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[Objective] In the context of synergistic pollution and carbon reduction, revealing the coupling relationship between industrial growth and carbon emissions based on different growth rates is crucial for promoting low-carbon development in interprovincial boundary regions. [Methods] This study focused on the Huaihai Economic Zone and used an industrial growth-carbon emission decoupling model to conduct the research. [Results] (1) During the study period, the decoupling relationship between industrial growth and carbon emissions had evolved from weak decoupling to strong negative decoupling. Counties in a decoupled state tended to be concentrated around central cities such as Xuzhou. The coupling coordination had continuously improved, with the proportion of highly coordinated and extremely coordinated counties increasing from 18.64% to 83.05%. (2) The industrial growth-carbon emission asynchronous growth index exhibited a pattern of rise-fall-rise-fall, consistently in the phase of weak expansion of carbon emissions. In terms of spatial distribution, the relative growth rate of most counties’ industries exceeded the relative growth rate of carbon emissions, and over 40% of the counties showed a downward trend. (3) Factors such as technological innovation, environmental regulation, and carbon sequestration capacity had a positive and strengthening effect. The impact of factors such as industrialization level, the proportion of resource-based industries, and interprovincial boundaries on the asynchronous growth relationship changed from positive promotion to negative repression. Industrial transformation and the evolution of interprovincial boundaries were the main driving forces. (4) The mismatch between the market dominance of industrial growth and the government-led carbon emission control, as well as the mismatch between the external driving force of industrial development and the internal driving force, have caused spatiotemporal heterogeneity in the asynchronous growth of industrial growth and carbon emissions. This has also spurred the strategic demand for regional coordinated development, which is interconnected with and mutually influenced by the asynchronous growth. [Conclusion] Industrial low carbonization is the main path to promote the coordinated development of industrial growth and carbon emission, while the spatiotemporal coordinated evolution and development of industrial growth and carbon emission is conducive to boosting the low-carbon and green development of industry.

Semiconductors with visible light catalytic characteristics can realize the degradation of pollutants, CO2 reduction, and hydrogen preparation in sunlight. They have huge application value in the fields of environmental repair and green energy. Graphite phase nitride (g-C3N4, CN) is widely used in various fields such as photocatalytic degradation of pollutants due to its suitable gap width, easy preparation, low cost, fast visible light response, and rich surface activity sites. However, the absorption rate of ordinary CN on visible light is low, and the carriers are easy to recombination, making the lower optical catalytic activity. Therefore, in order to improve the photocatalytic characteristics of the CN, it is necessary to make the surface modification. This article first introduces several main methods for the current surface modification of CN, including size regulation, catalyst embedding, defect introduction, heterostructure construction, etc., and then summarizes the optical catalytic application and related mechanisms of CN. Finally, some challenges and development prospects of CN in preparation and photocatalytic applications are proposed. [ABSTRACT FROM AUTHOR]

Objective To screen the influencing factors of liver cirrhosis with spontaneous bacterial peritonitis (SBP) so as to establish and verify a diagnostic model of cirrhosis with SBP. Methods A total of 7 461 patients with liver cirrhosis were collected from 2 Grade-A hospitals in Chongqing (Hospital A and B) between July 2015 and December 2019. According to the occurrence of SBP during hospitalization, they were divided into SBP (n=1 173) and non-SBP (n=6 288) groups. A total of 3 776 patients (70%) from Hospital A were randomly selected as the training set, while the remaining 1 619 patients (30%) and the 2 066 patients from hospital B were subjected as the internal and external validation sets respectively. Univariate and logistic analyses were used to screen variables, and logistic regression, random forest (RF), decision tree (DT) and XGBoost models were subsequently established using the training set. Then, the optimized logistic regression model was built on the basis of the above 4 models. Finally, the 5 models were applied in the internal and external verification sets for evaluating and comparing the diagnostic value of different models for cirrhosis with SBP. Results The machine learning algorithm suggested that the 7 common influencing factors with significance in all models were as follows: decompensation stage (OR=5.354, 95%CI: 3.770-7.803), lymphocyte percentage (OR=0.951, 95%CI: 0.939-0.962), total bilirubin (OR=1.003, 95%CI: 1.002-1.004), abnormal C-reactive protein (OR=1.626, 95%CI: 1.310-2.017), international normalized ratio (OR=1.346, 95%CI: 1.091-1.681), prealbumin (OR=0.990, 95%CI: 0.987-0.993) and model for end stage liver disease (MELD) score (OR=1.038, 95%CI: 1.015-1.063). The AUC of the internal verification of the optimized logistic model was 0.860, with a sensitivity of 0.872 and a specificity of 0.719; while the corresponding values of the external verification were 0.818, 0.662 and 0.812, respectively. Delong test showed that there was no statistical difference in AUC between the optimized logistic regression model and the other models with favorable performance. Conclusion The risk prediction models established by machine learning algorithm for liver cirrhosis complicated with SBP have high diagnostic value. Among them, the optimized logistic regression model has performed well in both internal and external verification, and is able to provide reference for clinical diagnosis of SBP.

