Kimcrabtree6910

RESULTS Kidney damage induced by CIS was confirmed by the increase of creatinine, urea and uric acid levels in the blood of juvenile rats. The renal oxidative disturbance was characterized by an increase in the levels of thiobarbituric acid reactive substances (TBARS), protein carbonyl, and nitrogen oxides (Nox), as well as the decrease in non-protein thiol content (NPSH), glutathione-S-transferase (GST), catalase (CAT) and superoxide dismutase (SOD) activities. CIS inhibited the activities of δ-aminolevulinic acid dehydratase (δ-ALA-D) and Na+, K+-ATPase and down-regulated the Nrf2/Keap-1/HO-1 pathway in the kidney of juvenile rats. CONCLUSION Both Ebselen and (PhSe)2 modulated back to the normal levels all parameters altered by the CIS administration in the kidney of juvenile rats. Thus, this study shows that (PhSe)2 was as effective as Ebselen in protecting the kidney against oxidative damage caused by CIS in rats. PURPOSE To design and evaluate a self-trainable natural language processing (NLP)-based procedure to classify unstructured radiology reports. The method enabling the generation of curated datasets is exemplified on CT pulmonary angiogram (CTPA) reports. METHOD We extracted the impressions of CTPA reports created at our institution from 2016 to 2018 (n = 4397; language German). The status (pulmonary embolism yes/no) was manually labelled for all exams. Data from 2016/2017 (n = 2801) served as a ground truth to train three NLP architectures that only require a subset of reference datasets for training to be operative. The three architectures were as follows a convolutional neural network (CNN), a support vector machine (SVM) and a random forest (RF) classifier. Impressions of 2018 (n = 1377) were kept aside and used for general performance measurements. Furthermore, we investigated the dependence of classification performance on the amount of training data with multiple simulations. RESULTS The classification performance of all three models was excellent (accuracies 97 %-99 %; F1 scores 0.88-0.97; AUCs 0.993-0.997). Highest accuracy was reached by the CNN with 99.1 % (95 % CI 98.5-99.6 %). Training with 470 labelled impressions was sufficient to reach an accuracy of > 93 % with all three NLP architectures. CONCLUSION Our NLP-based approaches allow for an automated and highly accurate retrospective classification of CTPA reports with manageable effort solely using unstructured impression sections. click here We demonstrated that this approach is useful for the classification of radiology reports not written in English. Moreover, excellent classification performance is achieved at relatively small training set sizes. Nitrate reductase is a nitric oxide (NO) induced enzyme in plants, NO acts as a signaling molecule under ZnO NPs-induced stress whereas melatonin (N-acetyl-5-methoxytryptamine) could improve morpho-physiological attributes of plants under adverse conditions. In present study, seedlings of two rice genotypes differed regarding nitrate reductase activities i.e., transgenic 'NR' and wild type 'WT' were applied with two melatonin levels i.e., 0, 10 μΜ regarded as M0, M10, respectively and three levels of ZnO NPs i.e., 0, 50, 500 mg L-1 regarded as ZnO NPs0, ZnO NPs50 and ZnO NPs500, respectively. Results revealed that melatonin application substantially increased the dry biomass accumulation of both rice genotypes under all ZnO NPs levels. The root growth, mineral absorption as well as the antioxidant responses were also improved by melatonin application under ZnO NPs stress. The interactive effects of melatonin and genotype on plant growth, antioxidant responses and mineral contents i.e., Zn, Na, Fe and Mn were also found significant under different ZnO NPs stress. Furthermore, total plant dry weight was significantly correlated with the leaf dry weight, root volume, catalase (CAT) activity in leaves, Na accumulation in stem sheath and Fe accumulation in root under both M0 and M10 treatments. Moreover, the comparative transcriptome analysis identified key genes which were responsible for melatonin and NO-induced modulations in plant growth under ZnO NPs stress. Overall, melatonin could improve the morphological growth of the rice plants by modulating root-shoot characteristics, antioxidant activities and mineral uptake in root and shoot of rice. Human exposure to formaldehyde, toluene, xylene (FTX) and other volatile organic compounds (VOCs) are associated with negative health impact. To characterize the exposure and health effects of FTX and TVOC from indoor environments, we conducted an extensive monitoring campaign involving 1278 measurements of 472 indoor locations in Harbin, a megacity in China from May 2013 to March 2018. The results showed that household had the highest mean formaldehyde concentration (0.171 ± 0.084 mg m-3) among all types of indoor environments. Meanwhile, there was no significant differences in formaldehyde concentration of the living room, master bedroom, secondary bedroom and study room (p > 0.05), as well as toluene and xylene. The highest mean concentration of toluene, xylene and TVOC was measured in public bath center. Great difference was found between formaldehyde concentrations in 2013 and other years, except 2015. There were great positive nonlinear correlations between the indoor temperature and concentration of formaldehyde (p  less then  0.01), good negative nonlinear correlations between the finish time of decoration and concentration of formaldehyde (p  less then  0.01), good positive linear correlations between the relative humidity and concentration of formaldehyde (p  less then  0.01). A risk assessment methodology was utilized to evaluate the potential adverse health effects of the individual FTX compounds according to their carcinogenicities. The predicted carcinogenic risk of formaldehyde was greater than the threshold value 1E-06 at all environments. The non-carcinogenic risk of TX compounds in the population is negligible. For estimating human health risk exposure, sensitivity analysis showed that more attention should be given to the influential variables such as the level of pollutants.