Random survival forest for predicting the combined effects of multiple physiological risk factors on all-cause mortality.

Understanding the combined effects of risk factors on all-cause mortality is crucial for implementing effective risk stratification and designing targeted interventions, but such combined effects are understudied. We aim to use survival-tree based machine learning models as more flexible nonparametric techniques to examine the combined effects of multiple physiological risk factors on mortality. More specifically, we (1) study the combined effects between multiple physiological factors and all-cause mortality, (2) identify the five most influential factors and visualize their combined influence on all-cause mortality, and (3) compare the mortality cut-offs with the current clinical thresholds. Data from the 1999-2014 NHANES Survey were linked to National Death Index data with follow-up through 2015 for 17,790 adults. We observed that the five most influential factors affecting mortality are the tobacco smoking biomarker cotinine, glomerular filtration rate (GFR), plasma glucose, sex, and white blood cell count. Specifically, high mortality risk is associated with being male, active smoking, low GFR, elevated plasma glucose levels, and high white blood cell count. The identified mortality-based cutoffs for these factors are mostly consistent with relevant studies and current clinical thresholds. This approach enabled us to identify important cutoffs and provide enhanced risk prediction as an important basis to inform clinical practice and develop new strategies for precision medicine.

Data Science Applications in Circular Economy: Trends, Status, and Future.

The circular economy (CE) aims to decouple the growth of the economy from the consumption of finite resources through strategies, such as eliminating waste, circulating materials in use, and regenerating natural systems. Due to the rapid development of data science (DS), promising progress has been made in the transition toward CE in the past decade. DS offers various methods to achieve accurate predictions, accelerate product sustainable design, prolong asset life, optimize the infrastructure needed to circulate materials, and provide evidence-based insights. Despite the exciting scientific advances in this field, there still lacks a comprehensive review on this topic to summarize past achievements, synthesize knowledge gained, and navigate future research directions. In this paper, we try to summarize how DS accelerated the transition to CE. We conducted a critical review of where and how DS has helped the CE transition with a focus on four areas including (1) characterizing socioeconomic metabolism, (2) reducing unnecessary waste generation by enhancing material efficiency and optimizing product design, (3) extending product lifetime through repair, and (4) facilitating waste reuse and recycling. We also introduced the limitations and challenges in the current applications and discussed opportunities to provide a clear roadmap for future research in this field.

Atmospheric fate and impacts of HFO-1234yf from mobile air conditioners in East Asia.

HFO-1234yf (2,3,3,3-tetrafluoropropene) is being used as refrigerant to replace HFC-134a (1,1,1,2-tetrafluoroethane), a potent greenhouse gas, in mobile air conditioners. However, the environmental impacts of HFO-1234yf, which is quickly and almost completely transformed to the persistent and phytotoxic trifluoroacetic acid (TFA), is of great concern. Here, we used the nested-grid chemical transport model, GEOS-Chem, to assess the fate and environmental impacts of HFO-1234yf emissions from mobile air conditioners in East Asia. With total emissions of 30.3 Gg yr-1, the annual mean concentrations of HFO-1234yf in China, Japan, and South Korea were 4.00, 3.23, and 5.54 pptv (parts per trillion volume), respectively, and the annual deposition fluxes (dry plus wet) of TFA in these regions were 0.35, 0.48, and 0.53 kg km-2 yr-1, dominated by wet deposition. About 14 %, 13 % and 11 % of HFO-1234yf emissions were deposited as TFA in China, Japan and South Korea, respectively, i.e. a large portion of TFA was deposited in areas outside of the emission boundary regions. The TFA characteristics in Japan and South Korea was significantly influenced by emission from China, which contributions ranged from 43 % to 94 % for the TFA concentrations and 44 % to 98 % for the TFA depositions across the four seasons. This suggests that the influence of neighboring emission sources cannot be ignored when assessing the impact of HFO-1234yf emissions in individual countries.

Projections of National-Gridded Emissions of Hydrofluoroolefins (HFOs) in China.