Purpose: Hospitalized hypertensive patients rely on blood pressure medication, yet there is limited research on the sole use of amlodipine, despite its proven efficacy in protecting target organs and reducing mortality. This study aims to identify key indicators influencing the efficacy of amlodipine, thereby enhancing treatment outcomes. Patients and Methods: In this multicenter retrospective study, 870 hospitalized patients with primary hypertension exclusively received amlodipine for the first 5 days after admission, and their medical records contained comprehensive blood pressure records. They were categorized into success (n=479) and failure (n=391) groups based on average blood pressure control efficacy. Predictive models were constructed using six machine learning algorithms. Evaluation metrics encompassed the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). SHapley Additive exPlanations (SHAP) analysis assessed feature contributions to efficacy. Results: All six machine learning models demonstrated superior predictive performance. Following variable reduction, the model predicting amlodipine efficacy was reconstructed using these algorithms, with the light gradient boosting machine (LightGBM) model achieving the highest overall performance (AUC = 0.803). Notably, amlodipine showed enhanced efficacy in patients with low platelet distribution width (PDW) values, as well as high hematocrit (HCT) and thrombin time (TT) values. Conclusion: This study utilized machine learning to predict amlodipine's effectiveness in hypertension treatment, pinpointing key factors: HCT, PDW, and TT levels. Lower PDW, along with higher HCT and TT, correlated with enhanced treatment outcomes. This facilitates personalized treatment, particularly for hospitalized hypertensive patients undergoing amlodipine monotherapy. [ABSTRACT FROM AUTHOR]

Objective To investigate the influencing factors of suspected allergic reactions caused by Xueshuantong Injection, and to provide reference for clinical rational drug use. Methods From June 2015 to February 2019, the medical records of the inpatients with the use of Xueshuantong Injection from 7 hospitals were collected. And 78 patients with suspected allergic reaction to the injection were obtained by prescription sequence analysis. Another 312 subjects sampled at 1:4 ratio of gender, age and inpatient department using propensity score matching were assigned into the control group. Conditional logistic regression was used to analyze the effects of solvent, allergy history, single dose and combination on suspected allergic reaction. Results There were no significant differences in the solvent, allergy history and single dose between the 2 groups (P>0.05). But statistical difference was seen in the combination of some drugs (P < 0.05), such as omeprazole (OR=3.28, P=0.007 6), pantoprazole (OR=3.103, P=0.002 8), mannitol (OR=4.017, P=0.000 4), tranexamic acid (OR=2.895, P=0.016 1), lidocaine (OR=4.401, P=0.001 3) and ambroxol (OR=3.607, P=0.003 6). Combination of the above drugs may increase the risk of allergic reactions. Conclusion The combination of Xueshuantong Injection with omeprazole, pantoprazole, mannitol, tranexamic acid, lidocaine and ambroxol may increase the risk of allergic reactions in the patients

Background and Aims: Influenza is one of the most widespread respiratory infections and poses a huge burden on health care worldwide. Vaccination is key to preventing and controlling influenza. Influenza vaccine hesitancy is an important reason for the low vaccination rate. In 2019, Vaccine hesitancy was identified as one of the top 10 threats to global health by the World Health Organization. However, there remains a glaring scarcity of bibliometric research in that regard. This study sought to identify research hotspots and future development trends on influenza vaccine hesitation and provide a new perspective and reference for future research. Methods: We retrieved publications on global influenza vaccine hesitancy from the Web of Science Core Collection database, Scopus, and PubMed databases from inception to 2022. This study used VOSviewer and CiteSpace for visualization analysis. Results: Influenza vaccine hesitancy‐related publications increased rapidly from 2012 and peaked in 2022. One hundred and nine countries contributed to influenza vaccine hesitation research, and the United States ranked first with 541 articles and 7161 citations. Vaccines‐Basel was the journal with the largest number of published studies on influenza vaccine hesitations. MacDonald was the most frequently cited author. The most popular research topics on influenza vaccine hesitancy were (1) determinants of influenza vaccination in specific populations, such as healthcare workers, children, pregnant women, and so on; (2) influenza and COVID‐19 vaccine hesitancy during the COVID‐19 pandemic. Conclusions: The trend in the number of annual publications related to influenza vaccine hesitancy indicating the COVID‐19 pandemic will prompt researchers to increase their attention to influenza vaccine hesitancy. With healthcare workers as the key, reducing vaccine hesitancy and improving vaccine acceptance in high‐risk groups will be the research direction in the next few years. [ABSTRACT FROM AUTHOR]

Background: Influenza‐related encephalopathy is a rapidly progressive encephalopathy that usually presents during the early phase of influenza infection and primarily manifests as central nervous system dysfunction. This study aimed to analyze the current research status and hotspots of influenza‐related encephalopathy since 2000 through bibliometrics analysis. Methods: The Web of Science Core Collection (WOSCC) was used to extract global papers on influenza‐related encephalopathy from 2000 to 2022. Meanwhile, the VOSviewer and CiteSpace software were used for data processing and result visualization. Results: A total of 561 published articles were included in the study. Japan was the country that published the most articles, with 205 articles, followed by the United States and China. Okayama University and Tokyo Medical University published the most articles, followed by Nagoya University, Tokyo University, and Juntendo University. Based on the analysis of keywords, four clusters with different research directions were identified: "Prevalence of H1N1 virus and the occurrence of neurological complications in different age groups," "mechanism of brain and central nervous system response after influenza virus infection," "various acute encephalopathy" and "diagnostic indicators of influenza‐related encephalopathy." Conclusions: The research progress, hotspots, and frontiers on influenza‐related encephalopathy after 2000 were described through the visualization of bibliometrics. The findings will lay the groundwork for future studies and provide a reference for influenza‐related encephalopathy. Research on influenza‐related encephalopathy is basically at a stable stage, and the number of research results is related to outbreaks of the influenza virus. [ABSTRACT FROM AUTHOR]

