Quantitative impacts of meteorology and emissions on the long-term trend of O3 in the Yangtze River Delta (YRD), China from 2015 to 2022.

Extensive spatiotemporal analyses of long-trend surface ozone in the Yangtze River Delta (YRD) region and its meteorology-related and emission-related have not been systematically analyzed. In this study, by using 8-year-long (2015-2022) surface ozone observation data, we attempted to reveal the variation of multiple timescale components using the Kolmogorov-Zurbenko filter, and the effects of meteorology and emissions were quantitatively isolated using multiple linear regression with meteorological variables. The results showed that the short-term, seasonal, and long-term components accounted for daily maximum 8-hr average O3 (O3-8 hr) concentration, 46.4%, 45.9%, and 1.0%, respectively. The meteorological impacts account for an average of 71.8% of O3-8 hr, and the YRD's eastern and northern sections are meteorology-sensitive areas. Based on statistical analysis technology with empirical orthogonal function, the contribution of meteorology, local emission, and transport in the long-term component of O3-8 hr were 0.21%, 0.12%, and 0.6%, respectively. The spatiotemporal analysis indicated that a distinct decreasing spatial pattern could be observed from coastal cities towards the northwest, influenced by the monsoon and synoptic conditions. The central urban agglomeration north and south of the YRD was particularly susceptible to local pollution. Among the cities studied, Shanghai, Anqing, and Xuancheng, located at similar latitudes, were significantly impacted by atmospheric transmission-the contribution of Shanghai, the maximum accounting for 3.6%.

A methodological framework for estimating ambient PM2.5 particulate matter concentrations in the UK.

Scientific evidence sustains PM2.5 particles' inhalation may generate harmful impacts on human beings' health; therefore, their monitoring in ambient air is of paramount relevance in terms of public health. Due to the limited number of fixed stations within the air quality monitoring networks, development of methodological frameworks to model ambient air PM2.5 particles is primordial to providing additional information on PM2.5 exposure and its trends. In this sense, this work aims to offer a global easily-applicable tool to estimate ambient air PM2.5 as a function of meteorological conditions using a multivariate analysis. Daily PM2.5 data measured by 84 fixed monitoring stations and meteorological data from ERA5 (ECMWF Reanalysis v5) reanalysis daily based data between 2000 and 2021 across the United Kingdom were attended to develop the suggested approach. Data from January 2017 to December 2020 were employed to build a mathematical expression that related the dependent variable (PM2.5) to predictor ones (sea-level pressure, planetary boundary layer height, temperature, precipitation, wind direction and speed), while 2021 data tested the model. Evaluation indicators evidenced a good performance of model (maximum values of RMSE, MAE and MAPE: 1.80 µg/m3, 3.24 µg/m3, and 20.63%, respectively), compiling the current legislation's requirements for modelling ambient air PM2.5 concentrations. A retrospective analysis of meteorological features allowed estimating ambient air PM2.5 concentrations from 2000 to 2021. The highest PM2.5 concentrations relapsed in the Mid- and Southlands, while Northlands sustained the lowest concentrations.

Assessment and prediction of Water Quality Index (WQI) by seasonal key water parameters in a coastal city: application of machine learning models.

The Water Quality Index (WQI) provides comprehensive assessments in river systems; however, its calculation involves numerous water quality parameters, costly in sample collection and laboratory analysis. The study aimed to determine key water parameters and the most reliable models, considering seasonal variations in the water environment, to maximize the precision of WQI prediction by a minimal set of water parameters. Ten statistical or machine learning models were developed to predict the WQI over four seasons using water quality dataset collected in a coastal city adjacent to the Yellow Sea in China, based on which the key water parameters were identified and the variations were assessed by the Seasonal-Trend decomposition procedure based on Loess (STL). Results indicated that model performance generally improved with adding more input variables except Self-Organizing Map (SOM). Tree-based ensemble methods like Extreme Gradient Boosting (XGB) and Random Forest (RF) demonstrated the highest accuracy, particularly in winter. Nutrients (Ammonia Nitrogen (AN) and Total Phosphorus (TP)), Dissolved Oxygen (DO), and turbidity were determined as key water parameters, based on which, the prediction accuracy for Medium and Low grades was perfect while it was over 80% for the Good grade in spring and winter and dropped to around 70% in summer and autumn. Nutrient concentrations were higher at inland stations; however, it worsened at coastal stations, especially in summer. The study underscores the importance of reliable WQI prediction models in water quality assessment, especially when data is limited, which are crucial for managing water resources effectively.

