reduction in the YF burden. Despite considerable reduction in incidence, YF remains a significant public health threat in Sub-Saharan Africa. Full article

Unregulated irrigation with partially industrial effluents may lead to heavy metal contamination in crops and pose significant human health risks, especially in developing countries like India. Therefore, the present study aimed to quantify six heavy metals (Cd, Cr, Cu, Fe, Mn, and Zn) in soil and wheat irrigated with paper mill effluent, assess plant responses, and evaluate associated health risks for consumers. For this, a field study was conducted across ten sites (five effluent-irrigated, five borewell-irrigated as control), analyzing soil and wheat tissues for metal concentrations and calculating risk indices including bioaccumulation factor (Bf), translocation factor (Tf), Dietary Intake of Metals (DIM < 1), Health Risk Index (HRI < 1), and Target Hazard Quotient (THQ < 1). Results indicated high concentrations of Cd and Cr in effluent-irrigated soils and wheat tissues (root > stem > leaves > grains) compared to control sites, with some values exceeding permissible limits. Although the THQ values for heavy metals were below 1, indicating a low immediate health risk, concentrations of Cd and Cr in both soil and crop tissues exceeded acceptable safety standards. This study provides empirical evidence supporting the need for effluent treatment and policy interventions to mitigate agricultural contamination from the use of industrial effluents and protect public health. Full article

Tobacco use remains a critical global health issue, with extensive research focusing on its impact on public health, particularly its strong association with oral cavity cancer. It is a leading cause of preventable disease and death worldwide, affecting millions each year. Despite increased awareness and regulatory measures, tobacco continues to pose significant challenges, prompting ongoing investigations into its health effects and related behaviors. Objective: This study aims to conduct a bibliometric analysis of smoking and smokeless tobacco research from 2014 to 2024, focusing on identifying key research trends, influential contributors, emerging topics, and collaborative networks on a global scale. Methods: A dataset of 2694 research papers from PubMed was analyzed using bibliometric tools. Keyword co-occurrence, authorship patterns, and institutional collaborations were mapped to reveal dominant themes and trends. Additionally, country-specific publications were examined to assess geographical contributions and emerging research frontiers. Results: The analysis indicates a 7.3% annual increase in publications, with a peak in 2021 likely influenced by COVID-19. Research topics have shifted from traditional tobacco-related health impacts, such as lung cancer and cardiovascular diseases, to newer areas like e-cigarettes and social determinants of health. Strong international collaborations were noted, with the U.S., China, and Europe as dominant contributors. Emerging research frontiers include electronic nicotine delivery systems and strategies aimed at controlling tobacco-related health risks. Conclusion: This bibliometric study highlights significant growth in tobacco-related research over the past decade. Evolving trends reflect a shift toward newer tobacco products and public health challenges. These findings provide valuable insights for shaping future research agendas and informing global tobacco control policies. Full article

This study integrated molecular dynamics (MD) simulations with machine learning techniques, specifically Linear, Ridge, and Support Vector Regression, to predict the thermodynamic properties of amorphous silicon (a-Si) under varying conditions. The MD simulations provided a detailed dataset that captured the atomic-level behavior of the a-Si, which enabled exploration of how thermodynamic factors, such as the cooling rate, temperature, and pressure, affect the material’s density, internal energy, and enthalpy. Machine learning models were trained on this dataset and demonstrated exceptional predictive accuracy with $R^2$ values that exceeded 0.95 and minimal root-mean-square errors. The results reveal that the temperature and pressure significantly influenced the thermodynamic properties of the a-Si, while the cooling rate had a minor effect. The models generated isobaric and isothermal curves, which offered deeper insights into the thermodynamic behavior of the a-Si and complemented traditional MD simulations by providing a more efficient means to explore thermodynamic states. This work highlights the potential of machine learning to accelerate the study of materials by enabling faster exploration of thermodynamic behavior and the generation of additional data. This approach enhances the understanding of the equation of state of a-Si and opens new avenues for applying this hybrid modeling technique to other materials. Full article