Environmental pollutants cause harm to animals, humans, and ecosystems worldwide, and powerful approaches are urgently needed to address this challenge. Dye degradation photocatalysis is one of the most promising methods for the chemical breakdown of organic pollutants. The large specific surface area produced by nanotechnology significantly improves photocatalytic performance. In the wave of full-fledged research on photocatalytic nanoparticles, there is a higher demand for unique means of characterization, such as real-time growth diagnosis and precise phase modulation of the particles. The advantages of laser spectroscopy are its non-contact methodology, rapid response, and accurate directionality, and therefore, spectral characterization methods are becoming more suitable for the real-time diagnosis of photocatalysts. In this review, traditional and spectroscopic means of characterizing dye degradation photocatalytic nanoparticles were examined. In addition, laser spectroscopic characterization can be performed using PL, LCRS, LSPR, SERS, UTAS, and LIBS, which were introduced in this review. Notably, the regulation applicability of LSPR and SERS with noble metal-based nanoparticles and the real-time flexibility of UTAS and LIBS are advantages during the use of laser spectroscopy to examine nanocomposites of photocatalysis. Moreover, the future development of laser spectroscopy for photocatalytic nanoparticle characterization was discussed. [ABSTRACT FROM AUTHOR]

A graph is a widely used and effective data structure in many applications; it describes the relationships among nodes or entities. Currently, most semi-supervised or unsupervised graph neural network models are trained based on a very basic operation called negative sampling. Usually, the purpose of the learning objective is to maximize the similarity between neighboring nodes while minimizing the similarity between nodes that are not close to each other. Negative sampling can reduce the time complexity by sampling a small fraction of the negative nodes instead of using all of the negative nodes when optimizing the objective. However, sampling of the negative nodes may fail to deliver stable model performance due to the uncertainty in the sampling procedure. To avoid such disadvantages, we provide an efficient Non-Sampling Graph Neural Network (NS-GNN) framework. The main idea is to use all the negative samples when optimizing the learning objective to avoid the sampling process. Of course, directly using all of the negative samples may cause a large increase in the model training time. To mitigate this problem, we rearrange the origin loss function into a linear form and take advantage of meticulous mathematical derivation to reduce the complexity of the loss function. Experiments on benchmark datasets show that our framework can provide better efficiency at the same level of prediction accuracy compared with existing negative sampling-based models. [ABSTRACT FROM AUTHOR]

The connectivity of complex networks is usually determined by a small fraction of key nodes. Earlier works successfully identify an influential single node, yet have some problems for the case of multiple ones. In this paper, based on the matrix spectral theory, we propose the collective influence of multiple nodes. An interesting finding is that some traditionally influential nodes have strong internal coupling interactions that reduce their collective influence. We then propose a greedy algorithm to dismantle complex networks by optimizing the collective influence of multiple nodes. Experimental results show that our proposed method outperforms the state of the art methods in terms of the principal eigenvalue and the giant component of the remaining networks. [ABSTRACT FROM AUTHOR]

Under the trend of rapid urbanization, the urban heat island (UHI) effect has become a hot issue for scholars to study. In order to better alleviate UHI effect, it is important to understand the effect of landuse/landcover (LULC) and landscape patterns on the urban thermal environment from perspective of landscape ecology. This research aims to quantitatively investigate the effect of LULC landscape patterns on UHI effects more accurately based on a landscape metrics analysis. In addition, we also explore the complex relationship between land surface temperature (LST) and vegetation cover. Taking Zhengzhou City of China as a case study, an integrated method which includes the geographic information system (GIS), remote-sensing (RS) technology, and landscape metrics was employed to facilitate the analysis. Landsat data (2000–2014) were applied to investigate the spatiotemporal evolution patterns of LST and LULC. The results indicated that the mean LST value increased by 2.32°C between 2000 and 2014. The rise of LST was consistent with the trend of rapid urbanization in Zhengzhou City, which resulted in sharp increases in impervious surfaces (IS) and substantial losses of vegetation cover. Furthermore, the investigation of LST and vegetation cover demonstrated that fractional vegetation cover (FVC) had a stronger negative effect on LST than normalized differential vegetation index (NDVI). In addition, LST was obviously correlated with LULC landscape patterns, and both landscape composition and spatial configuration affected UHI effects to varying degrees. This study not only illustrates a feasible way to investigate the relationship between LULC and urban thermal environment but also suggests some important measures to improve urban planning to reduce UHI effects for sustainable development. [ABSTRACT FROM AUTHOR]

