The correlation between serum asprosin and left ventricular diastolic dysfunction in elderly patients with type 2 diabetes mellitus in the community.

AIMS/INTRODUCTION: Serum asprosin is expected to become a screening indicator in early-stage diabetic heart disease. The relationship between serum asprosin and left ventricular diastolic dysfunction (LVDD) was studied in elderly patients with type 2 diabetes mellitus in the community. MATERIALS AND METHODS: A total of 252 elderly patients with type 2 diabetes mellitus were recruited from Zhuoma Community Care Station and Chengbei West Street Community Care Service Center in Changzhi City of Shanxi Province from November 2019 to July 2021. Patients were divided into the LVDD group (n = 195) and the non-LVDD group (n = 57). The t-test, Mann-Whitney U test, and χ2 test were used to compare indicators between the LVDD group and the non-LVDD group. Pearson or Spearman correlation analysis was adopted to evaluate the correlation between serum asprosin and other clinical data. Multivariate logistic regression analysis was applied to analyze the influencing factors on LVDD. RESULTS: Compared with patients without LVDD, patients with LVDD had a higher level of low-density lipoprotein cholesterol (LDL-C), fasting blood glucose (FPG), and asprosin, but a lower level of early diastolic movement speed (A) to diastolic movement velocity (E) (E/A). Asprosin was positively associated with waist circumference (WC), body mass index (BMI), creatinine, triglycerides (P < 0.05), and negatively associated with E/A and high density lipoprotein cholesterol HDL-C (P < 0.05). The risk of LVDD increased with elevated asprosin levels after adjustment for age, systolic blood pressure (SBP), BMI, FPG, and LDL-C. Compared with patients in the lowest tertile of serum asprosin (<275.25 pg/mL), a serum level of asprosin between 275.25-355.08 pg/mL [OR (95% CI) is 2.368 (1.169-4.796), P < 0.05] and asprosin >355.08 pg/mL [OR (95% CI) is 2.549 (1.275-5.095), P < 0.05] patients have a higher risk of left ventricular diastolic dysfunction. CONCLUSIONS: Serum asprosin was positively associated with left ventricular diastolic dysfunction, and the risk of LVDD increased significantly with increased serum levels of asprosin.

Insights into PM2.5 pollution of four small and medium-sized cities in Chinese representative regions: Chemical compositions, sources and health risks.

Fine particles (PM2.5) pollution is still a severe issue in some cities in China, where the chemical characteristics of PM2.5 remain unclear due to limited studies there. Herein, we focused on PM2.5 pollution in small and medium-sized cities in key urban agglomerations and conducted a comprehensive study on the PM2.5 chemical characteristics, sources, and health risks. In the autumn and winter of 2019-2020, PM2.5 samples were collected simultaneously in four small and medium-sized cities in four key regions: Dingzhou (Beijing-Tianjin-Hebei region), Weinan (Fenwei Plain region), Fukang (Northern Slope of the Tianshan Mountain region), and Bozhou (Yangtze River Delta region). The results showed that secondary inorganic ions (43.1 %-67.0 %) and organic matter (OM, 8.6 %-36.4 %) were the main components of PM2.5 in all the cities. Specifically, Fukang with the most severe PM2.5 pollution had the highest proportion of SO42- (31.2 %), while the dominant components in other cities were NO3- and OM. The Multilinear Engine 2 (ME2) analysis identified five sources of PM2.5 in these cities. Coal combustion contributed most to PM2.5 in Fukang, but secondary sources in other cities. Combined with chemical characteristics and ME2 analysis, it was preliminarily determined that the primary emission of coal combustion had an important contribution to high SO42- in Fukang. Potential source contribution function (PSCF) analysis results showed that regional transport played an important role in PM2.5 in Dingzhou, Weinan and Bozhou, while PM2.5 in Fukang was mainly affected by short-range transport from surrounding areas. Finally, the health risk assessment indicated Mn was the dominant contributor to the total non-carcinogenic risks and Cr had higher carcinogenic risks in all cities. The findings provide a scientific basis for formulating more effective abatement strategies for PM2.5 pollution.

Enabling Self-Adaptive Water-Energy-Balance of Photothermal Water Diode Evaporator: Dynamically Maximizing Energy Utilization Under the Ever-Changing Sunlight.

