[Health Assessment of the Stream Ecosystem in Beijing, China].

With economic development, the health of river ecosystems is becoming severely threatened because of the increasing effects of human activities on river ecosystems. Here, 101 sites along regional river systems in Beijing rivers were investigated from autumn 2020 to summer 2021. A total of 34 metrics, including aquatic organisms, hydrology, water quality, and habitat, were calculated to be the candidate indicators. Principal component and correlation analyses were used to select the core metrics from the candidate indicators, and the weight of each core metric was estimated using the entropy method. The integrated index of stream ecological health was constructed to assess the health condition of the Beijing rivers. The results of the PCA and correlation analyses revealed that eleven metrics were selected as the core metrics to construct the integrated index of stream ecological health, including water temperature, flow velocity, BOD5, NH4+-N, Cu, the density of phytoplankton and zooplankton, the Shannon-Wiener diversity index of macroinvertebrates and fish, the BMWP index, and the qualitative habitat evaluation index. According to the health assessment results, 4.95% of the sampling sites were healthy, 23.76% were subhealthy, and 71.29% were in a fair or below healthy state. The river health status showed strong spatial heterogeneity, and the river health statuses in the northern and western regions were relatively good, whereas the river health status in the central and southeastern regions were relatively poor. The results of four aspects stream ecosystem assessment showed that the overall water quality of the rivers was "subhealthy" and the aquatic organisms and habitat were "general poor," but the hydrology was "poor." The evaluation results of five water systems demonstrated that the Chaobai River had the best health status, followed by that of the Yongding River, Daqing River, and Jiyun River, and the Beiyun River had the worst health status. Maintaining river ecological baseflow, ensuring river system connectivity, and improving and restoring the river habitat environment are the key aspects of river ecological restoration and protection in Beijing in the future.

MRI-Based Radiomics Models Developed With Features of the Whole Liver and Right Liver Lobe: Assessment of Hepatic Inflammatory Activity in Chronic Hepatic Disease.

BACKGROUND: The noninvasive assessment of hepatic inflammatory activity (HIA) is crucial for making clinical decisions and monitoring therapeutic efficacy in chronic liver disease (CLD). PURPOSE: To develop MRI-based radiomics models by extracting features from the whole liver and localized regions of the right liver lobe, compare the efficiency of two radiomics models, and further develop a radiomics nomogram for the assessment of HIA in CLD. STUDY TYPE: Retrospective. POPULATION: In all, 137 consecutive patients. FIELD STRENGTH/SEQUENCE: 1.5T/T2 -weighted imaging. ASSESSMENT: All patients (nonsignificant HIA, n = 98; significant HIA, n = 39) were randomly divided into a training (n = 95) and a test cohort (n = 42). Radiomics features were extracted from the regions covering the whole liver (ROI-w) and localized regions of the right liver lobe (ROI-r). Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analyses were used to select features and develop radiomics models. A combined model fusing the valuable radiomics features with clinical-radiological predictors was developed. Finally, a radiomics nomogram derived from the combined model was developed. STATISTICAL TESTS: Synthetic minority oversampling technique algorithm, LASSO, receiver operator characteristic curve, and calibration curve analysis were performed. RESULTS: The area under the curve (AUC), sensitivity, and specificity of the ROI-w radiomics model in assessing HIA were 0.858, 0.800, and 0.733, respectively. The ROI-r model were 0.844, 0.733, and 0.867, respectively. No differences were detected between the two radiomics models (P = 0.8329). The combined model fusing valuable ROI-w radiomics features, albumin, and periportal edema exhibited a promising performance (AUC, 0.911). The calibration curves showed good agreement between the actual observations and nomogram predictions. DATA CONCLUSION: The MRI-based radiomics models had a powerful ability to evaluate HIA and the ROI-w radiomics model was comparable to the ROI-r model. Moreover, the radiomics nomogram could be a favorable method to individually estimate HIA in CLD. J. MAGN. RESON. IMAGING 2020;52:1668-1678.

HIV screening among patients seeking care at Xuanwu Hospital: A cross-sectional study in Beijing, China, 2011-2016.