The conflicts among the landscape water demand and other urban water requirements are motivating improvements in water sustainability in arid urban areas. The accurate estimation of urban landscape plants’ evapotranspiration (ET_PLT) is crucial for effective irrigation management practices. This study examined two factor-based approaches—the Water Use Classification of Landscape Species (WUCOLS) and the Landscape Irrigation Management Program (LIMP)—in conjunction with the formula developed by Penman–Monteith to calculate the landscape irrigation water demand in Saudi Arabia. The reference evapotranspiration (ET_r) was calculated utilizing 40 years of recorded meteorological data from various locations in Saudi Arabia. Notable variations in ET_r were observed both geographically between different regions and seasonally within regions. The highest, lowest, and moderate ET_r values were recorded in Riyadh, Mecca, and Asir, measuring 9.5, 6.7, and 5.3 mm, respectively. Regarding the decoupling approaches, the moderate species factor (K_s) of WUCOLS was compared to the three levels of managed stress (K_sm) in LIMP, categorized as “low, moderate, and high”. The statistical analysis revealed a significant advantage of K_s moderate over K_sm low, with a 37.5% reduction in the average ET_PLT. Although no significant differences were observed between moderate K_s and K_sm, the ET_PLT derived from WUCOLS was 16.7% lower than that from LIMP. Conversely, the advantage shifted towards K_sm high, which demonstrated a 20% decrease in the ET_PLT estimates. These results support the Saudi Green Initiative by furnishing essential data for sustainable water management in arid regions, promoting a science-driven approach to enhance water use efficiency and alleviate water scarcity. Full article

This study explores the impact of green transformational leadership (GTL) on employee retention in the hospitality sector, with a specific focus on the mediating roles of employee satisfaction and perceived green organizational support (PGOS). Grounded in self-determination theory and organizational support theory, the research examines both the direct and indirect effects of GTL on employee retention. GTL fosters a sustainability-driven work environment, motivating employees through shared environmental values, which enhances their job satisfaction and perceived organizational support. These factors, in turn, strengthen employees’ commitment and willingness to stay within the organization. Using SmartPLS 3 for structural equation modeling (SEM), data collected from hospitality employees were analyzed to assess the hypothesized relationships. The findings confirm that GTL positively influences employee retention, both directly and indirectly, through the mediating effects of employee satisfaction and PGOS. This study contributes to the growing body of knowledge on sustainable leadership by highlighting the crucial role of green leadership practices in improving employee retention in environmentally conscious organizations. Implications for managers and policymakers in the hospitality sector are discussed, emphasizing the need for sustainability-driven leadership approaches to enhance workforce stability. Full article

Background: Gastrointestinal cancer imposes a heavy public health burden worldwide. The six major subtypes of GI cancer cases accounted for over a quarter of the total cancer cases in China. We examined and predicted the amount and trend of the burden of gastrointestinal cancer in China. Methods: We collected the crude incidence rate (CIR), age-standardized incidence rate (ASIR), age-standardized prevalence rate (ASPR), crude DALY rate, age-standardized DALY rate, crude death rate (CDR), and age-standardized death rate (ASDR) by gender, age, and risk factors from the Global Burden of Disease 2021 database, covering the period from 1990 to 2021. We used an age–period–cohort (APC) model to calculate the age, period, and cohort effects separately and a Bayesian APC model to predict the epidemiological trends and the age-specific incidence. Results: In 2021, 1,957,948 incidence cases of gastrointestinal cancers were estimated in China, with an incidence rate of 137.62 per 100,000. The burden was notably higher for males, with tobacco use accounting for 26.48% of male GI cancer DALYs versus dietary risks (13.22%) being predominant in females. Metabolic risk factors showed concerning growth trends, with high fasting plasma glucose increasing by 166.06% in males and 275.72% in females. The age–period–cohort analysis revealed peak incidence at advanced ages (>80 years) for most GI cancers. By 2050, China’s gastrointestinal cancer burden is projected to increase substantially, with total cases reaching 2.92 (95% CI: 2.27, 3.77) million (49.06% increase) and crude incidence rate rising to 230.51 (95% CI: 178.98, 298.03) per 100,000 (a 67.49% increase), predominantly driven by colorectal cancer (41.74%) and stomach cancer (21.47%). While crude incidence rates show consistent increases across all GI cancers, particularly in males, age-standardized incidence rates are predicted to decrease for most cancer types except for male colorectal and biliary tract cancers. Conclusions: The burden of gastrointestinal cancer in China is growing rapidly and will continue to increase. The performance of ASIR indicated that population aging is a potential driver of the increasing burden. Cancer control policies should focus on older people. It is critical to distinguish prevention strategies by gender and age according to the major risk factors. Full article