An analytical approach for identifying trend-seasonal components and detecting unexpected behaviour in psychological time-series.

The recent advances in technological capabilities have led to a massive production of time-series data and remarkable progress in longitudinal designs and analyses within psychological research. However, implementing time-series analysis can be challenging due to the various characteristics and complexities involved, as well as the need for statistical expertise. This paper introduces a statistical pipeline on time-series analysis for studying the changes in a single process over time at either a population or individual level, both retrospectively and prospectively. This is achieved through systemization and extension of existing modelling and inference techniques. This analytical approach enables practitioners not only to track but also to model and evaluate emerging trends and apparent seasonality. It also allows for the detection of unexpected events, quantifying their deviations from baseline and forecasting future values. Given that other discernible population- and individual-level changes in psychological and behavioural processes have not yet emerged, continued surveillance is warranted. A near real-time monitoring tool of time-series data could guide community psychological responses across multiple ecological levels, making it a valuable resource for field practitioners and psychologists. An empirical study is conducted to illustrate the implementation of the introduced analytical pipeline in practice and to demonstrate its capabilities.

Comparative bibliometric analysis of artificial intelligence-assisted polyp diagnosis and AI-assisted digestive endoscopy: trends and growth in AI gastroenterology (2003-2023).

Introduction: Artificial intelligence is already widely utilized in gastroenterology. This study aims to comprehensively evaluate the research hotspots and development trends within the field of AI in gastroenterology by employing bibliometric techniques to scrutinize geographical distribution, authorship, affiliated institutions, keyword usage, references, and other pertinent data contained within relevant publications. Methods: This investigation compiled all pertinent publications related to artificial intelligence in the context of gastrointestinal polyps and digestive endoscopy from 2003 to 2023 within the Web of Science Core Collection database. Furthermore, the study harnessed the tools CiteSpace, VOSviewer, GraphPad Prism and Scimago Graphica for visual data analysis. The study retrieved a total of 2,394 documents in the field of AI in digestive endoscopy and 628 documents specifically related to AI in digestive tract polyps. Results: The United States and China are the primary contributors to research in both fields. Since 2019, studies on AI for digestive tract polyps have constituted approximately 25% of the total AI digestive endoscopy studies annually. Six of the top 10 most-cited studies in AI digestive endoscopy also rank among the top 10 most-cited studies in AI for gastrointestinal polyps. Additionally, the number of studies on AI-assisted polyp segmentation is growing the fastest, with significant increases in AI-assisted polyp diagnosis and real-time systems beginning after 2020. Discussion: The application of AI in gastroenterology has garnered increasing attention. As theoretical advancements in AI for gastroenterology have progressed, real-time diagnosis and detection of gastrointestinal diseases have become feasible in recent years, highlighting the promising potential of AI in this field.

The Strong Correlation Between Multiple Births and Preterm Birth Rates in Greece From 1991 to 2022.

