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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]

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]

Background: Minimal access breast surgery (MABS) is commonly employed in the management of breast cancer, but there is limited research on the postoperative immune function associated with MABS. Objective: This study aimed to assess the postoperative immune function in breast patients who underwent MABS or conventional open breast surgery (COBS). Methods: We retrospectively analyzed the medical records of 829 breast cancer patients treated with either MABS or COBS at a single hospital between January 2020 and June 2023. Among them, 116 matched pairs were obtained through 1:1 propensity score matching (PSM). Flow cytometry was used to measure the percentages of CD3+, CD4+, and CD8+ cells, as well as the CD4+/CD8+ ratio, on three different time points: preoperative day 1 (PreD1), postoperative day 1 (PostD1), and postoperative day 7 (PostD7). Results: Both the MABS and COBS groups demonstrated a significant reduction in the percentages of CD3+, CD4+, and CD8+ cells, along with the CD4+/CD8+ ratio, from PreD1 to PostD1. Interestingly, the MABS group showed a reversal of these parameters, returning to preoperative levels by PostD7. Conversely, the COBS group showed an increase in these parameters from PostD1 to PostD7, but they still remained significantly lower than preoperative levels at PostD7. Conclusion: MABS treatment may result in reduced postoperative immune suppression and faster recovery of preoperative immune function compared to COBS in patients. [ABSTRACT FROM AUTHOR]

To examine the influence of Body Mass Index (BMI) on laparoscopic gastrectomy (LG) short-term and long-term outcomes for gastric cancer. A retrospective analysis was conducted on gastric cancer patients undergoing LG at the Third Hospital of Nanchang City from January 2013 to January 2022. Based on WHO BMI standards, patients were categorized into normal weight, overweight, and obese groups. Factors such as operative time, intraoperative blood loss, postoperative complications, and overall survival were assessed. Across different BMI groups, it was found that an increase in BMI was associated with longer operative times (average times: 206.22 min for normal weight, 231.32 min for overweight, and 246.78 min for obese), with no significant differences noted in intraoperative blood loss, postoperative complications, or long-term survival among the groups. The impact of BMI on long-term survival following LG for gastric cancer was found to be insignificant, with no notable differences in survival outcome between different BMI groups. Although higher BMI is associated with increased operative time in LG for gastric cancer, it does not significantly affect intraoperative blood loss, postoperative complications, recovery, or long-term survival. LG is a feasible treatment choice for obese patients with gastric cancer. [ABSTRACT FROM AUTHOR]

Background: Although the elderly constitute more than a third of hepatocellular carcinoma (HCC) patients, they have not been adequately represented in treatment and prognosis studies. Thus, there is not enough evidence to guide the treatment of such patients. The objective of this study is to identify the prognostic factors of older patients with HCC and to construct a new prognostic model for predicting their overall survival (OS). Methods: 2,721 HCC patients aged ≥ 65 were extracted from the public database-Surveillance, Epidemiology, and End Results (SEER) and randomly divided into a training set and an internal validation set with a ratio of 7:3. 101 patients diagnosed from 2008 to 2017 in the First Affiliated Hospital of Zhejiang University School of Medicine were identified as the external validation set. Univariate cox regression analyses and multivariate cox regression analyses were adopted to identify these independent prognostic factors. A predictive nomogram-based risk stratification model was proposed and evaluated using area under the receiver operating characteristic curve (AUC), calibration curves, and a decision curve analysis (DCA). Results: These attributes including age, sex, marital status, T stage, N stage, surgery, chemotherapy, tumor size, alpha-fetoprotein level, fibrosis score, bone metastasis, lung metastasis, and grade were the independent prognostic factors for older patients with HCC while predicting survival duration. We found that the nomogram provided a good assessment of OS at 1, 3, and 5 years in older patients with HCC (1-year OS: (training set: AUC = 0.823 (95%CI 0.803–0.845); internal validation set: AUC = 0.847 (95%CI 0.818–0.876); external validation set: AUC = 0.732 (95%CI 0.521–0.943)); 3-year OS: (training set: AUC = 0.813 (95%CI 0.790–0.837); internal validation set: AUC = 0.844 (95%CI 0.812–0.876); external validation set: AUC = 0.780 (95%CI 0.674–0.887)); 5-year OS: (training set: AUC = 0.839 (95%CI 0.806–0.872); internal validation set: AUC = 0.800 (95%CI 0.751–0.849); external validation set: AUC = 0.821 (95%CI 0.727–0.914)). The calibration curves showed that the nomogram was with strong calibration. The DCA indicated that the nomogram can be used as an effective tool in clinical practice. The risk stratification of all subgroups was statistically significant (p < 0.05). In the stratification analysis of surgery, larger resection (LR) achieved a better survival curve than local destruction (LD), but a worse one than segmental resection (SR) and liver transplantation (LT) (p < 0.0001). With the consideration of the friendship to clinicians, we further developed an online interface (OHCCPredictor) for such a predictive function (https://juntaotan.shinyapps.io/dynnomapp%5fhcc/). With such an easily obtained online tool, clinicians will be provided helpful assistance in formulating personalized therapy to assess the prognosis of older patients with HCC. Conclusions: Age, sex, marital status, T stage, N stage, surgery, chemotherapy, tumor size, AFP level, fibrosis score, bone metastasis, lung metastasis, and grade were independent prognostic factors for elderly patients with HCC. The constructed nomogram model based on the above factors could accurately predict the prognosis of such patients. Besides, the developed online web interface of the predictive model provide easily obtained access for clinicians. [ABSTRACT FROM AUTHOR]