Background: Employing the most recent dataset from the Global Burden of Disease (GBD) Study 2021, this report sought to delineate the current epidemiologic landscape of breast cancer in Asian women. Methods: We examined the evolving trends in disease prevalence and explored the correlations between breast cancer and factors such as age, temporal periods, and generational cohorts. We utilized an autoregressive integrated moving average (ARIMA) model to predict the incidence and deaths of breast cancer in Asia. Results: From 1990 to 2021, the age-standardized incidence rate (ASIR), age-standardized DALYs rate (ASDR), and age-standardized mortality rate showed an overall upward trend for Asian women with breast cancer. In 2021, the high-income Asia Pacific region had the highest ASIR value, while South Asia had the lowest ASIR value. The highest age-standardized mortality rate and ASDR values in 2021 occurred in Southeast Asia, while the lowest values for these metrics were in East Asia. In 2021, breast cancer incidence and DALYs were highest in the 50–54 age group, with deaths peaking in the 55–59 age group. The leading risk factor attributed to breast cancer deaths in Asia in 1990 and 2021 was a “diet high in red meat”. Breast cancer incidence and mortality rates are expected to continue to rise in Asia over the next 10 years. Conclusions: The burden of breast cancer in Asian women is increasing, especially in low SDI countries. This study highlighted the differences between populations and regions and predicted the incidence and mortality rates of breast cancer in Asia over the next decade using an ARIMA model. An increased awareness of breast cancer risk factors and prevention strategies is necessary to reduce breast cancer burden in the future. Full article

Objectives: To evaluate the medical image segmentation performance of monochromatic images in various energy levels. Methods: The low-density module (25 mm in diameter, 6 Hounsfield Unit (HU) in density difference from background) from the ACR464 phantom was scanned at both 10 mGy and 5 mGy dose levels. Virtual monoenergetic images (VMIs) at different energy levels of 40, 50, 60, 68, 74, and 100 keV were generated. The images at 10 mGy reconstructed with 50% adaptive statistical iterative reconstruction veo (ASIR-V50%) were used to train an image segmentation model based on U-Net. The evaluation set used 5 mGy VMIs reconstructed with various reconstruction algorithms: FBP, ASIR-V50%, ASIR-V100%, deep learning image reconstruction (DLIR) with low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strength levels. U-Net was employed as a tool to compare algorithm performance. Image noise and segmentation metrics, such as the DICE coefficient, intersection over union (IOU), sensitivity, and Hausdorff distance, were calculated to assess both image quality and segmentation performance. Results: DLIR-M and DLIR-H consistently achieved lower image noise and better segmentation performance, with the highest results observed at 60 keV, and DLIR-H had the lowest image noise across all energy levels. The performance metrics, including IOU, DICE, and sensitivity, were ranked in descending order with energy levels of 60 keV, 68 keV, 50 keV, 74 keV, 40 keV, and 100 keV. Specifically, at 60 keV, the average IOU values for each reconstruction method were 0.60 for FBP, 0.67 for ASIR-V50%, 0.68 for ASIR-V100%, 0.72 for DLIR-L, 0.75 for DLIR-M, and 0.75 for DLIR-H. The average DICE values were 0.75, 0.80, 0.82, 0.83, 0.85, and 0.86. The sensitivity values were 0.93, 0.91, 0.96, 0.95, 0.98, and 0.98. Conclusions: For low-density, non-enhancing objects under a low dose, the 60 keV VMIs performed better in automatic segmentation. DLIR-M and DLIR-H algorithms delivered the best results, whereas DLIR-H provided the lowest image noise and highest sensitivity. Full article