Background This study aims to investigate the correlation between the rising preterm birth rate (PBR) in Greece from 1991 to 2022 and the incidence of multiple births. Methodology Official data on live births in Greece from 1991 to 2022 were sourced from the Hellenic Statistical Authority. The PBR, defined as the number of live births occurring at <37 gestational weeks, and the multiple birth rate (MBR), representing live births from multifetal gestations, were calculated per 100 total live births. The relationship between the PBR and the MBR was evaluated using the non-parametric Spearman's rank correlation coefficient (rho). This association was confirmed through linear regression models, with MBR as the independent variable and PBR as the dependent variable, calculating the beta coefficient (ß) and the coefficient of determination (R-squared). Results A very strong direct positive correlation was identified between PBR and MBR throughout the study period, with a Spearman's rho of 0.950 (p < 0.001). This conclusion was further supported by the linear regression model, which yielded a ß coefficient of 3.32 (95% confidence interval = 2.78 to 3.86, p < 0.001). The R-squared was 0.838, indicating that the change in MBR explained 83.8% of the rise in PBR. The strongest correlations were observed for moderate PBR (32-33 weeks) with a rho of 0.962 (p < 0.001) and late PBR (34-36 weeks) with a rho of 0.940 (p < 0.001). During the period of a steep increase in prematurity rates in the country (1991-2011), an almost perfect correlation between PBR and MBR (rho = 0.987, p < 0.001) was noted. However, in recent years (2011-2022), characterized by a marginal increase in PBR, this association diminished, with a rho of 0.655 (p = 0.021). Conclusions This analysis revealed a strong positive correlation between the PBR and MBR in Greece from 1991 to 2022, underscoring the significant impact of multiple pregnancies on the substantial increase in preterm births within the Greek population.

Mapping the evolution and research landscape of ferroptosis-targeted nanomedicine: insights from a scientometric analysis.

Objective: Notable progress has been made in "ferroptosis-based nano drug delivery systems (NDDSs)" over the past 11 years. Despite the ongoing absence of a comprehensive scientometric overview and up-to-date scientific mapping research, especially regarding the evolution, critical research pathways, current research landscape, central investigative themes, and future directions. Methods: Data ranging from 1 January 2012, to 30 November 2023, were obtained from the Web of Science database. A variety of advanced analytical tools were employed for detailed scientometric and visual analyses. Results: The results show that China significantly led the field, contributing 82.09% of the total publications, thereby largely shaping the research domain. Chen Yu emerged as the most productive author in this field. Notably, the journal ACS Nano had the greatest number of relevant publications. The study identified liver neoplasms, pancreatic neoplasms, gliomas, neoplasm metastases, and melanomas as the top five crucial disorders in this research area. Conclusion: This research provides a comprehensive scientometric assessment, enhancing our understanding of NDDSs focused on ferroptosis. Consequently, it enables rapid access to essential information and facilitates the extraction of novel ideas in the field of ferroptotic nanomedicine for both experienced and emerging researchers.

Patterns of Antidepressant and Anxiolytic Use and Spending in 14 European Countries (2012-2021): A Comprehensive Time Series Analysis.

Background: The assessment of antidepressant and anxiolytic consumption and expenditures represents a reliable barometer of the burden of such mental health disorders and the effectiveness of relative healthcare services. Objectives: The current analysis aims to evaluate trajectories of consumption and expenditures of antidepressant and anxiolytic drugs to define patterns of usage and spending across 14 European countries between 2012 and 2021. Methods: A retrospective longitudinal study was performed based on pooled time series secondary data analysis over 2012/2021. Defined Daily Doses (DDD) per 1000 inhabitants and health expenditure per capita were analysed. Linear and quadratic trends were computed to determine relationships between the variables of interest. Results: Only 2 patterns of consumption/expenditure of antidepressants can be identified: consumption and expenditure both grow; consumption grows, and spending decreases. Consumption and expenditures registered 2 main patterns, decreasing in most European countries and increasing only in 2 cases. Conclusion: Prevailing patterns of consumption and spending show an increase in antidepressants and a decrease in anxiolytics. The variation in consumption of such drugs during this timeframe is attributable to several reasons, such as the epidemiological characteristics of mental disease, for instance, the prevalence and incidence of disorders, the accessibility of drugs and alternative treatments, like psychotherapy, different clinical practices and national guidelines. However, such analyses deserve attention for targeted policies and strategies for promoting mental health.

Secular trends of physical fitness in Austrian children attending sports schools: An analysis of repeated cross-sections from 2006 to 2023.

