Abstract:Background: Pulmonary arterial hypertension (PAH) represents a substantial global public health burden, particularly among women of reproductive age, who face significant risks during pregnancy if affected by the condition. This study seeks to provide a thorough evaluation of the disease burden and trends of PAH in women of reproductive age (WPAH), aiming to guide the development of targeted prevention and control strategies. Methods: Data on the incidence, prevalence, disability-adjusted life years (DALYs), and deaths of WPAH were extracted from the Global Burden of Disease study (GBD) database for the years 1990 to 2021. Age-standardized rate (ASR) of incidence (ASIR) and ASR of prevalence (ASPR), ASR of DALYs (ASDR), and ASR of mortality(ASMR) were calculated at global, regional and national level, stratified by sociodemographic index (SDI) and age, to assess percentage changes and estimated annual percentage change (EAPC). The Spearman correlation test was employed to quantify the interlink between SDI and disease burden indicators, while burden disparities were analysed using decomposition analysis. Health inequality was assessed using the slope index of inequality and the concentration index of inequality. Finally, the Bayesian age-period-cohort (BAPC) model was employed to project the future disease burden. Results: In 2021, the global case of incidence, prevalence, DALYs, and deaths of WPAH all increased. Since 1990, the global incidence rate of pulmonary hypertension has increased, while the prevalence rate has remained relatively stable, and both DALYs and death rates have decreased significantly. Across the 5 SDI regions, the ASIR and ASPR in the low SDI region demonstrated a marked decline. In contrast, the ASIR in other regions remained stable or showed a slight decrease, while the ASPR remained stable or increased slightly. Nonetheless, both the ASDR and ASMR displayed a clear downward trend across regions. The global cases of WPAH across all age groups has risen, with the 45–49 age group experiencing the most substantial growth. Results from the Spearman correlation test indicated a negative correlation between SDI and the ASIR. The ASPR initially decreased and then increased with rising SDI levels, while both the ASDR and ASMR initially increased and then declined as SDI increased. Decomposition analysis highlighted that population expansion was the key determinant of fluctuations in global incidence, prevalence, DALYs and death rates. The health inequality analysis indicates relatively minor disparities in inequality across regions with different SDI levels. Furthermore, projections using the BAPC model suggest that the ASIR, ASPR, ASDR and ASMR are likely to experience a decline by 2040. Conclusions: Overall, the burden of WPAH remains substantial. However, with advancements in treatment, the burden has improved from 1990 to 2021 and is projected to continue improving by 2040.
Abstract:The global burden of cardiovascular diseases (CVD) is on the rise. Central arteriosclerosis, a critical pathological mechanism underlying CVD, is influenced by multiple factors. Given the limitations of current single-target pharmacological interventions, there is an urgent need to investigate multi-target therapeutic strategies. This study employs a network pharmacology approach, integrating data from the SwissTargetPrediction and GeneCards databases toidentify 37 intersection targets of isoflavones and central arteriosclerosis. Aprotein-protein interaction (PPI) network is constructed to pinpoint core targets, including SRC, CASP3, ESR1, EGFR, and STAT3. Gene Ontology (GO) andKyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicate that isoflavones modulate inflammatory responses, apoptosis, andoxidative stress by regulating the "Cytokine-cytokine receptor interaction" pathway. The multi-target synergistic effects of isoflavones emulate the activityof the estrogen receptor (ESR1), inhibiting endothelial cell apoptosis and suppressing STAT3-mediated inflammatory signal transduction, thereby enhancing vascular homeostasis. In comparison to traditional statins, the multi-target mechanism of isoflavones aligns more closely with the intricate pathological network of arteriosclerosis, offering novel insights for the development of natural therapeutics. This study elucidates the comprehensive therapeutic potential of isoflavones. Future experiments are necessary to validate the specific targets and dose-response effects to facilitate clinical applications.
Abstract:Background With the aging population in the Western Pacific region, the rising burden of liver cancer associated with high body mass index (BMI) has become a focus of public health concern. This study aims to assess trends in liver cancer burden linked to high BMI among the elderly (60+) from 1990 to 2021 and explore its relationships with aging, socioeconomic development, and human resources for health (HRH). Methods Data on deaths and disability-adjusted life years were sourced from the Global Burden of Disease Study (GBD) 2021, while HRH data came from GBD 2019. The estimated annual percentage change was calculated to assess trends, and decomposition analysis examined the contributing factors. Spearman’s rank correlation was used to evaluate relationships between disease burden and the Socio-demographic Index (SDI) and HRH. Results From 1990 to 2021, the liver cancer burden linked to high BMI significantly increased among the elderly in the Western Pacific. China had the highest overall burden due to its large population, while Australia saw the greatest growth. Decomposition analysis revealed that population growth and epidemiological changes were the key drivers. However, the relationship between disease burden and SDI, as well as HRH density, was weak. Conclusion The burden of liver cancer caused by high BMI in the Western Pacific region remains a serious public health issue. Promoting the early diagnosis and comprehensive intervention of obesity and its related diseases, optimizing the allocation of health resources, and strengthening the management of chronic diseases are key strategies for reducing the burden of liver cancer.
