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Quercetin alleviates cadmium chloride-induced kidney damage in rats by suppressing endoplasmic reticulum stress through SIRT1-dependent deacetylation regarding Xbp-1s and eIF2α.
Data from National Health and Nutrition Examination Survey for 2003-2016 for US adults aged ≥ 20 years (N = 10,942) were used to study variabilities and associations with urinary albumin creatinine ratio (UACR) in the adjusted concentrations (AGM) of urine bisphenol A (BPA) across various stages of renal function (RF). RF stages considered were RF-1 (eGFR > 90 mL/min/1.73 m2), RF-2 (60 ≤ eGFR ≤ 90 mL/min/1.73 m2), RF-3A (45 ≤ eGFR less then  60 mL/min/1.73 m2), and RF-3B/4 (15 ≤ eGFR less then  45 mL/min/1.73 m2). Irrespective of gender, race/ethnicity, and smoking status, AGMs for BPA were located on U-curves with point of inflection at RF-2. In general, decreases from RF-1 to RF-2 were followed by increases from RF-2 to RF-3A and from RF-3A to RF-3B/4. For example, AGMs for males were observed to be 1.52, 1.48, 1.61, and 1.69 ng/mL at RF-1, RF-2, RF-3A, and RF-3B/4 respectively. A similar U-curve was observed for those without albuminuria but for those with albuminuria, BPA levels continued increasing until RF-3A before decreasing at RF-3B/4. Severe kidney dysfunction was found to be associated with statistically significantly higher concentrations of BPA in urine. Shape of concentration curves for BPA across RF stages is determined by the balance of actively mediated secretion and reabsorption operating on both sides of renal proximal tubules during each stage of RF. Shape of concentration curves for BPA across various stages of RF was age and concentration dependent. Associations between BPA and UACR were found to be negative (p = 0.02), positive (p = 0.23), negative (p = 0.53), and negative (p  less then  0.01) respectively at RF-1, RF-2, RF-3A, and RF-3B/4 respectively.This study aims to decompose the changes in CO2 emission, in 1990-1995 and 2010-2015, to identify the main drivers of those changes at the sectoral level. Using energy input and input-output table, the emission changes were decomposed into six factors energy intensity, carbonization factor, technology, structural demand, consumption pattern effect, and scale effect. This model would allow a given country to identify the impacts of energy consumption, energy mix, and production efficiency (as direct sources of emission) while paying close attention to their linkages with both the economic structure and the accretion of final demand. This research was the first attempt to decompose CO2 emission changes using the input-output framework in Indonesia. The results revealed that the scale effect was the main driving factor of emission in 1990-1995. In 2010-2015, embodied emission in the chemical industry, mining, rubber, and plastic industry significantly rose due to an increase in energy intensity. Meanwhile, the embodied emission in electricity, road and air transportations, and non-metallic mineral production rose due to inefficiency in production. The energy policy to improve efficiency and diversified primary energy seemed to be negative between 2010 and 2015. Consequently, the embodied emission from energy intensity factor in several energy-intensive sectors increased along with a lack of contribution to changes in primary energy composition. This study also demonstrated that there was a decline in structural demand for electricity and mining between 2010 and 2015, which contributed negatively to the emission and yet outdrew with the rise from scale effect.Air pollutant concentration of Trivandrum, the capital of Kerala, exceeded the limits of National Ambient Air Quality (NAAQ) standards, according to a study conducted in 2015 by NATPAC. These polluted corridors harbour vegetation on roadsides and traffic islands, planted solely for aesthetic appeal. Analysis of air pollution tolerance levels of existing plants can act as a scientific basis for efficient planning of the urban landscape. Sixty-seven species, including flowering, fruit-bearing, ornamental, shade-providing and timber-yielding species, were screened for their relative resistance to air pollution. Based on leaf pH, relative water content, chlorophyll and ascorbic acid levels, the Air Pollution Tolerance Indices (APTI) of each species were formulated and they were grouped into the following tolerant, moderately tolerant, intermediate and sensitive groups. Agave americana (18.40), Cassia roxburghii (17.63), Anacardium occidentale (11.97), Cassia fistula (11.60), Mangifera indica (11.59) and Saraca asoca (10.88) may be considered for planting near green spaces like roundabouts and near pollution prone industrial areas, as they belong to tolerant category. Comparison of APTI during summer and monsoon also revealed the stability of Agave americana, Saraca asoca, Ficus benghalensis, Peltophorum pterocarpum, Ficus elastica, Ixora finlaysoniana, Mangifera indica, Canna indica and Delonix regia in maintaining pollution tolerance even during water disparity. BP-1-102 cost Agave americana, Anacardium occidentale, Ficus elastica, Mangifera indica, Syzygium cumini, Ficus benghalensis, Nerium oleander and Ficus benjamina were found to be suited for mass planting, as was evident from their Anticipated Performance Indices (API).Studying relationships between meteorological conditions and respiratory virus infections may help interpret the causality of disease outbreaks and provide a better understanding of the seasonal distribution of viruses. Therefore, in this study, we analyzed the correlations between meteorological data and the trends of infection by human parainfluenza virus-1 (HPIV-1; also known as human respirovirus 1), human parainfluenza virus-2 (human orthorubulavirus 2), and human parainfluenza virus-3 (human respirovirus 3) using 9010 viral samples collected at Dankook University Hospital from January 1, 2012, to December 31, 2018. Infection frequency data were used to detect the seasonal patterns of HPIV-1, HPIV-2, and HPIV-3 infections, and these patterns were compared with local weather data over the same period. We performed descriptive statistical analysis, frequency analysis, t test, and binomial logistic regression analysis to examine the relationships of weather and particulate matter conditions with the incidence of HPIV-1, HPIV-2, and HPIV-3 infections.
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