BACKGROUND: Physical fitness (PF) offers numerous physical and mental health benefits, especially during childhood. Previous studies investigating trends in children's PF over the years reported contradictory findings. OBJECTIVE: To identify and analyse secular trends in PF among Austrian schoolchildren from 2006 to 2023. METHOD: A repeated cross-sections design was used to examine the PF of children enrolling in sports schools between 2006 and 2023. During this period, a standardized eight-item motor performance testing battery was administered yearly to capture markers of strength, speed, endurance, agility and reaction time in Austrian schools. RESULTS: A total of n = 3827 children (996 girls) with a mean age of 9.9 ±â€¯1.0 years were included. Linear mixed models indicated significant declines in sprint performance (5, 10, 20 m), tapping, jump (long jump and drop jump), throwing (medicine ball), and agility (snake run). No changes were observed in cardiorespiratory fitness (8 min run) or reaction time. CONCLUSION: There has been a steady decline in PF among Austrian children attending sports schools. This finding underscores the need for enhanced PF monitoring and training in schools to improve public health outcomes.

Historical trends in teacher personality from human language.

Understanding the historical perception and value of teacher personalities reveals key educational priorities and societal expectations. This study analyzes the evolution of teachers' ascribed Big Five personality traits from 1800 to 2019, drawing on millions of English-language books. Word frequency analysis reveals that conscientiousness is the most frequently discussed trait, followed by agreeableness, openness, extraversion, and neuroticism. This pattern underscores society's focus on whether teachers are responsible. Polarity analysis further indicates a higher prevalence of low neuroticism descriptors (e.g., patient and tolerant) in descriptions of teachers compared to the general population, reinforcing the perception of teachers as stable and dependable. The frequent use of terms like "moral", "enthusiastic", and "practical" in describing teachers highlights the positive portrayal of their personalities. However, since the mid-20th century, there has been a notable rise in negative descriptors related to openness (e.g., traditional and conventional), coupled with a decline in positive openness terms. This shift suggests an evolving view of teachers as less receptive to new ideas. These findings offer valuable insights into the historical portrayal and societal values attributed to teacher personalities.

Productivity experienced a more rapid enhancement trend than greenness across the Tibetan Plateau.

Satellite-derived products and field measurements verify that Tibetan Plateau (TP) has been experiencing continuous vegetation greening and productivity increase; however, it remains unclear how this greening translates into productivity and how long-term productivity variations depend on greenness across the TP. Moreover, ignoring the accuracy evaluation of satellite-derived greenness and productivity products may mislead the understanding of TP vegetation changes. Thus, we initially assessed the accuracy of three widely used leaf area index (LAI, proxy of greenness) products (i.e., MODIS, GLASS and GEOV2 LAI) and three gross primary productivity (GPP, proxy of productivity) products (i.e., MODIS, GLASS and PML-V2 GPP) to selected robust products to represent greenness and productivity respectively. Then, we explored the dependence of spatiotemporal GPP dynamics on greenness variations during 2000-2020. Results indicated that PML-V2 GPP and MODIS LAI were more robust and reliable than other satellite-derived products when compared to the reference values. They revealed a prevailing increase in GPP over the past two decades, with a regional average of 71 % higher than that of LAI. Notably, the area proportion of significant productivity enhancement was 31.6 % higher than that of significant greening. About 24.7 % of the TP displayed significantly inconsistent trends. The dependency of GPP on LAI gradually decreased with the increasing water availability, the complexity of vegetation structures, and dense canopy community. By calculating leaf photosynthetic capacity, we found that this indicator greatly regulated the velocity discrepancy between GPP and LAI, and the contribution of only greening to productivity is limited, only occupying 11.9 % of the TP, which was helpful in understanding the inter-annual changes of vegetation dynamics under varying environment conditions. We therefore reveal an unexpected rapid increase in productivity than greening during 2000-2020 on the TP, as well as highlight the caution of only using satellite-derived greenness indicators for assessing long-term changes in vegetation productivity dynamics, especially over mesic ecosystems with complex vegetation structures and dense canopies of TP.

SMGformer: integrating STL and multi-head self-attention in deep learning model for multi-step runoff forecasting.