Hydrofluoroolefins (HFOs) are being used as substitutes for potent greenhouse gas hydrofluorocarbons (HFCs). However, the use and environmental impacts of HFOs are of great concern due to the rapid degradation of HFOs to produce persistent and phytotoxic trifluoroacetic acid (TFA). Here, we provide a comprehensive projection of HFO emissions in China during 2024-2060 for the first time. Under the Kigali Amendment to the Montreal Protocol, China's HFO emissions are estimated to increase from 1.7 (1.3-2.3) to 148.8 (111.4-185.4) kt in 2024-2060 with cumulative emissions of 2.8 (2.0-3.5) Gt, and cumulative reduced HFCs emissions are evaluated to be 5.4 Gt CO2-equivalent. High HFO emissions would be distributed mainly in the North China Plain and the eastern and coastal areas. HFO-1234yf (2,3,3,3-tetrafluoropropene) contributes most of HFO emissions with a cumulative emission of 1.7 Gt in 2024-2060, while the cumulative increment of TFA deposition from HFO-1234yf emissions would reach 0.4-1.0 Gt. The long-term national-gridded HFO emission inventories can provide scientific support for evaluating the environmental risks of HFOs and developing HFC phase-out pathways for addressing climate change.

Strong relations of peroxyacetyl nitrate (PAN) formation to alkene and nitrous acid during various episodes.

Peroxyacetyl nitrate (PAN) is one of the critical secondary pollutants in photochemical smog. This study investigated the relationship between PAN and PAN precursors with the Regional Atmospheric Chemical Mechanism version 2 model in six episodes recorded in Zhengzhou. In all episodes, peroxyacetyl radical (PA) was primarily produced by acetaldehyde oxidation, with more than 70% contributions. In photochemical episodes and photochemical-haze co-occurring episodes (combined episodes), methylglyoxal secondarily contributes 8.1%-10.6% to PA while in haze pollution, the propagation of other radicals to PA is the second most important source (12.0%-19.1%). Among anthropogenic non-methane hydrocarbons, alkene restricted PAN formation as first-generation precursors, with the relative incremental reactivity of PAN (RIRPAN) more than 0.6 during three-type episodes. Nitrous acid (HONO) also played important role in PAN formation. Especially during photochemical episodes, RIRPAN(HONO) reached 0.79, which was comparable to the RIRPAN value of alkene. Through sensitivity analysis of the relative formation of PAN to O3 (the amount of PAN generated when 100 ppb O3 formed), HONO was identified as the key precursor of PAN in haze pollution by promoting the oxidation of NMHC, while alkene predominated the relative formation of PAN to O3 in photochemical and combined pollution through producing acetaldehyde. The sensitivity of PAN to HONO is obviously enhanced with higher NOx/VOC ratios during photochemical and combined pollution.

High-Resolution Urban Air Quality Mapping for Multiple Pollutants Based on Dense Monitoring Data and Machine Learning.

Spatially explicit urban air quality information is important for urban fine-management and public life. However, existing air quality measurement methods still have some limitations on spatial coverage and system stability. A micro station is an emerging monitoring system with multiple sensors, which can be deployed to provide dense air quality monitoring data. Here, we proposed a method for urban air quality mapping at high-resolution for multiple pollutants. By using the dense air quality monitoring data from 448 micro stations in Lanzhou city, we developed a decision tree model to infer the distribution of citywide air quality at a 500 m × 500 m × 1 h resolution, with a coefficient of determination (R2) value of 0.740 for PM2.5, 0.754 for CO and 0.716 for SO2. Meanwhile, we also show that the deployment density of the monitoring stations can have a significant impact on the air quality inference results. Our method is able to show both short-term and long-term distribution of multiple important pollutants in the city, which demonstrates the potential and feasibility of dense monitoring data combined with advanced data science methods to support urban atmospheric environment fine-management, policy making, and public health studies.

Using Deep Learning to Fill Data Gaps in Environmental Footprint Accounting.