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: This study aimed to construct a risk prediction model to estimate the odds of osteoporosis (OP) in elderly patients with type 2 diabetes mellitus (T2DM) and evaluate its prediction efficiency. Methods: This study included 21,070 elderly patients with T2DM who were hospitalized at six tertiary hospitals in Southwest China between 2012 and 2022. Univariate logistic regression analysis was used to screen for potential influencing factors of OP and least absolute shrinkage. Further, selection operator regression (LASSO) and multivariate logistic regression analyses were performed to select variables for developing a novel predictive model. The area under the receiver operating characteristic curve (AUROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the performance and clinical utility of the model. Results: The incidence of OP in elderly patients with T2DM was 7.01% (1,476/21,070). Age, sex, hypertension, coronary heart disease, cerebral infarction, hyperlipidemia, and surgical history were the influencing factors. The seven-variable model displayed an AUROC of 0.713 (95% confidence interval [CI]:0.697–0.730) in the training set, 0.716 (95% CI: 0.691–0.740) in the internal validation set, and 0.694 (95% CI: 0.653–0.735) in the external validation set. The optimal decision probability cut-off value was 0.075. The calibration curve (bootstrap = 1,000) showed good calibration. In addition, the DCA and CIC demonstrated good clinical practicality. An operating interface on a webpage (https://juntaotan.shinyapps.io/osteoporosis/) was developed to provide convenient access for users. Conclusions: This study constructed a highly accurate model to predict OP in elderly patients with T2DM. This model incorporates demographic characteristics and clinical risk factors and may be easily used to facilitate individualized prediction. [ABSTRACT FROM AUTHOR]

Objectives: To appraise effective predictors for infection in patients with decompensated cirrhosis (DC) by using XGBoost algorithm in a retrospective case-control study. Methods: Clinical data were retrospectively collected from 6,648 patients with DC admitted to five tertiary hospitals. Indicators with significant differences were determined by univariate analysis and least absolute contraction and selection operator (LASSO) regression. Further multi-tree extreme gradient boosting (XGBoost) machine learning-based model was used to rank importance of features selected from LASSO and subsequently constructed infection risk prediction model with simple-tree XGBoost model. Finally, the simple-tree XGBoost model is compared with the traditional logical regression (LR) model. Performances of models were evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Results: Six features, including total bilirubin, blood sodium, albumin, prothrombin activity, white blood cell count, and neutrophils to lymphocytes ratio were selected as predictors for infection in patients with DC. Simple-tree XGBoost model conducted by these features can predict infection risk accurately with an AUROC of 0.971, sensitivity of 0.915, and specificity of 0.900 in training set. The performance of simple-tree XGBoost model is better than that of traditional LR model in training set, internal verification set, and external feature set (P < 0.001). Conclusions: The simple-tree XGBoost predictive model developed based on a minimal amount of clinical data available to DC patients with restricted medical resources could help primary healthcare practitioners promptly identify potential infection. [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]