Abstract:Background Internet addiction has become a growing concern among adolescents, particularly in China, where the pervasive use of digital technology can interfere with daily life and academic performance. Previous research has highlighted various factors contributing to internet addiction, including physical activity and social interaction. However, the relationship between these factors and internet addiction remains under explored, especially in the context of Chinese middle school students. Methods This study was conducted in November 2024 among 2336 middle school students from five secondary schools in Guangdong Province, China. A self-reported questionnaire was distributed to measure students’ demographic information, physical activity levels, social interaction, and internet addiction. Physical activity was assessed using the International Physical Activity Questionnaire(IPAQ) short form, and internet addiction was measured using the Chinese Internet Addiction Scale(CIAS-R). Regression analysis was performed to examine the relationships between physical activity, social interaction, and internet addiction. Results The findings revealed that physical activity was initially a significant predictor of internet addiction, but its effect diminished when social interaction was included in the model. Social interaction emerged as a strong predictor of internet addiction, explaining a large portion of the variance. Furthermore, a significant interaction effect was found between physical activity and social interaction, with both factors contributing jointly to the prediction of internet addiction. The final model (R^2 = 0.651) explained 65.1% of the variance in internet addiction, with social interaction(β= 0.107, p < 0.01) and the interaction term (β = 0.071, p < 0.05) being statistically significant. Conclusion This study underscores the importance of both physical activity and social interaction in predicting internet addiction among Chinese middle school students. While physical activity alone had a limited effect, the interaction between physical activity and social interaction was a significant predictor of internet addiction. These findings suggest that interventions targeting both physical activity and social engagement may be effective in mitigating the risk of internet addiction among adolescents. Future research should explore the causal relationships and additional factors influencing internet addiction.
Abstract:This study investigates the impact of digital talent flow on industrial restructuring in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), considering the threshold effect of technological innovation and spatial dependence among cities. The study emphasizes that the benefits of talent mobility extend beyond inflow to include the broader dynamics of both inflow and outflow, along with the accompanying spill overs of knowledge, technology, and experience. Using panel data from cities in the GBA from 2003 to 2021, our results show: First, there is no significant spatial dependence in the rationality of industrial structure among cities in the GBA. However, negative spatial dependence is observed in the quantity of industrial structure upgrading, while positive spatial dependence is noted in the quality. Second, the flow of digital talent promotes the rationality and quality of industrial structure but suppresses the quantity of industrial structure upgrading. The robustness test confirms the importance of peripheral cities, and policy implications from these findings suggest tailored strategies for peripheral areas.
Abstract:Objective: This study aims to analyse the long-term trends and future projections of liver cancer burden in China from 1990 to 2035, focusing on incidence, prevalence, mortality, and disability-adjusted life years (DALYs), using data from the Global Burden of Disease Study and a Bayesian Age-Period-Cohort model. Methods: Age-standardized rates for incidence, prevalence, mortality, DALYs, and years lived with disability (YLDs) were used to assess the liver cancer burden from1990 to 2021. A Bayesian Age-Period-Cohort model was applied to project trends from2021to2035. Results: From 1990 to 2021, age-standardized liver cancer incidence slightly decreased, from 13.51 to 13.29 per 100,000, while rates for males rose from18.94to20.00 per 100,000. Mortality and DALYs significantly decreased, particularly in males, but prevalence declined modestly for both sexes. The liver cancer burden peaked in different age groups for males (50–54) and females (65–69). Future projections (2021–2035) suggest a 31.7% increase in incidence, from197,000 cases in 2021 to 259,000 in 2035, and a 31.2% rise in prevalence. DALYs and deaths are projected to increase by 30.5% and 35.9%, respectively. Conclusion: Liver cancer burden in China has decreased in terms of age-standardized incidence, mortality, and DALYs. However, absolute numbers are expected to rise by 2035, particularly among males and older populations. This highlights the need for strengthened prevention, early detection, and treatment strategies to address the growing liver cancer burden.