Maintaining a match between input solar energy and required energy by water supply management is key to achieving efficient interfacial solar-driven evaporation (ISDE). In practice, the solar radiation flux is constantly changing throughout the day, so keeping a dynamic water-energy-balance of ISDE is a big challenge. Herein, a photothermal water diode (WD) evaporator concept is proposed by an integrated hydrophilic/hydrophobic Janus absorber to overcome the issue. Due to the unique unidirectional water transport properties induced by asymmetric wettability, a self-adaptive balance between photothermal energy input and water uptake is established, thus realizing the energy matching and utilization maximization. The experimental and simulation results exhibit that with the increase of sunlight intensity, the water supply speed is significantly accelerated due to the dynamic management and self-regulation on water replenishment. Therefore, an excellent evaporation rate of up to 2.14 kg m-2 h-1 with a high efficiency of 93.7% under 1 sun illumination is achieved. This water diode engineering with Janus wettability provides a novel strategy and extends the path for designing solar evaporation systems with diverse water supply properties, which shows great potential in different environmental conditions.

Comparison of Thermal Deformation Behavior and Characteristics of Mg-Gd-Y-Zn Alloys with and without Bulk LPSO Phase.

Alloys Mg-8Gd-4Y-0.6Zn-0.5Zr (referred to as 0.6Zn) without the bulk long-period stacking ordered (LPSO) phase and Mg-8Gd-4Y-1.1Zn-0.5Zr (referred to as 1.1Zn) containing the bulk LPSO phase were prepared and a series of hot compression tests were conducted to examine and evaluate the influence of the bulk LPSO phase on the thermal deformation behavior and characteristics of the Mg-Gd-Y-Zn-Zr alloy. The bulk LPSO phase affects the dynamic recrystallization behavior, resulting in differences in flow stress between two alloys under different conditions. Specifically, in the temperature range of 380~460 °C, compression at lower strain rates is beneficial for the LPSO phase to promote dynamic recrystallization, while compression at a high strain rate inhibits the dynamic recrystallization due to the severe deformation of the bulk LPSO phase to release the stress concentration instead. The increase in temperature helps the LPSO promote dynamic recrystallization. As a result, the LPSO phase promotes dynamic recrystallization at all experimental strain rates at 500 °C. Furthermore, the thermal processing maps of the 0.6Zn and 1.1Zn alloys are established, and their optimal processing windows are located at 500 °C/0.001~0.01 s-1 and 500 °C/0.01 s-1, respectively. In addition, the instability zones for the 1.1Zn alloy are much larger than that for the 0.6Zn alloy, which corresponds to the microcracks generated at the interfaces between α-Mg and bulk LPSO phases.

Neuroblastoma of the lumbosacral canal in an adult: a case report and literature review.

Neuroblastoma (NB) is a leading cause of death in children. It usually occurs in the adrenal gland and rarely in the spinal canal. Here, we report the case of a 48-year-old male patient with abnormal thickening of the cauda equina nerve as revealed by lumbosacral magnetic resonance imaging. The patient's main clinical manifestations were numbness and pain in both lower limbs. The patient underwent surgical treatment; however, intraoperatively, an unclear border was observed between the cauda equina nerve and the tumor; therefore, the tumor was not forcibly excised. The postoperative pathological results were reported as NB. The disease known as NB, which is extremely rare. We believe that a pathological biopsy is extremely vital for diagnosing NB, and aggressive post-operative radio-chemotherapy could potentially prolong the patient's survival time.

Strategies for the coordinated control of particulate matter and carbon dioxide under multiple combined pollution conditions.