Environmental footprint accounting relies on economic input-output (IO) models. However, the compilation of IO models is costly and time-consuming, leading to the lack of timely detailed IO data. The RAS method is traditionally used to predict future IO tables but suffers from doubts for unreliable estimations. Here we develop a machine learning-augmented method to improve the accuracy of the prediction of IO tables using the US summary-level tables as a demonstration. The model is constructed by combining the RAS method with a deep neural network (DNN) model in which the RAS method provides a baseline prediction and the DNN model makes further improvements on the areas where RAS tended to have poor performance. Our results show that the DNN model can significantly improve the performance on those areas in IO tables for short-term prediction (one year) where RAS alone has poor performance, R2 improved from 0.6412 to 0.8726, and median APE decreased from 37.49% to 11.35%. For long-term prediction (5 years), the improvements are even more significant where the R2 is improved from 0.5271 to 0.7893 and median average percentage error is decreased from 51.12% to 18.26%. Our case study on evaluating the US carbon footprint accounts based on the estimated IO table also demonstrates the applicability of the model. Our method can help generate timely IO tables to provide fundamental data for a variety of environmental footprint analyses.

Estimation of Unit Process Data for Life Cycle Assessment Using a Decision Tree-Based Approach.

Lacking unit process data is a major challenge for developing life cycle inventory (LCI) in life cycle assessment (LCA). Previously, we developed a similarity-based approach to estimate missing unit process data, which works only when less than 5% of the data are missing in a unit process. In this study, we developed a more flexible machine learning model to estimate missing unit process data as a complement to our previous method. In particular, we adopted a decision tree-based supervised learning approach to use an existing unit process dataset (ecoinvent 3.1) to characterize the relationship between the known information (predictors) and the missing one (response). The results show that our model can successfully classify the zero and nonzero flows with a very low misclassification rate (0.79% when 10% of the data are missing). For nonzero flows, the model can accurately estimate their values with an R2 over 0.7 when less than 20% of data are missing in one unit process. Our method can provide important data to complement primary LCI data for LCA studies and demonstrates the promising applications of machine learning techniques in LCA.

Urban Air Pollution Mapping Using Fleet Vehicles as Mobile Monitors and Machine Learning.

Spatially explicit urban air quality information is important for developing effective air quality control measures. Traditionally, urban air quality is measured by networks of stationary monitors that are not universally available and sparsely sited. Mobile air quality monitoring using equipped vehicles is a promising alternative but has focused on vehicle-level experiments and lacks fleet-level demonstration. Here, we equipped 260 electric vehicles in a ride-hailing fleet in Beijing, China with low-cost sensors to collect real-time, spatial-resolved data on fine particulate matter (PM2.5) concentrations. Using this data, we developed a decision tree model to infer the distribution of PM2.5 concentrations in Beijing at 1 km by 1 km and 1 h resolution. Our results are able to show both short- and long-term variations of urban PM2.5 concentrations and identify local air pollution hotspots. Compared with a benchmark model that only uses data from stationary monitoring sits, our model has shown significant improvement with the coefficient of determination increased from 0.56 to 0.80 and root mean square error decreased from 12.6 to 8.1 µg/m3. To the best of our knowledge, this study collects the largest mobile sensor data for urban air quality monitoring, which are augmented by state-of-the-art machine learning techniques to derive high-quality urban air pollution mapping. Our results demonstrate the potential and necessity of using fleet vehicles as routine mobile sensors combined with advanced data science methods to provide high-resolution urban air quality monitoring.

Pharmacodynamics and pharmacokinetics of Danshen in isoproterenol-induced acute myocardial ischemic injury combined with Honghua.