The notion of resilience to analyse how fast systems recover from shocks has been increasingly taken up in economic geography, in which there is a burgeoning literature on regional resilience. Regional resilience is a place‐sensitive, multi‐layered and multi‐scalar, conflict‐ridden and highly contingent process. The nature of shocks is one important impact factor on regional resilience. Arguably, so far, most literature on regional resilience has dealt with the financial crisis in 2008/2009. In this research note, we will analyse both the particular characteristics of the current COVID‐19 crisis, as well as its effects on regional recovery and potential resilience in China, where it started. We conclude that a complex combination of the characteristics of the current COVID‐19 crisis, the institutional experience of dealing with previous pandemic and epidemic crises, government support schemes, as well as regional industrial structures, might potentially affect the recovery and resilience rates of Chinese regions. [ABSTRACT FROM AUTHOR]

Resource‐based cities (RBCs) whose economies depend primarily on exploiting and processing natural resources usually have rigid, singular, and low‐end industrial structures, which often cripples their ability to cope with external disturbances such as international resource price fluctuations and economic downturns. This paper quantitatively analyzes the economic resilience of RBCs in China in terms of resistance and recoverability during the Asian financial crisis and the global financial crisis. Furthermore, it identifies the main factors affecting resilience. There are four main findings: First, RBCs were quickly and negatively impacted by the Asian financial crisis, which suggests that economic resistance was generally low during this period. In the recovery period, while the rate of recovery was slow at the beginning, economic recoverability improved after 2002. Economic resistance and recoverability were found to have a strong negative correlation. Second, at the beginning of the global financial crisis, the economic resistance of RBCs was generally high. However, after 2012, the number of cities that were severely affected by the economic crisis increased rapidly. Third, economic resistance varied across different types of RBCs. Coal‐based and forestry‐based cities had lower economic resistance, while oil & gas‐based cities were more resistant. RBCs in the Eastern region generally had low economic resistance, while the economic resilience of recessionary cities was also low. Finally, while factors affecting the economic resilience varied across the two economic cycles, we found that economic development, labor conditions and, most of all, the industrial structure had a statistically significant negative effect on economic resilience. [ABSTRACT FROM AUTHOR]

Economic polarization is a special manifestation of economic disparity which intensifies the gap between the rich and the poor in a region and brings about a series of social problems. Though more and more scholars are studying the phenomenon of economic polarization, there are few studies on polarization level division and early warning analysis in the existing literature. The main purpose of this paper is to propose a standard for rationally dividing the level of economic polarization. This paper firstly analyzes the current situation of economic polarization by using the economic data of 54 counties and cities in Jiangsu Province from 2000 to 2016 and secondly predicts the economic polarization level of Jiangsu Province from 2017 to 2015 through the grey model. We find that, according to the classification criteria of polarization levels, the phenomenon of economic polarization in Jiangsu Province is both not as serious as expected and at a moderate level of alertness. The results of this study can provide important reference value for the coordinated development of Jiangsu Province. [ABSTRACT FROM AUTHOR]

Understanding the spatial distribution of land surface temperature (LST) and its impact factors is crucial for mitigating urban heat island effect. However, few studies have quantitatively investigated the spatial non-stationarity and spatial scale effects of the relationships between LST and its impact factors at multi-scales. The main purposes of this study are as follows: (1) to estimate the spatial distributions of urban heat island (UHI) intensity by using hot spots analysis and (2) to explore the spatial non-stationarity and scale effects of the relationships between LST and related impact factors at multiple resolutions (30–1200 m) and to find appropriate scales for illuminating the relationships in a plain city. Based on the LST retrieved from Landsat 8 OLI/TIRS images, the Geographically-Weighted Regression (GWR) model is used to explore the scale effects of the relationships in Zhengzhou City between LST and six driving indicators: The Fractional Vegetation Cover (FVC), the Impervious Surface (IS), the Population Density (PD), the Fossil-fuel CO2 Emission data (FFCOE), the Shannon Diversity Index (SHDI) and the Perimeter-area Fractal Dimension (PAFRAC),which indicate the vegetation abundance, built-up, social-ecological variables and the diversity and shape complexity of land cover types. Our findings showed that the spatial patterns of LST show statistically significant hot spot zones in the center of the study area, partly extending to the western and southern industrial areas, indicating that the intensity of the urban heat island is significantly spatial clustering in Zhengzhou City. In addition, compared with the Ordinary Least Squares (OLS) model, the GWR model has a better ability to characterize spatial non-stationarity and analyze the relationships between the LST and its impact factors by considering the space-varying relationships of different variables, especially at the fine spatial scales (30–480 m). However, the strength of GWR model has become relatively weak with the increase of spatial scales (720–1200 m). This reveals that the GWR model is recommended to be applied in the analysis of UHI problems and related impact factors at scales finer than 480 m in the plain city. If the spatial scale is coarser than 720 m, both OLS and GWR models are suitable for illustrating the correct relationships between UHI effect and its influence factors in the plain city due to their undifferentiated performance. These findings can provide valuable information for urban planners and researchers to select appropriate models and spatial scales seeking to mitigate urban thermal environment effect. [ABSTRACT FROM AUTHOR]

Objective: This study aims to evaluate the association between body mass index (BMI) and the incidence of breast cancer-related lymphedema (BCRL)., Methods: This retrospective cohort study analyzed data from 1464 breast cancer patients treated at The Third Hospital of Nanchang between 2018 and 2021. Patients were categorized based on BMI (<25, 25 to < 30, ≥ 30 kg/m²). Variables such as axillary lymph node dissection, infections, radiotherapy, and comorbidities were taken into account., Results: The incidence of BCRL was 23.4%. Higher BMI was associated with increased risk of BCRL, with significant incidence rates observed at 1, 2, and 3 years in the higher BMI groups. Multivariate analysis confirmed BMI as an independent risk factor for BCRL., Conclusion: Elevated BMI is associated with increased BCRL risk and decreased BCRL-free survival, underscoring the significance of weight management in breast cancer care., (© 2024 Wiley Periodicals LLC.)