Accurate runoff forecasting is of great significance for water resource allocation flood control and disaster reduction. However, due to the inherent strong randomness of runoff sequences, this task faces significant challenges. To address this challenge, this study proposes a new SMGformer runoff forecast model. The model integrates Seasonal and Trend decomposition using Loess (STL), Informer's Encoder layer, Bidirectional Gated Recurrent Unit (BiGRU), and Multi-head self-attention (MHSA). Firstly, in response to the nonlinear and non-stationary characteristics of the runoff sequence, the STL decomposition is used to extract the runoff sequence's trend, period, and residual terms, and a multi-feature set based on 'sequence-sequence' is constructed as the input of the model, providing a foundation for subsequent models to capture the evolution of runoff. The key features of the input set are then captured using the Informer's Encoder layer. Next, the BiGRU layer is used to learn the temporal information of these features. To further optimize the output of the BiGRU layer, the MHSA mechanism is introduced to emphasize the impact of important information. Finally, accurate runoff forecasting is achieved by transforming the output of the MHSA layer through the Fully connected layer. To verify the effectiveness of the proposed model, monthly runoff data from two hydrological stations in China are selected, and eight models are constructed to compare the performance of the proposed model. The results show that compared with the Informer model, the 1th step MAE of the SMGformer model decreases by 42.2% and 36.6%, respectively; RMSE decreases by 37.9% and 43.6% respectively; NSE increases from 0.936 to 0.975 and from 0.487 to 0.837, respectively. In addition, the KGE of the SMGformer model at the 3th step are 0.960 and 0.805, both of which can maintain above 0.8. Therefore, the model can accurately capture key information in the monthly runoff sequence and extend the effective forecast period of the model.

Spatio-temporal evolution of social-ecological system resilience in ethnic tourism destinations in mountainous areas and trend prediction: a case study in Wuling, China.

Mountainous ethnic tourism lands are important social-ecological system types. With tourism as the main disturbance factor, the theory of social-ecological system resilience provides a new way to realize the sustainable development of ethno-tourism in mountainous areas. This study divides the social-ecological system into social, economic, and ecological subsystems. It constructs an evaluation index system to assess the resilience of ethnic tourism destinations in mountainous areas, considering vulnerability and adaptability. We investigate 64 counties in the Wuling Mountain area and use set-pair analysis to assess the resilience index of the social-ecological system from 2000 to 2020 and reveal the temporal and spatial characteristics. Obstacle degree models and a genetic algorithm-back propagation neural network are utilized to determine the influencing factors and predict future development trends. The following results were obtained: (1) Temporally, the resilience index shows a steady upward trend, reaching a moderate level. The resilience of the social subsystem fluctuates and rises; the economic subsystem exhibits slow, fast, and slow growth rates with occasional abrupt changes; and the ecological subsystem demonstrates a stable, slightly increasing trend. (2) Spatially, the resilience index is high at the edges and low in the central area, exhibiting a concave distribution. Most counties have moderate or higher resilience. The social and ecological subsystems have low resilience in the south and high resilience in the north. The resilience of the economic subsystem is high at the edges and low in the central area. (3) On the distribution of major obstacle factors, the first two are similar at the county level, and the last three are significantly different. The similarity of the barrier factors is related to the degree of regional proximity of the county, and overall, the similarity is decreasing from north to south and from west to east in the distribution pattern within the area. and to a certain extent, it is affected by terrain and geomorphology. (4) The spatial distribution of the resilience index is similar in 2025 and 2030. The index decreases slightly and then increases annually, with a lower growth rate in the south than in the north. Lower values occur in the northern and southwestern parts, whereas higher values are observed around high-value areas. The region as a whole will develop in a coordinated and integrated manner in the future.

Trends and socio-demographic determinants of suicide-related mortality in Türkiye: An epidemiological study from 2009 to 2022.