Abstract:Background: Depression in children and adolescents poses significant public health challenges, impacting individual and societal well-being. Traditional methods for diagnosing and predicting depression often rely on symptomatic assessments post-onset, delaying intervention. Recent advancements in machine learning (ML) provide new opportunities for early prediction and prevention. Methods: This study leveraged data from the U.S. National Health Interview Survey(NHIS) from 2004 to 2014, involving 27,642 participants aged 4-17. We employed machine learning models, including XGBoost, Random Forest, Decision Tree, and Bagging Classifier, to analyze a vast array of physical activity and health behavior data. These models were evaluated for their accuracy in predicting mental health indicators. Results: The XGBoost model demonstrated notable accuracy (77.5%) in forecasting mental health scores, closely followed by the Random Forest classifier (77.4%). The models were further assessed for their ability to classify varying degrees of mental anxiety, with the redefined categories of 'Mild', 'Moderate', and 'Severe'. The retrained XGBoost model showed enhanced accuracy across these categories (81.7%- 85.7%), with AUC values indicating reliable differentiation between mental health states. Discussion: This study expands the scope of ML in predicting depression, highlighting the intricate relationship between diverse health behaviors and mental health. The predictive accuracy of the models underscores the potential of M Linearly detection and intervention for depression. Future research should focus on refining these models for broader application and exploring their utility in real-world clinical settings.
Abstract:Pancreatic ductal adenocarcinoma (PAAD) is characterized by its highly aggressive nature, presenting substantial challenges in clinical management. Surgical resection remains a potential curative option; however, most patients receive a diagnosis at intermediate or advanced stages, where the tumor often invades nearby critical blood vessels, nerves, and organs, thus reducing the probability of successful resection. Additionally, the issues of postoperative recurrence and metastasis require urgent attention and resolution. The applicationof single-cell transcriptome sequencing technology holds promise for exploringtumor heterogeneity, mechanisms of drug resistance, the plasticity ofthe immune microenvironment, and independent prognostic markers in PAAD. This article aims to provide a comprehensive review of current treatment strategies for PAAD, critically evaluating their strengths and limitations, and to summarize the novel applications of transcriptomics in identifying tumor-targeted therapeutic and diagnostic targets, thereby offering theoretical support for its implementation inPAAD treatment.
Abstract:Frugality represents a fundamental aspect of traditional Chinesecultural values, consistently shaping individual behavior. Effectively stimulatingresidents' intrinsic motivation to adopt low-carbon consumption practices is crucial for reducing carbon emissions in China. This research utilizes aquestionnair survey to investigate the low-carbon consumption intentions of residents in Shanxi Province, utilizing a structural equation model for analysis andprediction with SPSS 26.0 and AMOS 26.0. Drawing on the Theory of PlannedBehavior (TPB), the study investigates the influence of attitude, subjective norms, and perceived behavioral control on low-carbon consumption intentions, withaparticular focus on the moderating role of frugality values. The findings not onlyreaffirm the moderating function of Confucian cultural values but also extend theTPB framework by incorporating frugality as a moderating factor. Consistent withprior research, the results indicate a positive relationship between low-carbonconsumption intentions and attitudes, subjective norms, and perceived behavioral control. Although frugality values did not exert a direct effect on low-carbon consumption intentions, they significantly moderated the relationships between attitude and perceived behavioural control with consumption intentions. However, no significant moderating effect was observed for subjective norms, adding complexity to the existing literature. These findings contribute to a deeper understanding of individual consumption behaviour and offer empirical insights into the role of cultural values in shaping low-carbon consumption intentions, providing novel strategies for enhancing public engagement in sustainable consumption practices in Shanxi Province.
Abstract:Timely and accurate identification of plant diseases is essential for increasing plant productivity. The widely used state-of-the-art CNN-based models still face challenges and limitations on leaf images under complex backgrounds due to a lack of a global receptive field and self-attention mechanism. This study proposes a lightweight transfer learning-based vision transformer architecture (TVITA) for the automatic identification of plant leaf diseases without using any convolution. Two popular PlantVillage datasets—the original dataset (OD) with 55,448 images and the augmented dataset (AD) with 61,486 images—were used for model training (70%), validation (20%), and testing (10%). The proposed TVITA model, leveraging transfer learning by fine-tuning the pre-trained VITSO model on the augmented dataset, outperformed both the VITSO and VITSA models, which were trained from scratch on the OD and AD, respectively. The TVITA achieved a recognition accuracy of 97.85%, precision of 96.58%, recall of 97.50%, F1 score of 96.95%, and AUC of 98.76% on the OD testing set, and 97.50%, 97.00%, 97.07%, 96.99%, and 98.74% on the AD testing set. The results indicate that the proposed TVITA model achieves stateof-the-art accuracy, greater robustness, and lower computational cost compared with popular stateof-the-art CNN-based architectures. This study highlights the efficacy of transfer learning in enhancing ViT models' performance for plant disease identification, suggesting potential applications in other domains requiring high classification accuracy.