Background: This study aimed to identify the predictive factors for prolonged length of stay (LOS) in elderly type 2 diabetes mellitus (T2DM) patients suffering from cerebral infarction (CI) and construct a predictive model to effectively utilize hospital resources., Methods: Clinical data were retrospectively collected from T2DM patients suffering from CI aged ≥65 years who were admitted to five tertiary hospitals in Southwest China. The least absolute shrinkage and selection operator (LASSO) regression model and multivariable logistic regression analysis were conducted to identify the independent predictors of prolonged LOS. A nomogram was constructed to visualize the model. The discrimination, calibration, and clinical practicality of the model were evaluated according to the area under the receiver operating characteristic curve (AUROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC)., Results: A total of 13,361 patients were included, comprising 6,023, 2,582, and 4,756 patients in the training, internal validation, and external validation sets, respectively. The results revealed that the ACCI score, OP, PI, analgesics use, antibiotics use, psychotropic drug use, insurance type, and ALB were independent predictors for prolonged LOS. The eight-predictor LASSO logistic regression displayed high prediction ability, with an AUROC of 0.725 (95% confidence interval [CI]: 0.710-0.739), a sensitivity of 0.662 (95% CI: 0.639-0.686), and a specificity of 0.675 (95% CI: 0.661-0.689). The calibration curve (bootstraps = 1,000) showed good calibration. In addition, the DCA and CIC also indicated good clinical practicality. An operation interface on a web page (https://xxmyyz.shinyapps.io/prolonged&#95;los1/) was also established to facilitate clinical use., Conclusion: The developed model can predict the risk of prolonged LOS in elderly T2DM patients diagnosed with CI, enabling clinicians to optimize bed management., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Tang, Zhao, Xiao, Jiang, Tan, Xu, Pan and Wang.)

Background: Since severe fever with thrombocytopenia syndrome virus (SFTSV) was first reported in 2009, a large number of relevant studies have been published. However, no bibliometrics analysis has been conducted on the literature focusing on SFTSV. This study aims to evaluate the research hotspots and future development trends of SFTSV research through bibliometric analysis, and to provide a new perspective and reference for future SFTSV research and the prevention of SFTSV., Methods: We retrieved global publications on SFTSV from the Web of Science Core Collection (WoSCC) and Scopus databases from inception of the database until 2022 using VOSviewer software and CiteSpace was used for bibliometric analysis., Results: The number of SFTSV-related publications has increased rapidly since 2011, peaking in 2021. A total of 45 countries/regions have published relevant publications, with China topping the list with 359. The Viruses-Basel has published the most papers on SFTSV. In addition, Yu et al. have made the greatest contribution to SFTSV research, with their published paper being the most frequently cited. The most popular SFTSV study topics included: (1) pathogenesis and symptoms, (2) characteristics of the virus and infected patients, and (3) transmission mechanism and risk factors for SFTSV., Conclusions: In this study, we provide a detailed description of the research developments in SFTSV since its discovery and summarize the SFTSV research trends. SFTSV research is in a phase of explosive development, and a large number of publications have been published in the past decade. There is a lack of collaboration between countries and institutions, and international collaboration and exchanges should be strengthened in the future. The current research hotpots of SFTSV is antiviral therapy, immunotherapy, virus transmission mechanism and immune response., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zhang, Tan, Jin, Qian, Wang, Zhou, Yuan and Wu.)

Background: Comprehensive eye examinations for diabetic retinopathy is poorly implemented in medically underserved areas. There is a critical need for a widely available and economical tool to aid patient selection for priority retinal screening. We investigated the possibility of a predictive model for retinopathy identification using simple parameters., Methods: Clinical data were retrospectively collected from 4, 159 patients with diabetes admitted to five tertiary hospitals. Independent predictors were identified by univariate analysis and least absolute shrinkage and selection operator (LASSO) regression, and a nomogram was developed based on a multivariate logistic regression model. The validity and clinical practicality of this nomogram were assessed using concordance index (C-index), area under the receiver operating characteristic curve (AUROC), calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC)., Results: The predictive factors in the multivariate model included the duration of diabetes, history of hypertension, and cardiovascular disease. The three-variable model displayed medium prediction ability with an AUROC of 0.722 (95%CI 0.696-0.748) in the training set, 0.715 (95%CI 0.670-0.754) in the internal set, and 0.703 (95%CI 0.552-0.853) in the external dataset. DCA showed that the threshold probability of DR in diabetic patients was 17-55% according to the nomogram, and CIC also showed that the nomogram could be applied clinically if the risk threshold exceeded 30%. An operation interface on a webpage (https://cqmuxss.shinyapps.io/dr&#95;tjj/) was built to improve the clinical utility of the nomogram., Conclusions: The predictive model developed based on a minimal amount of clinical data available to diabetic patients with restricted medical resources could help primary healthcare practitioners promptly identify potential retinopathy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Yang, Tan, He, Huang, Wang, Gong, Liu, Zhang and Xu.)