BACKGROUND: Suicide is a leading cause of death, particularly among the young age group, in Türkiye, a country with a high population of young people. AIM: The aim of this study is determine the suicide related mortality trend and some associated socio-demographic factors in Türkiye between 2009 and 2022. METHODS: The study includes an epidemiological trend on suicide. The Turkish Statistical Institute's data portal was used. The study population is 46,691 people (Male/female ratios = 74.6%/ 25.4%). Changes in mortality trends were evaluated using the Joinpoint Regression method with Annual Percentage Change (APC) and Average Percentage Change (AAPC) analysis. RESULTS: Over the past 14 years in Türkiye, there has been a statistically significant increase in the standardized mortality rate among males (2009-2022 APC = .76, p = .012) and a statistically non-significant decrease among females (2009-2022 APC = -1.62, p = .083). According to the Nomenclature of Territorial Units for Statistics (NUTS-1) classification, the highest increase is observed in the Central Anatolia region followed by the Western Black Sea region (APC = 3.0 and 2.7, respectively; p < .005). While the percentage of primary school and below has decreasing trends (2009-2022 APC = -7.11, p < .005), there is a significant increase in the percentage of university graduates (2009-2016 APC = 10.87, p < .001). Regarding the marital status, there is a significant decrease in the percentage of those who are married (2009-2013 APC = -3.36, p < .001; 2013-2016 APC = 2.42, p = .037; 2016-2022 APC = -2.31, p = .003) and a significant increase in those who have never been married (2009-2013 APC = 2.63, p < .001; 2013-2016 APC = -1.36, p = .002). CONCLUSION: Although suicide related mortality is below the global average, there has been an increasing trend in turkey in the last 14 years. Analytical and intervention studies on the socio-demographic determinants supporting the increasing trend are needed.

The Diabetes Technology Society Error Grid and Trend Accuracy Matrix for Glucose Monitors.

INTRODUCTION: An error grid compares measured versus reference glucose concentrations to assign clinical risk values to observed errors. Widely used error grids for blood glucose monitors (BGMs) have limited value because they do not also reflect clinical accuracy of continuous glucose monitors (CGMs). METHODS: Diabetes Technology Society (DTS) convened 89 international experts in glucose monitoring to (1) smooth the borders of the Surveillance Error Grid (SEG) zones and create a user-friendly tool-the DTS Error Grid; (2) define five risk zones of clinical point accuracy (A-E) to be identical for BGMs and CGMs; (3) determine a relationship between DTS Error Grid percent in Zone A and mean absolute relative difference (MARD) from analyzing 22 BGM and nine CGM accuracy studies; and (4) create trend risk categories (1-5) for CGM trend accuracy. RESULTS: The DTS Error Grid for point accuracy contains five risk zones (A-E) with straight-line borders that can be applied to both BGM and CGM accuracy data. In a data set combining point accuracy data from 18 BGMs, 2.6% of total data pairs equally moved from Zones A to B and vice versa (SEG compared with DTS Error Grid). For every 1% increase in percent data in Zone A, the MARD decreased by approximately 0.33%. We also created a DTS Trend Accuracy Matrix with five trend risk categories (1-5) for CGM-reported trend indicators compared with reference trends calculated from reference glucose. CONCLUSION: The DTS Error Grid combines contemporary clinician input regarding clinical point accuracy for BGMs and CGMs. The DTS Trend Accuracy Matrix assesses accuracy of CGM trend indicators.

Response of maiden ewes to the 'ram effect' is a robust management practice and a candidate selection trait for enhanced reproductive performance in drylands.

Management of reproduction that relies on naturally based solutions is extremely important to counter the negative perception around hormone-based interventions. In Mediterranean latitudes and wider regions of non-tropical drylands, sheep do not normally ovulate during spring but exposure to a ram can induce oestrus and ovulation. This study assesses the response of maiden Barbarine ewes in drylands to the 'ram effect' during springtime and estimates the genetic parameters of this response. The study uses a database documenting, for 24 consecutive years, the response of nulliparous 18-month-old ewes when mated after stimulation by the 'ram effect.' In addition to the oestrous and fertility database, a pedigree database was also available. Nearly half of the maiden ewes responded to the 'ram effect,' while 24% exhibited spontaneous reproductive activity and displayed oestrus during the first 14 days following the introduction of rams. Nearly 5% of females did not exhibit oestrus, and these animals are proposed for early culling. Average annual values of Normalised Difference Vegetation Index (NDVI), reflecting vegetation cover, and the percent of maiden ewes spontaneously cycling were positively correlated (P = 0.006). Interestingly, NDVI was negatively correlated with the percentage of anoestrus females that did not respond to the 'ram effect' but exhibited oestrus beyond the hypothetical time frame commonly used to describe it. Average fertility was 82.8% and was significantly affected by mating year, live weight at mating, and the response to the 'ram effect.' The highest fertility (88.39%) was for females spontaneously cycling at the time of ram introduction, and the lowest (83.35%) was for females coming into oestrus beyond the time frame for a ram-induced oestrus and ovulation. Heritability from a univariate logit-transformed analysis for fertility was 0.10 and the genetic correlation between fertility and the interval between ram introduction and oestrus was 0.26, suggesting that a shorter interval is associated with higher fertility. Thus, the interval between ram introduction and oestrus is a good candidate for selective breeding for high fertility of maiden ewes in drylands mated out-of-season using the 'ram effect.'

