Comparative mathematical modeling of causal association between metal exposure and development of chronic kidney disease.

Background: Previous studies have identified several genetic and environmental risk factors for chronic kidney disease (CKD). However, little is known about the relationship between serum metals and CKD risk. Methods: We investigated associations between serum metals levels and CKD risk among 100 medical examiners and 443 CKD patients in the medical center of the First Hospital Affiliated to China Medical University. Serum metal concentrations were measured using inductively coupled plasma mass spectrometry (ICP-MS). We analyzed factors influencing CKD, including abnormalities in Creatine and Cystatin C, using univariate and multiple analysis such as Lasso and Logistic regression. Metal levels among CKD patients at different stages were also explored. The study utilized machine learning and Bayesian Kernel Machine Regression (BKMR) to assess associations and predict CKD risk based on serum metals. A chained mediation model was applied to investigate how interventions with different heavy metals influence renal function indicators (creatinine and cystatin C) and their impact on diagnosing and treating renal impairment. Results: Serum potassium (K), sodium (Na), and calcium (Ca) showed positive trends with CKD, while selenium (Se) and molybdenum (Mo) showed negative trends. Metal mixtures had a significant negative effect on CKD when concentrations were all from 30th to 45th percentiles compared to the median, but the opposite was observed for the 55th to 60th percentiles. For example, a change in serum K concentration from the 25th to the 75th percentile was associated with a significant increase in CKD risk of 5.15(1.77,8.53), 13.62(8.91,18.33) and 31.81(14.03,49.58) when other metals were fixed at the 25th, 50th and 75th percentiles, respectively. Conclusions: Cumulative metal exposures, especially double-exposure to serum K and Se may impact CKD risk. Machine learning methods validated the external relevance of the metal factors. Our study highlights the importance of employing diverse methodologies to evaluate health effects of metal mixtures.

Lagging effects and prediction of pollutants and their interaction modifiers on influenza in northeastern China.

BACKGROUND: Previous studies have typically explored the daily lagged relations between influenza and meteorology, but few have explored seasonally the monthly lagged relationship, interaction and multiple prediction between influenza and pollution. Our specific objectives are to evaluate the lagged and interaction effects of pollution factors and construct models for estimating influenza incidence in a hierarchical manner. METHODS: Our researchers collect influenza case data from 2005 to 2018 with meteorological and contaminative factors in Northeast China. We develop a generalized additive model with up to 6 months of maximum lag to analyze the impact of pollution factors on influenza cases and their interaction effects. We employ LASSO regression to identify the most significant environmental factors and conduct multiple complex regression analysis. In addition, quantile regression is taken to model the relation between influenza morbidity and specific percentiles (or quantiles) of meteorological factors. RESULTS: The influenza epidemic in Northeast China has shown an upward trend year by year. The excessive incidence of influenza in Northeast China may be attributed to the suspected primary air pollutant, NO2, which has been observed to have overall low levels during January, March, and June. The Age 15-24 group shows an increase in the relative risk of influenza with an increase in PM2.5 concentration, with a lag of 0-6 months (ERR 1.08, 95% CI 0.10-2.07). In the quantitative analysis of the interaction model, PM10 at the level of 100-120 µg/m3, PM2.5 at the level of 60-80 µg/m3, and NO2 at the level of 60 µg/m3 or more have the greatest effect on the onset of influenza. The GPR model behaves better among prediction models. CONCLUSIONS: Exposure to the air pollutant NO2 is associated with an increased risk of influenza with a cumulative lag effect. Prioritizing winter and spring pollution monitoring and influenza prediction modeling should be our focus.

Time series analyses based on the joint lagged effect analysis of pollution and meteorological factors of hemorrhagic fever with renal syndrome and the construction of prediction model.

BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS) is a rodent-related zoonotic disease induced by hantavirus. Previous studies have identified the influence of meteorological factors on the onset of HFRS, but few studies have focused on the stratified analysis of the lagged effects and interactions of pollution and meteorological factors on HFRS. METHODS: We collected meteorological, contaminant and epidemiological data on cases of HFRS in Shenyang from 2005-2019. A seasonal autoregressive integrated moving average (SARIMA) model was used to predict the incidence of HFRS and compared with Holt-Winters three-parameter exponential smoothing model. A distributed lag nonlinear model (DLNM) with a maximum lag period of 16 days was applied to assess the lag, stratification and extreme effects of pollution and meteorological factors on HFRS cases, followed by a generalized additive model (GAM) to explore the interaction of SO2 and two other meteorological factors on HFRS cases. RESULTS: The SARIMA monthly model has better fit and forecasting power than its own quarterly model and the Holt-Winters model, with an optimal model of (1,1,0) (2,1,0)12. Overall, environmental factors including humidity, wind speed and SO2 were correlated with the onset of HFRS and there was a non-linear exposure-lag-response association. Extremely high SO2 increased the risk of HFRS incidence, with the maximum RR values: 2.583 (95%CI:1.145,5.827). Extremely low windy and low SO2 played a significant protective role on HFRS infection, with the minimum RR values: 0.487 (95%CI:0.260,0.912) and 0.577 (95%CI:0.370,0.898), respectively. Interaction indicated that the risk of HFRS infection reached its highest when increasing daily SO2 and decreasing humidity. CONCLUSIONS: The SARIMA model may help to enhance the forecast of monthly HFRS incidence based on a long-range dataset. Our study had shown that environmental factors such as humidity and SO2 have a delayed effect on the occurrence of HFRS and that the effect of humidity can be influenced by SO2 and wind speed. Public health professionals should take greater care in controlling HFRS in low humidity, low windy conditions and 2-3 days after SO2 levels above 200 µg/m3.

Photocatalytic reduction of Cr(VI) in aqueous solution using TiO2 nanoparticles prepared with various alcohols as solvent.

TiO2 nanoparticles were prepared with various linear alkyl chains of alcohols under a sol-gel process. The structure characterization and the photocatalytic reduction of hexavalent chromium of the TiO2 nanoparticles were investigated. The phase transformation temperature, crystal aggregation and surface area of prepared TiO2 samples were found to be strongly influenced by alcohol used. The phase transformation from anatase to rutile was retarded and the surface area was reduced for TiO2 prepared with alcohols of longer alkyl chain. TiO2 nanoparticles prepared with methanol or ethanol exhibited higher photocatalytic reduction activity of hexavalent chromium possibly due to greater and more positively charged surface area.

Decomposition of acetone by hydrogen peroxide/ozone process in a rotating packed contactor.

The direct use of ozone (O3) in water and wastewater treatment processes is found to be inefficient, incomplete, and limited by the ozone transfer between the gas-liquid interface because of its low solubility and instability in aqueous solutions. Therefore, rotating packed contactors were introduced to improve the transfer of ozone from the gaseous phase to the solution phase, and the effect of several reaction parameters were investigated on the temporal variations of acetone concentration in aqueous solution. The decomposition rate constant of acetone was enhanced by increasing the rotor speed from 450 to 1800 rpm. Increasing the hydrogen peroxide (H2O2)/O3 molar ratios accelerated the decomposition rate until a certain optimum H2O2/O3 molar ratio was reached; further addition of H2O2 inhibited the decomposition of acetone, possibly because excessive amounts of H2O2 added might serve as a scavenger to deplete hydroxyl free radicals.