Air pollutants represented by fine particulate matter (PM2.5) and the greenhouse effect caused by carbon dioxide (CO2), are both urgent threats to public health. Tackling the synergistic reduction of PM2.5 and CO2 is critical to achieving improvements in clean air worldwide. A persistent issue is the identification of their common sources and integrated impacts under different environmental conditions. In this study, we investigated the characteristics of the pollution types captured by combined analysis through a comprehensive observational dataset for 2017-2020, and applied machine learning algorithms to quantify the effects of drivers on air pollutants and CO2 formation. More importantly, detailed conclusions were drawn for the joint control of PM2.5-CO2 in multiple pollution types by using ensemble traceability technique. We demonstrated that reducing coal combustion emissions was an effective measure to maximize the benefits of PM2.5-CO2 in weather with low CO2 levels and no PM2.5 pollution. Correspondingly, on days with severe PM2.5 episodes, prioritizing control of vehicle emissions can simultaneously mitigate PM2.5 and CO2. Similar conclusions were found at high CO2 levels, accompanied by a more extensive role of vehicle emissions. Furthermore, a comparison of the differences in source impacts between PM2.5-CO2 and individual species suggests that focusing only on the sources that contribute significantly to one species may result in an underestimation or overestimation of PM2.5-CO2 source impacts. One such implication, as evidenced by our findings, is that synergistic controlling common sources of pollutants should be efficient. Thereby, common source management targeting PM2.5-CO2 under multiple pollution types is a more workable solution to alleviate environmental pollution.

Sustainable Operation and Maintenance Modeling and Application of Building Infrastructures Combined with Digital Twin Framework.

Sustainable management is a challenging task for large building infrastructures due to the uncertainties associated with daily events as well as the vast yet isolated functionalities. To improve the situation, a sustainable digital twin (DT) model of operation and maintenance for building infrastructures, termed SDTOM-BI, is proposed in this paper. The proposed approach is able to identify critical factors during the in-service phase and achieve sustainable operation and maintenance for building infrastructures: (1) by expanding the traditional 'factor-energy consumption' to three parts of 'factor-event-energy consumption', which enables the model to backtrack the energy consumption-related factors based on the relevance of the impact of random events; (2) by combining with the Bayesian network (BN) and random forest (RF) in order to make the correlation between factors and results more clear and forecasts more accurate. Finally, the application is illustrated and verified by the application in a real-world gymnasium.

Abundant nitrogen oxide and weakly acidic environment synergistically promote daytime particulate nitrate pollution.

Nitrate is formed through the chemical production of gas-phase nitric acid and subsequent partitioning to the aerosol phase during the daytime. Many studies in the past separated these two aspects, even though they occur simultaneously in the atmosphere. To better understand the nitrate formation mechanism and effectively mitigate its production, it is necessary to consider the synergy between these two mechanisms. For this, we analyze hourly-speciated ambient observations data, with EK&TMA (Empirical Kinetic & Thermodynamic Modeling Approach) map to comprehensively explore the factors controlling nitrate production. Results show that precursor NO2 concentration and aerosol pH, which are related to anthropogenic activities, are the two major factors for chemical kinetics production and gas/particle thermodynamic partitioning processes respectively. Abundant NO2 and weakly acidic environments are favorable conditions for daytime particulate nitrate pollution, thus collaborative control of coal source, vehicle source, and dust source is needed to alleviate nitrate pollution.

Source apportionment of fine particulate matter at a megacity in China, using an improved regularization supervised PMF model.

The source apportionment of particulate matter plays an important role in solving the atmospheric particulate pollution. Positive matrix factorization (PMF) is a widely used source apportionment model. At present, high resolution online datasets are increasingly rich, but acquiring accurate and timely source apportionment results is still challenging. Integrating prior knowledge into modelling process is an effective solution and can yield reliable results. This study proposed an improved source apportionment method for the regularization supervised PMF model (RSPMF). This method leveraged actual source profile to guide factor profile for rapidly and automatically identifying source categories and quantifying source contributions. The results showed that the factor profile from RSPMF could be interpreted as seven factors and approach to actual source profile. Average source contributions were also an agreement between RSPMF and EPAPMF, including secondary nitrate (26 %, 27 %), secondary sulfate (23 %, 24 %), coal combustion (18 %, 18 %), vehicle exhaust (15 %, 15 %), biomass burning (10 %, 9 %), dust (5 %, 4 %), industrial emission (3 %, 3 %). The solutions of RSPMF also exhibited good generalizability during different episodes. This study reveals the superiority of supervised model, this model embeds prior knowledge into modelling process to guide model for obtaining more reliable results.

Trends of source apportioned PM2.5 in Tianjin over 2013-2019: Impacts of Clean Air Actions.