ETHNOPHARMACOLOGICAL RELEVANCE: Herb pair, the most fundamental and simplest form of herb compatibility, serves as the basic building block of traditional Chinese medicine formulae. The Danshen-Honghua herb pair (DH), composed of Salviae Miltiorrhizae Radix et Rhizoma (Danshen in Chinese) and Carthami Flos (Honghua in Chinese), has remarkable clinical efficacy to cure cardio-cerebrovascular diseases. This study was designed to investigate the pharmacodynamics of DH in comparison with single herbs and pharmacokinetics of DH relative to Danshen in acute myocardial ischemic injury. MATERIALS AND METHODS: Sixty male Wistar rats were divided into control, model and drug treated groups. The acute myocardial ischemia rat model was induced by administering 85 mg/kg/d isoproterenol (ISO) subcutaneously for two consecutive days. For pharmacodynamic study, histopathological and biochemical analysis were performed to assess the anti-myocardial ischemic effects. While for pharmacokinetic study, a UPLC-MS/MS method was developed for determination of nine main active ingredients, namely danshensu, protocatechuic acid, protocatechualdehyde, caffeic acid, lithospermic acid, rosmarinic acid, salvianolic acid B, salvianolic acid A and salvianolic acid C in rat plasma. RESULTS: The histopathological and biochemical analysis revealed that DH exerted enhanced anti-myocardial ischemic effects against the ISO-induced myocardial ischemia compared with single herbs. The pharmacokinetic study indicated that DH could significantly increase the t1/2z of danshensu, Tmax, AUC0-∞ and MRT0-t of protocatechuic acid in comparison with Danshen alone in normal rats, but more importantly elevate systemic exposure level and prolong t1/2z of protocatechualdehyde, caffeic acid, Tmax of danshensu in acute myocardial ischemia rats. CONCLUSIONS: Our findings demonstrated the greater effects of DH after the compatibility in ISO-induced acute myocardial ischemia rats at pharmacodynamic and pharmacokinetic levels and provided valuable information for clinical application of herb pairs.

Large-scale rapid detection of circulating microRNAs in plasma for diagnosis and screening of specific diseases.

microRNAs are a type of evolutionarily conserved small non-coding RNA with a length of 18-25 nucleotides. In recent years, increasing studies have shown that the content of specific miRNAs in the blood changes significantly during the occurrence and development of major diseases such as cardiovascular disease and cancer. Therefore, miRNAs may serve as important new biomarkers that can be used for disease diagnosis in the future. Here, we improved the polyethylene glycol layer on the surface of a traditional silicon sphere to specifically capture miRNAs by means of a full-function microplate detector, at 100 microliters. The detection limit for specific miRNAs per liter of plasma can reach 1 fM, and simultaneous detection of 96 samples can be achieved. Compared with the traditional real-time PCR technology, our detection eliminates the complex steps of miRNA extraction, reverse transcription, amplification, etc. and avoids more human error in the detection process. Using the full-featured microwell detector, we can rapidly detect specific miRNAs in plasma, which can be used in the diagnosis of cardiovascular diseases in the future.

[Regularity of traditional Chinese medicine prescriptions for same treatment for cardiovascular and cerebrovascular diseases].

To explore the regularity of traditional Chinese medicine(TCM) prescriptions for cardio-cerebrovascular diseases,the core drug groups with common therapeutic effects on cerebrovascular diseases represented by stroke and cardiovascular diseases represented by coronary artery disease were extracted,and their consistency and difference in the treatment of different diseases were analyzed.A total of 388 Chinese patent medicines were collected for the treatment of cerebrovascular diseases,cardiovascular diseases and cardio-cerebrovascular diseases.The dominant and recessive patterns of Chinese patent medicines in clinical use were found by "frequency analysis","compatibility analysis" and "network analysis" respectively.According to the findings of the three parts,Salviae Miltiorrhizae Radix et Rhizoma,Chuanxiong Rhizoma,Carthami Flos and Astragali Radix have a high frequency of use in the treatment of brain disease,heart disease and both,with frequent combined medication.Data mining confirmed the core drug combinations for the treatment of cerebral and cardiac vascular diseases,so as to reveal the similarities and differences in the drug use of Chinese medicine for these diseases,and provide a basis for the rational use of traditional Chinese medicine in clinical practice.This analysis also defines a new direction for the future development of prescription combinations for different indications of cerebral and cardiac diseases.

Influencing factors and prediction of ambient Peroxyacetyl nitrate concentration in Beijing, China.