Background: Globally, blood pressure management strategies were ineffective, and a low percentage of patients receiving hypertension treatment had their blood pressure controlled. In this study, we aimed to build a medication prediction model by correlating patient attributes with medications to help physicians quickly and rationally match appropriate medications., Methods: We collected clinical data from elderly hypertensive patients during hospitalization and combined statistical methods and machine learning (ML) algorithms to filter out typical indicators. We constructed five ML models to evaluate all datasets using 5-fold cross-validation. Include random forest (RF), support vector machine (SVM), light gradient boosting machine (LightGBM), artificial neural network (ANN), and naive Bayes (NB) models. And the performance of the models was evaluated using the micro-F1 score., Results: Our experiments showed that by statistical methods and ML algorithms for feature selection, we finally selected Age, SBP, DBP, Lymph, RBC, HCT, MCHC, PLT, AST, TBIL, Cr, UA, Urea, K, Na, Ga, TP, GLU, TC, TG, γ-GT, Gender, HTN CAD, and RI as feature metrics of the models. LightGBM had the best prediction performance with the micro-F1 of 78.45%, which was higher than the other four models., Conclusion: LightGBM model has good results in predicting antihypertensive medication regimens, and the model can be beneficial in improving the personalization of hypertension treatment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wang, Yan, Xiang, Tan, Yang and Zhao.)

Background: Depression is associated with an increased risk of death in patients with coronary heart disease (CHD). This study aimed to explore the factors influencing depression in elderly patients with CHD and to construct a prediction model for early identification of depression in this patient population., Materials and Methods: We used propensity-score matching to identify 1,065 CHD patients aged ≥65 years from four hospitals in Chongqing between January 2015 and December 2021. The patients were divided into a training set ( n = 880) and an external validation set ( n = 185). Univariate logistic regression, multivariate logistic regression, and least absolute shrinkage and selection operator regression were used to determine the factors influencing depression. A nomogram based on the multivariate logistic regression model was constructed using the selected influencing factors. The discrimination, calibration, and clinical utility of the nomogram were assessed by the area under the curve (AUC) of the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA) and clinical impact curve (CIC), respectively., Results: The predictive factors in the multivariate model included the lymphocyte percentage and the blood urea nitrogen and low-density lipoprotein cholesterol levels. The AUC values of the nomogram in the training and external validation sets were 0.762 (95% CI = 0.722-0.803) and 0.679 (95% CI = 0.572-0.786), respectively. The calibration curves indicated that the nomogram had strong calibration. DCA and CIC indicated that the nomogram can be used as an effective tool in clinical practice. For the convenience of clinicians, we used the nomogram to develop a web-based calculator tool (https://cytjt007.shinyapps.io/dynnomapp&#95;depression/)., Conclusion: Reductions in the lymphocyte percentage and blood urea nitrogen and low-density lipoprotein cholesterol levels were reliable predictors of depression in elderly patients with CHD. The nomogram that we developed can help clinicians assess the risk of depression in elderly patients with CHD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Tan, Xu, He, Zhang, Xiang, Xu, Xu, Gong, Tan and Tan.)

Study Objectives: There is no consensus information on infections associated with nonbenzodiazepines. Knowledge about infections related to newly marketed hypnotics (orexin receptor antagonists and melatonin receptor agonists) is scarce. The study aimed to detect infection signals for nonbenzodiazepines, orexin receptor antagonists, and melatonin receptor agonists by analyzing data from the U.S. Food & Drug Administration adverse event reporting system., Methods: A disproportionality analysis was performed to quantitatively detect infection signals for hypnotics by calculating the reporting odds ratio and the 95% confidence interval. Data registered in the U.S. Food & Drug Administration adverse event reporting system from 2010-2020 were retrieved., Results: A total of 3,092 patients with infection were extracted for the 3 classes of hypnotic drugs. Nonbenzodiazepines were associated with a higher disproportionality of infections (reporting odds ratio: 1.10; 95% confidence interval, 1.06-1.14). The association of infections was not present for melatonin receptor agonists (reporting odds ratio: 0.86; 95% confidence interval, 0.74-1.00) and orexin receptor antagonists (reporting odds ratio: 0.19; 95% confidence interval, 0.15-0.25). Significant reporting associations were identified for nonbenzodiazepines concerning the categories of bone and joint infections, dental and oral soft tissue infections, upper respiratory tract infections, and urinary tract infections., Conclusions: Nonbenzodiazepines had a positive signal for infections, while orexin receptor antagonists and melatonin receptor agonists had a negative signal. More research needs to be conducted to confirm this relationship., Citation: Meng L, Huang J, He Q, et al. Hypnotics and infections: disproportionality analysis of the U.S. Food & Drug Administration adverse event reporting system database. J Clin Sleep Med. 2022;18(9):2229-2235., (© 2022 American Academy of Sleep Medicine.)