A long-term (2013-2019) PM2.5 speciation dataset measured in Tianjin, the largest industrial city in northern China, was analyzed with dispersion normalized positive matrix factorization (DN-PMF). The trends of source apportioned PM2.5 were used to assess the effectiveness of source-specific control policies and measures in support of the two China's Clean Air Actions implemented nationwide in 2013-2017 and 2018-2020, respectively. Eight sources were resolved from the DN-PMF analysis: coal combustion (CC), biomass burning (BB), vehicular emissions, dust, steelmaking and galvanizing emissions, a mixed sulfate-rich factor and secondary nitrate. After adjustment for meteorological fluctuations, a substantial improvement in PM2.5 air quality was observed in Tianjin with decreases in PM2.5 at an annual rate of 6.6%/y. PM2.5 from CC decreased by 4.1%/y. The reductions in SO2 concentration, PM2.5 contributed by CC, and sulfate demonstrated the improved control of CC-related emissions and fuel quality. Policies aimed at eliminating winter-heating pollution have had substantial success as shown by reduced heating-related SO2, CC, and sulfate from 2013 to 2019. The two industrial source types showed sharp drops after the 2013 mandated controls went into effect to phaseout outdated iron/steel production and enforce tighter emission standards for these industries. BB reduced significantly by 2016 and remained low due to the no open field burning policy. Vehicular emissions and road/soil dust declined over the Action's first phase followed by positive upward trends, showing that further emission controls are needed. Nitrate concentrations remained constant although NOX emissions dropped significantly. The lack of a decrease in nitrate may result from increased ammonia emissions from enhanced vehicular NOX controls. The port and shipping emissions were evident implying their impacts on coastal air quality. These results affirm the effectiveness of the Clean Air Actions in reducing primary anthropogenic emissions. However, further emission reductions are needed to meet global health-based air quality standards.

Quantitative evidence from VOCs source apportionment reveals O3 control strategies in northern and southern China.

Ground-level ozone (O3) pollution has received widespread attention because its rising trend and adverse ecological impacts. However, the extremely strong photochemical reactions of its precursor volatile organic compounds (VOCs) increase the difficulty of reducing VOCs emissions to alleviate O3. Here, we carried out a one-year comprehensive observation in two representative cities, Tianjin (TJ, Northern China) and Guangzhou (GZ, Southern China). By revealing the concentration characteristics of three different types of VOCs, i.e., initial VOCs without photochemical reaction (In-VOCs), consumed VOCs (C-VOCs), and measured VOCs after the reaction (M-VOCs), we elucidated the important role of C-VOCs in the formation of O3. Although the overall trends were similar in both cities, the average concentration level of VOCs in GZ was 8.2 ppbv higher than that in TJ, and the photochemical loss of VOCs was greater by 2.2 ppbv. In addition, various drivers affecting O3 generation from C-VOCs were specifically explored, and it was found that most alkenes of TJ were key substances for rapid O3 formation compared to aromatics of GZ. Meanwhile, favorable meteorological conditions such as high temperature (T > 31 °C in TJ, and T > 33 °C in GZ), low relative humidity (56% in TJ and 45% in GZ), and stable atmospheric environment (proper pressure and gentle wind speed) also contribute to the generation of O3. More importantly, we combined chemical kinetics and receptor model to quantify the three-type VOCs source contributions and assess the potential impact of C-VOCs sources on O3 production, thus proposing environmental abatement technologies corresponding to the three types of VOCs. The differences in the comparison results of the three-type VOCs highlight the need to reduce O3 pollution from C-VOCs sources, which provides insights for future clean air policies development.

The high-dimensional geographic dataset revealed significant differences in the migration ability of cadmium from various sources in paddy fields.

Cadmium (Cd) contamination in paddy fields and its subsequent transfer in soil-rice systems are of particular concern. Significant discrepancies exist in the transfer process of Cd pollution sources from soil to rice. Here, we proposed a novel hybrid framework to reveal the priority of controlling Cd pollution sources in soil-rice systems, based on a high-dimensional geographical database. We further defined transfer potential (TP) to describe the ability of Cd from soil to rice (TPr = Cdr/Cds) and activated status (TPa = Cda/Cds), respectively, to reveal the priority sources of Cd pollution at the regional scale. The mining source has both high levels of TPr and TPa, which should be a controlled priority. Followed by traffic sources with a higher value of TPr, showing the risk to rice rather than the soil. The activated and enriched capacities of soil Cd are unequal in different sources that we attribute to the disparities of Cd transport in soil-rice systems. Cd contamination shows a significant spatial heterogeneity due to the difference in its transport performance. Our findings provide support for designing site-specific and pollution-targeted control priorities for suitable Cd pollution mitigation strategies at the regional scale.