Peroxyacyl nitrates (PANs) are important secondary pollutants in ground-level atmosphere. Accurate prediction of atmospheric pollutant concentrations is crucial to guide effective precautions for before and during specific pollution events. In this study, four models based on the back-propagation (BP) artificial neural network (ANN) and multiple linear regression (MLR) methods were used to predict the hourly average PAN concentrations at Peking University, Beijing, in 2014. The model inputs were atmospheric pollutant data and meteorological parameters. Model 3 using a BP-ANN based on the original variables achieved the best prediction results among the four models, with a correlation coefficient (R) of 0.7089, mean bias error of -0.0043 ppb, mean absolute error of 0.4836 ppb, root mean squared error of 0.5320 ppb, and Willmott's index of agreement of 0.8214. Based on a comparison of the performance indices of the MLR and BP-ANN models, we concluded that the BP-ANN model was able to capture the highly non-linear relationships between PAN concentration and the conventional atmospheric pollutant and meteorological parameters, providing more accurate results than the traditional MLR models did, with a markedly higher goodness of R. The selected meteorological and atmospheric pollutant parameters described a sufficient amount of PAN variation, and thus provided satisfactory prediction results. More specifically, the BP-ANN model performed very well for capturing the variation pattern when PAN concentrations were low. The findings of this study address some of the existing knowledge gaps in this research field and provide a theoretical basis for future regional air pollution control.

A Comprehensive Strategy to Evaluate Compatible Stability of Chinese Medicine Injection and Infusion Solutions Based on Chemical Analysis and Bioactivity Assay.

Stability of traditional Chinese medicine injection (TCMI) is an important issue related with its clinical application. TCMI is composed of multi-components, therefore, when evaluating TCMI stability, several marker compounds cannot represent global components or biological activities of TCMI. Till now, when evaluating TCMI stability, method involving the global components or biological activities has not been reported. In this paper, we established a comprehensive strategy composed of three different methods to evaluate the chemical and biological stability of a typical TCMI, Danhong injection (DHI). UHPLC-TQ/MS was used to analyze the stability of marker compounds (SaA, SaB, RA, DSS, PA, CA, and SG) in DHI, UHPLC-QTOF/MS was used to analyze the stability of global components (MW 80-1000 Da) in DHI, and cell based antioxidant capability assay was used to evaluate the bioactivity of DHI. We applied this strategy to assess the compatible stability of DHI and six infusion solutions (GS, NS, GNS, FI, XI, and DGI), which were commonly used in combination with DHI in clinic. GS was the best infusion solution for DHI, and DGI was the worst one based on marker compounds analysis. Based on global components analysis, XI and DGI were the worst infusion solutions for DHI. And based on bioactivity assay, GS was the best infusion solution for DHI, and XI was the worst one. In conclusion, as evaluated by the established comprehensive strategy, GS was the best infusion solution, however, XI and DGI were the worst infusion solutions for DHI. In the compatibility of DHI and XI or DGI, salvianolic acids in DHI would be degraded, resulting in the reduction of original composition and generation of new components, and leading to the changes of biological activities. This is the essence of instability compatibility of DHI and some infusion solutions. Our study provided references for choosing the reasonable infusion solutions for DHI, which could contribute the improvement of safety and efficacy of DHI. Moreover, the established strategy may be applied for the compatible stability evaluation of other TCMIs.

DanHong injection targets endothelin receptor type B and angiotensin II receptor type 1 in protection against cardiac hypertrophy.