OBJECTIVES: One-third of people living with HIV in China are still unaware of their status, so we sought to better understand HIV testing in the general hospital setting in China. METHODS: A cross-sectional study was conducted using the electronic medical records of all patients who attended Xuanwu Hospital in Beijing, January 1, 2011 to December 31, 2016. HIV screening and detection rates and characteristics of patients diagnosed with HIV were assessed. RESULTS: Overall, 235,961 patients were screened, for a screening rate of 1.4%. Although most were outpatients (98.4%), screening rate was higher among inpatients (70.0% versus 0.4%), and highest in internal medicine (36.1%) and surgery (33.3%) departments. A total of 140 patients were diagnosed with HIV, for a detection rate of 5.93 per 10,000. Detection rates were highest among outpatients (9.34 per 10,000), and patients attending the dermatology and sexually transmitted infection (STI) department (153.85 per 10,000). Most diagnoses were made among males (91.4%), aged 20-39 (67.1%), who reported becoming infected through homosexual contact (70.0%). CONCLUSIONS: HIV screening in China's general hospitals needs to be improved. More focus should be placed on screening outpatients, especially in the dermatology and STI department, and young men.

Comparative analysis of water quality and toxicity assessment methods for urban highway runoff.

In this study, comparative analyses of highway runoff samples obtained from seventeen storm events have been conducted between the traditional water quality assessment method and biotoxicity tests, using zebrafish (Danio rerio) embryos and luminous bacteria (Vibrio qinghaiensis. Q67) to provide useful information for ecotoxicity assessment of urban highway runoff. The study results showed that the Nemerow pollution index based on US EPA recommended Criteria Maximum Concentrations (CMC) (as traditional water quality assessment method) had no significant correlation with luminous bacteria acute toxicity test results, while significant correlation has been observed with two indicators of 72 hpf (hours post fertilization) hour hatching rate and 96 hpf abnormality rate from the toxicity test with zebrafish embryos. It is therefore concluded that the level of mixture toxicity of highway runoff could not be adequately measured by the Nemerow assessment method. Moreover, the key pollutants identified from the water quality assessment and from the biotoxicity evaluation were not consistent. For biotoxic effect evaluation of highway runoff, three indexes were found to be sensitive, i.e. 24 hpf lethality and 96 hpf abnormality of zebrafish embryos, as well as the inhibition rate for luminous bacteria Q67. It is therefore recommended that these indexes could be incorporated into the traditional Nemerow method to provide a more reasonable evaluation of the highway runoff quality and ecotoxicity.

A GIS-based spatial correlation analysis for ambient air pollution and AECOPD hospitalizations in Jinan, China.

BACKGROUND: Acute exacerbations of COPD (AECOPD) are important events during disease procedure. AECOPD have negative effect on patients' quality of life, symptoms and lung function, and result in high socioeconomic costs. Though previous studies have demonstrated the significant association between outdoor air pollution and AECOPD hospitalizations, little is known about the spatial relationship utilized a spatial analyzing technique- Geographical Information System (GIS). OBJECTIVE: Using GIS to investigate the spatial association between ambient air pollution and AECOPD hospitalizations in Jinan City, 2009. METHODS: 414 AECOPD hospitalization cases in Jinan, 2009 were enrolled in our analysis. Monthly concentrations of five monitored air pollutants (NO2, SO2, PM10, O3, CO) during January 2009-December 2009 were provided by Environmental Protection Agency of Shandong Province. Each individual was geocoded in ArcGIS10.0 software. The spatial distribution of five pollutants and the temporal-spatial specific air pollutants exposure level for each individual was estimated by ordinary Kriging model. Spatial autocorrelation (Global Moran's I) was employed to explore the spatial association between ambient air pollutants and AECOPD hospitalizations. A generalized linear model (GLM) using a Poisson distribution with log-link function was used to construct a core model. RESULTS: At residence, concentrations of SO2, PM10, NO2, CO, O3 and AECOPD hospitalization cases showed statistical significant spatially clustered. The Z-score of SO2, PM10, CO, O3, NO2 at residence is 15.88, 13.93, 12.60, 4.02, 2.44 respectively, while at workplace, concentrations of PM10, SO2, O3, CO and AECOPD hospitalization cases showed statistical significant spatially clustered. The Z-score of PM10, SO2, O3, CO at workplace is 11.39, 8.07, 6.10, and 5.08 respectively. After adjusting for potential confounders in the model, only the PM10 concentrations at workplace showed statistical significance, with a 10 µg/m(3) increase of PM10 at workplace associated with a 7% (95%CI: [3.3%, 10%]) increase of hospitalizations due to AECOPD. CONCLUSIONS: Ambient air pollution is correlated with AECOPD hospitalizations spatially. A 10 µg/m(3) increase of PM10 at workplace was associated with a 7% (95%CI: [3.3%, 10%]) increase of hospitalizations due to AECOPD in Jinan, 2009. As a spatial data processing tool, GIS has novel and great potential on air pollutants exposure assessment and spatial analysis in AECOPD research.