A hybrid framework for delineating the migration route of soil heavy metal pollution by heavy metal similarity calculation and machine learning method.

Soil heavy metal contamination was a global environmental issue that posed adverse impacts on ecological and human health risks. The controlling of soil heavy metal is mainly focused on the emission source and pipe-end treatment, less is known about the intermediate controlling process. The migration route of heavy metals exhibited the spatial evolution of pollutants from the sources to the pipe-end, which provided the more reasonable location for the target-oriented treatment of soil heavy metal. Here, we proposed a new view of heavy metal similarity, which quantitatively expressed how closely of the contaminations between the study area and the test areas. We found that the similarity of different heavy metals was unequally distributed across locations that were related with five main sources, namely agricultural activities, natural sources, traffic emissions, industrial activities, and other sources. Based on the similarity, a state-of-the-art machine learning method was applied to delineate the migration route of soil heavy metals. Thereinto, As was concentrated around livestock farms, and its migration route was close to the water system. Cd migration route was over-dispersed in the areas where located mine fields and chemical plants. Migration routes of Hg and Pb were along rivers, which were related to agricultural activities and natural sources. Overall, the perspective on similarity and migration routes provided theoretical basis and method to alleviate soil heavy metal pollution at regional scale and can be extended across largescale regions.

The role of source emissions in sulfate formation pathways based on chemical thermodynamics and kinetics model.

Sulfate is a major PM2.5 constituent and poses a significant threat to ecosystems and human health, which has attracted lots of attention to the sulfate formation mechanism. In recent years, there has been great scientific interest in the multiphase oxidation of SO2 in aqueous aerosol particles. Many factors are involved in the reaction process, including precursor SO2, oxidants/catalysts, and aerosol acidity, which are three channels closely related to the source emission. The conjoint analysis of source emissions and sulfate aqueous formation can provide a scientific basis for designing effective strategies, though the related research is extremely limited. Here, we applied an improved solute strength-dependent chemical Thermodynamics & Kinetics model (for aqueous pathway contribution) and the Partial Target Transformation-Positive matrix factor model (for source apportionment) to explore the role of source emission in sulfate aqueous formation. The results indicated H2O2 aqueous oxidation was the dominant pathway (65.9 %), and secondary nitrate source may grow together with sulfate formation from H2O2 pathway. H2O2 and TMI pathways were related to higher SOR (sulfur oxidation rate). TMI pathway was significant in summer (54.6 %) and increased with secondary sources and vehicle exhaust. NO2 pathway was more significant at low secondary source and high coal combustion (higher contribution of NO2 pathway appeared in winter, 24.7 %). While high formation rate of the O3 pathway always occurred at low source levels. Coal combustion and vehicle exhaust showed obvious effects on sulfate aqueous formation. Notably, aerosol acidity is a significant factor related to sources and plays a key role in sulfate formation. The result also suggested aerosol pH may be more important than the amounts of substances involved in the oxidation reaction. The findings in this work can provide useful information for better understanding sulfate aqueous formation and offer a scientific basis for designing strategies for air pollution control and sulfate mitigation.

The impact of environmental risk and platform trust on satisfaction with health qr code use.

In response to the economic and social hazards posed by the COVID-19 global pandemic, many countries have adopted various information technologies to rapidly track and control the spread of the epidemic. Health Quick Response (QR) codes are emergency measures implemented by the Chinese government in the epidemic environment to balance epidemic prevention and control with recovery of economic and social development, and facilitate public mobility across regions and access to various public spaces. The use of health codes by the public is a practical necessity, but the satisfaction of their use is influenced by multiple factors such as environment, technology, and organization. In this paper, we collected data through a questionnaire to analyze the basic situation of public satisfaction with the use of health QR codes in China and its influencing factors. The results show that perceived quality and platform trust directly affect the satisfaction of health code usage, while environmental risk and platform trust indirectly affect the satisfaction of health code usage through the mediating effect of perceived quality.

Application of an Improved Gas-constrained Source Apportionment Method Using Data Fused Fields: a Case Study in North Carolina, USA.