Cardiac hypertrophy (CH) is an independent risk factor for cardiovascular diseases (CVDs). Mitigating or preventing CH is the most effective strategy for the treatment of CVDs. DanHong injection (DH) is a Chinese herbal medicine preparation (CHMP) widely used in clinical treatment of several CVDs in China. However, the direct targets and cellular mechanisms for these protective effects remain unclear. This study was designed to illustrate the direct targets of DH in protecting against CH and investigate CH molecular pathogenesis. A hypertrophic cell model was induced by endothelin-1 (ET-1) on human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Real time cellular analysis (RTCA) cardio system and high content analysis (HCA) were used to detect the changes in contractile function, morphology and protein level of hypertrophic hiPS-CMs. Agonist and antagonist assay on receptors were performed using calcium mobilization high-throughput screening (HTS). DH significantly attenuated CH by modulating myocardial contractility, suppressing cell area enlargement and down-regulating ET-1-induced brain natriuretic peptide (BNP), actinin alpha 2 (ACTN2) and cardiac muscle troponin T (TNNT2) protein expression (P < 0.05). Endothelin receptor type B (ETBR) and angiotensin II receptor type 1 (AT1R) were DH direct targets, with IC50 value of 25.67 µL/mL and 1.10 µL/mL, respectively. Proteomics analysis showed that proteins involved in cell cycle inhibition, RNA processing, mitochondrial translation and cytoskeleton are significant regulated by DH treatment. These data revealed that ETBR and AT1R are DH direct targets on protecting against CH, providing a strategy to explore direct targets of CHMPs.

[Comparative study on promoting blood effects of Danshen-Honghua herb pair with different preparations based on chemometrics and multi-attribute comprehensive index methods].

To evaluate the promoting blood circulation and removing blood stasis effects of Danshen-Honghua(DH) herb pair with different preparations (alcohol, 50% alcohol and water) on blood rheology and coagulation functions in acute blood stasis rats, and optimize the best preparation method of DH based on principal component analysis(PCA), hierarchical cluster heatmap analysis and multi-attribute comprehensive index methods. Ice water bath and subcutaneous injection of adrenaline were both used to establish the acute blood stasis rat model. Then the blood stasis rats were administrated intragastrically with DH (alcohol, 50% alcohol and water) extracts. The whole blood viscosity(WBV), plasma viscosity(PV), erythrocyte sedimentation rate(ESR) and haematocrit(HCT) were tested to observe the effects of DH herb pair with different preparations and doses on hemorheology of blood stasis rats; the activated partial thromboplastin time(APTT), thrombin time(TT), prothrombin time(PT), and plasma fibrinogen(FIB) were tested to observe the effects of DH herb pair with different preparations on blood coagulation function and platelet aggregation of blood stasis rats. Then PCA, hierarchical cluster heatmap analysis and multi-attribute comprehensive index methods were all used to comprehensively evaluate the total promoting blood circulation and removing blood stasis effects of DH herb pair with different preparations. The hemorheological indexes and coagulation parameters of model group had significant differences with normal blank group. As compared with the model group, the DH herb pair with different preparations at low, middle and high doses could improve the blood hemorheology indexes and coagulation parameters in acute blood stasis rats with dose-effect relation. Based on the PCA, hierarchical cluster heatmap analysis and multi-attribute comprehensive index methods, the high dose group of 50% alcohol extract had the best effect of promoting blood circulation and removing blood stasis. Under the same dose but different preparations, 50% alcohol DH could obviously improve the hemorheology and blood coagulation function in acute blood stasis rats. These results suggested that DH herb pair with different preparations could obviously ameliorate the abnormality of hemorheology and blood coagulation function in acute blood stasis rats, and the optimized preparation of DH herb pair on promoting blood effects was 50% alcohol extract, providing scientific basis for more effective application of the DH herb pair in modern clinic medicine.

Comparative analysis of main bio-active components in the herb pair Danshen-Honghua and its single herbs by ultra-high performance liquid chromatography coupled to triple quadrupole tandem mass spectrometry.