Background: Length of stay (LOS) is an important metric for evaluating the management of inpatients. This study aimed to explore the factors impacting the LOS of inpatients with type-2 diabetes mellitus (T2DM) and develop a predictive model for the early identification of inpatients with prolonged LOS. Methods: A 13-year multicenter retrospective study was conducted on 83,776 patients with T2DM to develop and validate a clinical predictive tool for prolonged LOS. Least absolute shrinkage and selection operator regression model and multivariable logistic regression analysis were adopted to build the risk model for prolonged LOS, and a nomogram was taken to visualize the model. Furthermore, receiver operating characteristic curves, calibration curves, and decision curve analysis and clinical impact curves were used to respectively validate the discrimination, calibration, and clinical applicability of the model. Results: The result showed that age, cerebral infarction, antihypertensive drug use, antiplatelet and anticoagulant use, past surgical history, past medical history, smoking, drinking, and neutrophil percentage-to-albumin ratio were closely related to the prolonged LOS. Area under the curve values of the nomogram in the training, internal validation, external validation set 1, and external validation set 2 were 0.803 (95% CI [confidence interval] 0.799–0.808), 0.794 (95% CI 0.788–0.800), 0.754 (95% CI 0.739–0.770), and 0.743 (95% CI 0.722–0.763), respectively. The calibration curves indicated that the nomogram had a strong calibration. Besides, decision curve analysis, and clinical impact curves exhibited that the nomogram had favorable clinical practical value. Besides, an online interface (https://cytjt007.shinyapps.io/prolonged%5flos/) was developed to provide convenient access for users. Conclusion: In sum, the proposed model could predict the possible prolonged LOS of inpatients with T2DM and help the clinicians to improve efficiency in bed management. [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]

This study analyzed the spatial-temporal heterogeneity of green development efficiency and its influencing factors in the growing Xuzhou Metropolitan Area for the period 2000-2015. The slacks-based measure (SBM) model, spatial autocorrelation, and the geographically weighted regression (GWR) model were used to conduct the analysis. The conclusions were as follows: first, the overall efficiency of green development of the Xuzhou Metropolitan Area decreased, the regional differences and spatial agglomeration shrunk and differences within the region were the main contributors to the regional differences of green development efficiency. Second, the counties with high-efficiency green development were distributed along the coast, and along the routes of the Beijing-Shanghai and the Eastern Longhai railways. A developing axis of the high-efficiency counties was the main feature of the spatial pattern for green development efficiency. Third, regarding spatial correlation and green development efficiency, the High-High type counties in the Xuzhou Metropolitan Area formed a centralized distribution corridor along the inter-provincial border areas of Henan and Jiangsu, whereas the Low-Low type counties were concentrated in the external, marginal parts of the metropolitan area. Fourth, the major factors (ranked in decreasing order of impact) influencing green development efficiency were innovation, government regulations, the economic development level, energy consumption, and industrial structure. These factors exerted their influence to varying extents; the influence of the same factor had different effects in different regions and obvious spatial differences were observed for the different 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]

This paper develops a conceptual model and an indicator system for measuring economic resilience of resource-based cities based on the theory of evolutionary resilience and the related concepts of persistence, adaptation, and transformation. Nineteen resource- based cities in Northeast China were analyzed using the indicator system. The results showed that Liaoning and Jilin provinces had higher economic resilience than Heilongjiang Province. Panjin, Benxi, and Anshan in Liaoning Province were the top three cities, while Shuangyashan and other coal-based cities in Heilongjiang Province ranked last. Metals- and petroleum-based cities had significantly higher resilience than coal-based cities. The differences in persistence, adaptability, transformation, and resilience among resource-based cities decreased since the introduction of the Northeast Revitalization Strategy in 2003. Forestry-based cities improved the most in terms of resilience, followed by metals-based and multiple-resource cities; however, resilience dropped for coal-based cities, and petroleum-based cities falling the most. The findings illustrate the importance and the way to develop a differentiated approach to improve resilience among resource-based cities. [ABSTRACT FROM AUTHOR]