A number of studies have found differing associations of disease outcomes with PM2.5 components (or species) and sources (e.g., biomass burning, diesel vehicles and gasoline vehicles). Here, a unique method of fusing daily chemical transport model (Community Multiscale Air Quality Modeling) results with observations has been utilized to generate spatiotemporal fields of the concentrations of major gaseous pollutants (CO, NO2, NOx, O3, and SO2), total PM2.5 mass, and speciated PM2.5 (including crustal elements) over North Carolina for 2002-2010. The fused results are then used in chemical mass balance source apportionment model, CMBGC-Iteration, which uses both gas constraint and particulate matter concentrations to quantify source impacts. The method, as applied to North Carolina, quantifies the impacts of ten source categories and provides estimates of source contributions to PM2.5 concentrations. The ten source categories include both primary sources (diesel vehicles, gasoline vehicles, dust, biomass burning, coal-fired power plants and sea salt) and secondary components (ammonium sulfate, ammonium bisulfate, ammonium nitrate and secondary organic carbon). The results show a steady decrease in anthropogenic source impacts, especially from diesel vehicles and coal-fired power plants. Secondary pollutant components accounted for approximately 70% of PM2.5 mass. This study demonstrates an ability to provide spatiotemporal fields of both PM components and source impacts using a chemical transport model fused with observation data, linked to a receptor-based source apportionment method, to develop spatiotemporal fields of multiple pollutants.

Targeting Atmospheric Oxidants Can Better Reduce Sulfate Aerosol in China: H2O2 Aqueous Oxidation Pathway Dominates Sulfate Formation in Haze.

Particulate sulfate is one of the most important components in the atmosphere. The observation of rapid sulfate aerosol production during haze events provoked scientific interest in the multiphase oxidation of SO2 in aqueous aerosol particles. Diverse oxidation pathways can be enhanced or suppressed under different aerosol acidity levels and high ionic strength conditions of atmospheric aerosol. The importance of ionic strength to sulfate multiphase chemistry has been verified under laboratory conditions, though studies in the actual atmosphere are still limited. By utilizing online observations and developing an improved solute strength-dependent chemical thermodynamics and kinetics model (EF-T&K model, EF is the enhancement factor that represents the effect of ionic strength on an aerosol aqueous-phase reaction), we provided quantitative evidence that the H2O2 pathway was enhanced nearly 100 times and dominated sulfate formation for entire years (66%) in Tianjin (a northern city in China). TMI (oxygen catalyzed by transition-metal ions) (14%) and NO2 (14%) pathways got the second-highest contributions. Machine learning supported the result that aerosol sulfate production was more affected by the H2O2 pathway. The collaborative effects of atmospheric oxidants and SO2 on sulfate aerosol production were further investigated using the improved EF-T&K model. Our findings highlight the effectiveness of adopting target oxidant control as a new direction for sustainable mitigation of sulfate, given the already low SO2 concentrations in China.

Association Between Serum Asprosin and Diabetic Nephropathy in Patients with Type 2 Diabetes Mellitus in the Community: A Cross-Sectional Study.

Objective: To explore the association between serum asprosin and diabetic nephropathy (DN) in patients with type 2 diabetes mellitus (T2DM) in the community. Methods: In this cross-sectional study, we retrospectively collected the clinical data of T2DM patients from a community health service center in southeastern Shanxi Province between November 2019 and July 2021. Logistic regression analysis was used to calculate the odds ratio (OR) and the 95% confidence interval (95% CI) of asprosin levels on the risk of DN. Results: Among 498 T2DM patients included in this study, 221 had microalbuminuria, 105 had massive albuminuria, and 172 did not have any signs of nephropathy. Serum asprosin level was positively correlated with diastolic blood pressure, body mass index, triglycerides, aspartate aminotransferase, alanine aminotransferase, creatinine, ACR and albumin-to-creatinine ratio (all P < 0.05) and negatively correlated with low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, fasting plasma glucose, HbA1c and estimated glomerular filtration rate (all P < 0.05). After adjusting for covariates, increased asprosin was associated with diabetic nephropathy (all OR = 2.560, 95% CI: 1.1592-4.116; P < 0.001). Conclusion: The risk of DN significantly increases with serum asprosin levels, especially among female patients.