Background: The International Normalized Ratio (INR) is significantly associated with Hepatic Encephalopathy (HE) in patients with liver cirrhosis. However, the dose-response relationship between continuous INR changes and HE risk has not been clearly defined. Thus, our goal was to explore the continuous relationship between HE and INR among patients hospitalized with liver cirrhosis and to evaluate the role of the INR as a risk factor for HE in these patients., Methods: A total of 6,266 people were extracted from the Big Data Platform of the Medical Data Research Institute of Chongqing Medical University. In this study, unconditional logistic regression and restricted cubic spline (RCS) model were used to analyze the dose-response association of INR with HE. Alcoholic liver disease, smoking status, and drinking status were classified for subgroup analysis., Results: The prevalence of HE in the study population was 8.36%. The median INR was 1.4. After adjusting for alcoholic liver disease, age, smoking status, drinking status, total bilirubin, neutrophil percentage, total hemoglobin, aspartate aminotransferase, serum sodium, albumin, lymphocyte percentage, serum creatinine, red blood cell, and white blood cell, multivariate logistic regression analysis revealed that INR ≥ 1.5 (OR = 2.606, 95% CI: 2.072-3.278) was significantly related to HE risk. The RCS model showed a non-linear relationship between the INR and HE (non-linear test, χ 2 = 30.940, P < 0.001), and an increased INR was an independent and adjusted dose-dependent risk factor for HE among patients with liver cirrhosis., Conclusion: This finding could guide clinicians to develop individualized counseling programs and treatments for patients with HE based on the INR risk stratification., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Tan, He, Li, Zhang, Yang, Xu and Xu.)

Background: Heart failure (HF) is an end-stage manifestation of and cause of death in coronary heart disease (CHD). The objective of this study was to establish and validate a non-invasive diagnostic nomogram to identify HF in patients with CHD., Methods: We retrospectively analyzed the clinical data of 44,772 CHD patients from five tertiary hospitals. Univariate logistic regression analyses and least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify independent factors. A nomogram based on the multivariate logistic regression model was constructed using these independent factors. The concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC) were used to evaluate the predictive accuracy and clinical value of this nomogram., Results: The predictive factors in the multivariate model included hypertension, age, and the total bilirubin, uric acid, urea nitrogen, triglyceride, and total cholesterol levels. The area under the curve (AUC) values of the nomogram in the training set, internal validation set, external validation set1, and external validation set2 were 0.720 (95% CI: 0.712-0.727), 0.723 (95% CI: 0.712-0.735), 0.692 (95% CI: 0.674-0.710), and 0.655 (95% CI: 0.634-0.677), respectively. The calibration curves indicated that the nomogram had strong calibration. DCA and CIC indicated that the nomogram can be used as an effective tool in clinical practice., Conclusion: The developed predictive model combines the clinical and laboratory factors of patients with CHD and is useful in individualized prediction of HF probability for clinical decision-making during treatment and management., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Tan, He, Li, Xu, Zhang, Xu, Zhang, Xiang, Tang and Zhao.)

Background: Liver cirrhosis is a major global health and economic challenge, placing a heavy economic burden on patients, families, and society. This study aimed to investigate medical expenditure trends in patients with liver cirrhosis and assess the drivers for such medical expenditure among patients with liver cirrhosis., Methods: Medical expenditure data concerning patients with liver cirrhosis was collected in six tertiary hospitals in Chongqing, China, from 2012 to 2020. Trends in medical expenses over time and trends according to subgroups were described, and medical expenditure compositions were analyzed. A multiple linear regression model was constructed to evaluate the factors influencing medical expenditure. All expenditure data were reported in Chinese Yuan (CNY), based on the 2020 value, and adjusted using the year-specific health care consumer price index for Chongqing., Results: Medical expenditure for 7,095 patients was assessed. The average medical expenditure per patient was 16,177 CNY. An upward trend in medical expenditure was observed in almost all patient subgroups. Drug expenses were the largest contributor to medical expenditure in 2020. A multiple linear regression model showed that insurance type, sex, age at diagnosis, marital status, length of stay, smoking status, drinking status, number of complications, autoimmune liver disease, and the age-adjusted Charlson comorbidity index score were significantly related to medical expenditure., Conclusion: Conservative estimates suggest that the medical expenditure of patients with liver cirrhosis increased significantly from 2012 to 2020. Therefore, it is necessary to formulate targeted measures to reduce the personal burden on patients with liver cirrhosis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Tan, Tang, He, Xu, Qiu, Chen, Zhang and Zhang.)