Diagnostic value of nocturnal trend changes in a dynamic electrocardiogram for coronary heart disease.

OBJECTIVE: To explore the diagnostic value of intermittent changes in the nocturnal ST segment trend graph in a dynamic electrocardiogram (ECG) for coronary heart disease (CHD). METHODS: A total of 205 patients who underwent coronary angiography were included in this retrospective study. The study sample was determined through a power analysis aimed at achieving power of 80% with a significance level of 0.05. The participants were divided into the CHD (n = 101) and the non-CHD (n = 104) group, based on the degree of coronary artery diameter stenosis. The morphological changes in the ST segment trend graph were observed and divided into two categories: 'wall-shaped' and 'peak-shaped' changes. RESULTS: Among the 205 patients, 94 had nocturnal ST segment dynamic changes and 111 did not. The detection rate of CHD without nocturnal ST segment dynamic changes was 21.59%, significantly lower than the detection rate of 93.18% in those with nocturnal ST segment changes, reflecting a statistically significant difference (P < 0.05). The positive rate of ST segment in patients with single-vessel disease (71.88%) was lower than in patients with multi-vessel disease (78.57%), and both differences were statistically significant (P < 0.05). The duration of ST segment trend graph changes in 94 cases in the CHD group with intermittent changes in the nocturnal ST segment trend graph was higher than in the non-CHD group, but no significant difference was observed (P > 0.05). The detection rate of CHD in the peak-shaped dynamic change group of the nocturnal ST segment trend graph was significantly higher (76/82) than in the wall-shaped (6/82) dynamic change group (P < 0.05). CONCLUSION: Peak-shaped changes in the nocturnal ST segment trend graph indicate coronary artery lesions. Nocturnal ST segment changes observed through dynamic ECG monitoring can serve as a valuable non-invasive predictor for CHD, providing a feasible method for early diagnosis and intervention in clinical practice.

Analysis of the spatio-temporal variability and convergence of green development efficiency of city clusters in the middle reaches of the Yangtze River in China.

The advantageous geographical location of the middle reaches of the Yangtze River (YRMR) plays an important role in promoting high-quality development in China and accelerating the development of the Yangtze River Economic Belt(YREB). In the new stage of development, green development efficiency (GDE) is an important metric for assessing the balance between economic development and ecological civilization. Therefore, taking three city clusters in China's YRMR as the study area, this research employs the Super-SBM model to assess their GDE in 2010-2020 in order to analyze the problem of GDE at the city scale. On this basis, spatio-temporal analyses, dynamic evolution, and convergence analyses have been carried out using the Thiel index, GML index model, and convergence model. The findings indicate that overall GDE has not yet reached an effective level and generally exhibits a two-stage fluctuating upward trend. Spatially, the pattern of distribution is "high in the southwest, low in the northeast". The differences within the city clusters in the YRMR in terms of green development are gradually narrowing, with differences within the same regions contributing the most to overall differences. The GML index shows a trend of fluctuating growth, indicating that the overall GDE growth is in good condition. GDE has grown significantly as a result of technological advancements, with the Wuhan metropolitan area (cluster A) experiencing the fastest growth in this regard, followed by the Chang-Zhu-Tan and the Poyang Lake rim city clusters (clusters B and C). The YRMR and its three major city clusters exhibit both σ- and ß-convergence patterns. The convergence of GDE is simultaneously affected by several factors, including economic development (RGDP), urbanization level (UR), industrial structure (IND), technological development (TEC), and the level of openness (LO). Regional heterogeneity also exists. The above study provides decision-making references and empirical evidence for promoting green development in China's city clusters in the YRMR and advancing the improvement of economic-environmental incongruence in this area.