Background: Whether body mass index (BMI) is a significant risk factor for recurrence of primary spontaneous pneumothorax (PSP) remains controversial. The purpose of this study was to examine whether BMI and other factors are linked to risk of PSP recurrence. Methods: A consecutive cohort of 273 patients was retrospectively evaluated. Patients were divided into those who experienced recurrence ( n = 81) and those who did not ( n = 192), as well as into those who had low BMI ( n = 75) and those who had normal or elevated BMI ( n = 198). The two pairs of groups were compared in terms of baseline data, and Cox proportional hazards modeling was used to identify predictors of PSP recurrence. Results: Rates of recurrence among all 273 patients were 20.9% at 1 year, 23.8% at 2 years, and 28.7% at 5 years. Univariate analysis identified the following significant predictors of PSP recurrence: height, weight, BMI, size of pneumothorax, and treatment modality. Multivariate analyses identified several risk factors for PSP recurrence: low BMI, pneumothorax size ≥50%, and non-surgical treatment. Kaplan-Meier survival analysis indicated that patients with low BMI showed significantly lower recurrence-free survival than patients with normal or elevated BMI ( P < 0.001). Conclusions: Low BMI, pneumothorax size ≥50%, and non-surgical treatment were risk factors for PSP recurrence in our cohort. Low BMI may be a clinically useful predictor of PSP recurrence. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to develop a simple, effective method to label hepatoma cells with aptamers and then detect them using fluorescent silica nanoparticles (FSNPs). Method: Streptavidin was conjugated to carboxyl-modified fluorescein isothiocyanate (FITC)-doped silica nanoparticles which were prepared by the reverse microemulsion method. The resulting streptavidin-conjugated fluorescent silica nanoparticles (SA-FSNPs) were mixed with hepatoma cells that had been labeled with biotin-conjugated aptamer TLS11a (Bio-TLS11a). The specificity and sensitivity of the nanoprobes were assessed using flow cytometry and fluorescence microscopy. Their toxicity was assessed in normal human liver cell cultures using the MTT assay, as well as in nude mice using immunohistochemistry. Results: SA-FSNPs showed uniform size and shape, and fluorescence properties of them was similar to the free FITC dye. SA-FSNPs were able to detect aptamer-labeled hepatoma cells with excellent specificity and good sensitivity, and they emitted strong, photobleach-resistant fluorescent signal. SA-FSNPs showed no significant toxic effects in vitro or in vivo. Conclusion: The combination of biotin-conjugated aptamers and SA-FSNPs shows promise for sensitive detection of hepatoma cells, and potentially of other tumor cell types as well. [ABSTRACT FROM AUTHOR]

A simple, highly sensitive method to detect leukemia cells has been developed based on aptamer-modified fluorescent silica nanoparticles (FSNPs). In this strategy, the amine-labeled Sgc8 aptamer was conjugated to carboxyl-modified FSNPs via amide coupling between amino and carboxyl groups. Sensitivity and specificity of Sgc8-FSNPs were assessed using flow cytometry and fluorescence microscopy. These results showed that Sgc8-FSNPs detected leukemia cells with high sensitivity and specificity. Aptamer-modified FSNPs hold promise for sensitive and specific detection of leukemia cells. Changing the aptamer may allow the FSNPs to detect other types of cancer cells. [ABSTRACT FROM AUTHOR]

Sustainable urbanization is not only an important research topic in the field of urbanization, but also the development direction of new-type urbanization. In this paper, we construct an index system to evaluate sustainable urbanization potential with the entropy method. Results show that potential values of sustainable urbanization in most cities are not high. Cities with higher sustainable urbanization potential values are mainly located in the central part of Northeast China. Environmental potential of sustainable urbanization is the main contributor to sustainable urbanization potential in Northeast China. There is no absolute relationship between city size and potential value, large city does not always mean greater potential. Correlation analysis shows that urbanization rate cannot reflect the sustainable urbanization potential of a region. Population urbanization is not the ultimate goal of sustainable urbanization. Unilateral pursue urbanization rate cannot improve the potential of sustainable urbanization. Towards sustainable urbanization, governments in Northeast China should revitalize local economy, pay more attention to the rural areas and develop low-carbon economy or ecological economy. Finally, this paper highlights the importance of choosing more integrated methodology or new models for measuring sustainable urbanization potential in view of the shortcomings of one method. [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]