A sensitive, reliable, and powerful ultra-high performance liquid chromatography coupled to triple quadrupole tandem mass spectrometry method was developed for simultaneous quantification of the 15 main bio-active components including phenolic acids and flavonoids within 13 min for the first time. The proposed method was first reported and validated by good linearity (r2  > 0.9975), limit of detection (1.12-7.01 ng/mL), limit of quantification (3.73-23.37 ng/mL), intra- and inter-day precisions (RSD ≤ 1.92%, RSD ≤ 2.45%), stability (RSD ≤ 5.63%), repeatability (RSD ≤ 4.34%), recovery (96.84-102.12%), and matrix effects (0.92-1.02). The established analytical methodology was successfully applied to comparative analysis of main bio-active components in the herb pair Danshen-Honghua and its single herbs. Compared to the single herb, the content of most flavonoid glycosides was remarkably increased in their herb pair, and main phenolic acids were decreased, conversely. The content changes of the main components in the herb pair supported the synergistic effects on promoting blood circulation and removing blood stasis. The results provide a scientific basis and reference for the quality control of Danshen-Honghua herb pair and the drug interactions based on variation of bio-active components in herb pairs.

Emission inventory and environmental distribution of decabromodiphenyl ether in China.

Decabromodiphenyl ether (decaBDE) is a highly brominated flame retardant that recent studies have identified as a potential persistent organic pollutant. Large amounts of decaBDE have been consumed and released in the environment in China, while no emission inventory has been available until now. In this study, a substance flow analysis was applied to establish the emission inventory of decaBDE in China from 1982 (the first year of decaBDE production in China) until 2013 based on activity data, transfer coefficients, and emission factors. The results show that the stock of decaBDE continually increased, reaching a peak of 290,000tons in 2007. The annual processing capacity of decaBDE also increased, and the processing capacity in 2013 was 49,000tons. Historical accumulative emissions were estimated to be 313.3tons from 1982 to 2013, and the annual emissions peaked in 2003 at 27.5tons. On average, decaBDE processing was the major source (58.4%) of total emissions, followed by treatment, production, and usage processes. From 1982 to 2013, decaBDE was released mainly into water sources, accounting for 50.7% of the accumulative emissions. At the provincial level, Guangdong, Shandong, and Zhejiang provinces were the largest producers in China. Simulations produced by the level III fugacity model showed that the projected concentration was very consistent with the measured value. The stock of decaBDE in the soil and sediment phases accounted for 99.8% of the total stock, and the transfer among the four environmental phases occurred mainly at the atmosphere-soil interface.

Quantitative determination of five metabolites of aspirin by UHPLC-MS/MS coupled with enzymatic reaction and its application to evaluate the effects of aspirin dosage on the metabolic profile.

Acetylsalicylic acid (Aspirin, ASA) is a famous drug for cardiovascular diseases in recent years. Effects of ASA dosage on the metabolic profile have not been fully understood. The purpose of our study is to establish a rapid and reliable method to quantify ASA metabolites in biological matrices, especially for glucuronide metabolites whose standards are not commercially available. Then we applied this method to evaluate the effects of ASA dosage on the metabolic and excretion profile of ASA metabolites in rat urine. Salicylic acid (SA), gentisic acid (GA) and salicyluric acid (SUA) were determined directly by UHPLC-MS/MS, while salicyl phenolic glucuronide (SAPG) and salicyluric acid phenolic glucuronide (SUAPG) were quantified indirectly by measuring the released SA and SUA from SAPG and SUAPG after ß-glucuronidase digestion. SUA and SUAPG were the major metabolites of ASA in rat urine 24h after ASA administration, which accounted for 50% (SUA) and 26% (SUAPG). When ASA dosage was increased, the contributions dropped to 32% and 18%, respectively. The excretion of other three metabolites (GA, SA and SAPG) however showed remarkable increases by 16%, 6% and 4%, respectively. In addition, SUA and SUAPG were mainly excreted in the time period of 12-24h, while GA was excreted in the earlier time periods (0-4h and 4-8h). SA was mainly excreted in the time period of 0-4h and 12-24h. And the excretion of SAPG was equally distributed in the four time periods. We went further to show that the excretion of five metabolites in rat urine was delayed when ASA dosage was increased. In conclusion, we have developed a rapid and sensitive method to determine the five ASA metabolites (SA, GA, SUA, SAPG and SUAPG) in rat urine. We showed that ASA dosage could significantly influence the metabolic and excretion profile of ASA metabolites in rat urine.