Background: The objective of this study was to use machine learning algorithms to construct predictive models for atrial fibrillation (AF) in elderly patients with coronary heart disease (CHD) and type 2 diabetes mellitus (T2DM)., Methods: The diagnosis and treatment data of elderly patients with CHD and T2DM, who were treated in four tertiary hospitals in Chongqing, China from 2015 to 2021, were collected. Five machine learning algorithms: logistic regression, logistic regression+least absolute shrinkage and selection operator, classified regression tree (CART), random forest (RF) and extreme gradient lifting (XGBoost) were used to construct the prediction models. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were used as the comparison measures between different models., Results: A total of 3,858 elderly patients with CHD and T2DM were included. In the internal validation cohort, XGBoost had the highest AUC (0.743) and sensitivity (0.833), and RF had the highest specificity (0.753) and accuracy (0.735). In the external verification, RF had the highest AUC (0.726) and sensitivity (0.686), and CART had the highest specificity (0.925) and accuracy (0.841). Total bilirubin, triglycerides and uric acid were the three most important predictors of AF., Conclusion: The risk prediction models of AF in elderly patients with CHD and T2DM based on machine learning algorithms had high diagnostic value. The prediction models constructed by RF and XGBoost were more effective. The results of this study can provide reference for the clinical prevention and treatment of AF., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Xu, Peng, Tan, Zhao, Yang and Tian.)

Background: Spontaneous bacterial peritonitis (SBP) is a common and life-threatening infection in patients with decompensated cirrhosis (DC), and it is accompanied with high mortality and morbidity. However, early diagnosis of spontaneous bacterial peritonitis (SBP) is not possible because of the lack of typical symptoms or the low patient compliance and positivity rate of the ascites puncture test. We aimed to establish and validate a non-invasive diagnostic nomogram to identify SBP in patients with DC., Method: Data were collected from 4,607 patients with DC from July 2015 to December 2019 in two tertiary hospitals in Chongqing, China (A and B). Patients with DC were divided into the SBP group (995 cases) and the non-SBP group (3,612 cases) depending on whether the patients had SBP during hospitalization. About 70% (2,685 cases) of patients in hospital A were randomly selected as the traindata, and the remaining 30% (1,152 cases) were used as the internal validation set. Patients in hospital B (770 cases) were used as the external validation set. The univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to screen variables, and logistic regression was used to determine independent predictors to construct a nomogram to identify patients with SBP. Area under curve (AUC), calibration curve, and dynamic component analysis (DCA) were carried out to determine the effectiveness of the nomogram., Result: The nomogram was composed of seven variables, namely, mean red blood cell hemoglobin concentration (odds ratio [ OR ] = 1.010, 95% CI : 1.004-1.016), prothrombin time ( OR = 1.038, 95% CI : 1.015-1.063), lymphocyte percentage ( OR = 0.955, 95% CI : 0.943-0.967), prealbumin ( OR = 0.990, 95% CI : 0.987-0.993), total bilirubin ( OR = 1.003 95% CI : 1.002-1.004), abnormal C-reactive protein (CRP) level ( OR = 1.395, 95% CI : 1.107-1.755), and abnormal procalcitonin levels ( OR = 1.975 95% CI : 1.522-2.556). Good discrimination of the model was observed in the internal and external validation sets (AUC = 0.800 and 0.745, respectively). The calibration curve result indicated that the nomogram was well-calibrated. The DCA curve of the nomogram presented good clinical application ability., Conclusion: This study identified the independent risk factors of SBP in patients with DC and used them to construct a nomogram, which may provide clinical reference information for the diagnosis of SBP in patients with DC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Xiang, Tan, Tan, Xu, Wen, Wang, Yang and Zhao.)

Background and Aims: Patients with acute decompensated (AD) cirrhosis are frequently readmitted to the hospital. An accurate predictive model for identifying high-risk patients may facilitate the development of effective interventions to reduce readmission rates. Methods: This cohort study of patients with AD cirrhosis was conducted at six tertiary hospitals in China between September 2012 and December 2016 (with 705 patients in the derivation cohort) and between January 2017 and April 2020 (with 251 patients in the temporal validation cohort). Least absolute shrinkage and selection operator Cox regression was used to identify the prognostic factors and construct a nomogram. The discriminative ability, calibration, and clinical net benefit were evaluated based on the C-index, area under the curve, calibration curve, and decision curve analysis. Kaplan-Meier curves were constructed for stratified risk groups, and log-rank tests were used to determine significant differences between the curves. Results: Among 956 patients, readmission rates were 24.58, 42.99, and 51.78%, at 30, 60, and 90 days, respectively. Bacterial infection was the main reason for index hospitalization and readmission. Independent factors in the nomogram included gastrointestinal bleeding [hazard rate (HR): 2.787; 95% confidence interval (CI): 2.221-3.499], serum sodium (HR: 0.955; 95% CI: 0.933-0.978), total bilirubin (HR: 1.004; 95% CI: 1.003-1.005), and international normalized ratio (HR: 1.398; 95% CI: 1.126-1.734). For the convenience of clinicians, we provided a web-based calculator tool (https://cqykdx1111.shinyapps.io/dynnomapp/). The nomogram exhibited good discrimination ability, both in the derivation and validation cohorts. The predicted and observed readmission probabilities were calibrated with reliable agreement. The nomogram demonstrated superior net benefits over other score models. The high-risk group (nomogram score >56.8) was significantly likely to have higher rates of readmission than the low-risk group (nomogram score ≤ 56.8; p < 0.0001). Conclusions: The nomogram is useful for assessing the probability of short-term readmission in patients with AD cirrhosis and to guide clinicians to develop individualized treatments based on risk stratification., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Xu, Tan, Wang, Zhao and Qin.)