Trend Analysis of Lung Cancer Incidence and Mortality in Xiamen (2011-2020).

Objective: To analyze the trends of lung cancer incidence and mortality in Xiamen from 2011 to 2020 and provide some clues for the lung cancer prevention and control. Methods: The data was obtained from the Xiamen City Cancer Register in Fujian Province, China. The data was updated on Sep 30, 2023. The codes of C33-C34 were used to identify the lung cancer. The newly diagnosed lung cancer patients during the period of 2011-2020 in Xiamen City were included in the evaluation of incidence and mortality and Cox analysis. Results: A total of 11408 lung cancer patients were enrolled. The crude incidence rate was 52.78 per 100000 and the age-standardized incidence rate (ASIWR) was 40.67 per 100000 from 2011 to 2020. Both the crude incidence rate of lung cancer (AAPC =5.92, P value <0.001) and ASIWR (AAPC = 4.93, P value <0.001) showed increasing trends. The crude incidence rate in female increased 4.90 times faster as that in male (AAPC: 12.34/2.52). The crude mortality rate and the age-standardized mortality rate (ASMWR) were 37.25 per 100000 and 28.30 per100000. The 5-year age-standardized relative survival rate (ARS) was 18.62% (95% CI: 17.63-19.67%). The 5-year ARS was higher in women than men (26.35% vs 15.28%) and higher in urban than rural areas (21.44% vs 11.96%). Patients with lower education levels had significantly lower ARS than those with higher education (14.66% vs 31.53%). The 5-year ARS improved notably from 2016-2020 compared to 2011-2015 (22.23% vs 13.21%). Farmers had the lowest ARS among occupations [13.34% (95% CI:11.93-14.92%)]. There were all increasing trends in 1-year, 3-year, 5-year, and 10-year ARS rates between 2011 and 2020 (all P values of AAPC<0.05). Conclusion: Lung cancer incidence in Xiamen increased, while mortality decreased with improved survival. Developing more perfect need to consider the differences in the social environment and other factors.

Analysis of the structure and trend prediction of China's total health expenditure.

Background: In the context of rapid economic and social development, there has been a continuous intensification of population aging, transformation of disease patterns, and wide application of new medical technologies. As a result, health expenditures in various countries have sharply soared. How to utilize limited medical resources to maximize the improvement of health levels has become a hot and challenging issue related to the well-being of all humanity. The relevant indicators of total health expenditure play a crucial role in monitoring and evaluating the fairness of health financing and health security in the region. Objective: This study explores the changes in the main expenses that constitute China's total health expenditure and uses indicators related to health expenditure to observe the changes and future development trends of China's health expenditure. Based on this, the utilization of China's health expenditure is monitored to identify possible problems, and thereby targeted suggestions for promoting the development of China's health and wellness cause are put forward. Methods: Based on the comparison of previous literature, this paper analyzes the changes and future development trends in China's health expenditure by using the relevant indicators of China's health expenditure through the structural variation analysis method and the gray prediction model. Results: The results show that the scale of government, social, and out-of-pocket health expenditures has continuously expanded, with social health expenditures becoming the main funding source for total health expenditures. The burden of medical expenditures on individuals has been further reduced. In the institutional method of total health expenditures, hospital expenditures account for about 60% of the total and are the main component. The expenditures of health administration and medical insurance management institutions are the main driving force behind the growth of total health expenditures. However, the proportion of health expenditures in China's GDP is relatively low, so more investment is needed in the healthcare sector, and the burden of individual medical expenses also needs to be continuously reduced. Discussion: In the future, China should further increase its investment in the medical and health sector. Specifically, the government should persist in investing in fundamental medical and health services. Simultaneously, efforts should be made to establish a scientific cost control mechanism for pharmaceuticals and broaden financing channels for healthcare, such as accelerating the development of commercial health insurance.