Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0012739 (disseminated intravascular coagulation)
 8,673 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The antitumor agents 5-(3,3-dimethyl-1-triazenyl)imidazole-4-carboxamide (DIC) and 5-[3,3-bis(2-chloroethyl)-1-triazenyl]imidazole-4-carboxamide (BIC) are substrates for NADPH-requiring microsomal enzymes of mouse liver. The products of DIC oxidation are 5-aminoimidazole-4-carboxamide (AIC) and formaldehyde. Those for BIC are AIC and, presumably, 2-chloroacetaldehyde. For DIC, the reaction has a pH optimum of 9.0; and the Michaelis constant (Km) is 0.25 mM. At lower pH values, the Km is not greatly increased; but there is a sharp rise in the Km values above pH 9.0. For the enzyme-catalyzed production of AIC from BIC, the pH optimum is 7.5; the Km value for BIC is 0.47 mM. Of a variety of tissues tested for enzymatic activity, only liver accomplishes the conversion of DIC and BIC to AIC. Most of the activity in the liver is located in the microsomal fraction, although detectable activity is present in washed mitochondria. For liver microsomes, the rate of reaction for BIC is greater than that for DIC, but apparently neither rate is fast enough to allow extensive metabolism of large doses of these agents.
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PMID:Microsomal metabolism of triazenylimidazoles. 24 85

Single ip injections of 600 mg/kg 5-(3,3-dimethyl-1-triazeno)-imidazole-4-carboxamide (DIC) and 900 mg/kg 5-[3,3-bis(2-chlorethyl)-1-triazeno]-imidazole-4-carboxamide (BIC) were given to pregnant Wistar rats at day 12 and the animals were killed 4 h after injection and at days 13-17 of gestation. Fetal tissues were used to determine total DNA, RNA, and protein and the data used to derive cell number and cell weight, RNA, and protein/cell. Both compounds reduced total fetal body weight, DNA, RNA, and protein but reduction of RNA by BIC was not statistically significant. These effects were observed 4 h after injection, increased with age (days 13-17), and were 3-4 times greater for DIC than BIC. By using the value of 6.2 mumug DNA/cell, cell number and per-cell values for weight, RNA, and protein, and weight: DNA, RNA:DNA, and protein:DNA ratios were computed. The per-cell values and ratios in the DIC-exposed animals were 8-44% greater and in BIC-treated animals 0-11% greater than control animals of the same gestational age. Percentage of body water was the same in the experimental and control animals. The differences in DNA, RNA, and protein are believed to be related to drug-induced growth retardation incident to total fetal DNA reduction resulting in diminished cell number.
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PMID:Cellular and biochemical aspects of growth retardation in rat fetuses induced by maternal administration of selected anticancer agents. 119 32

In this article, we present a selective overview of some recent developments in Bayesian model and variable selection methods for high dimensional linear models. While most of the reviews in literature are based on conventional methods, we focus on recently developed methods, which have proven to be successful in dealing with high dimensional variable selection. First, we give a brief overview of the traditional model selection methods (viz. Mallow's Cp, AIC, BIC, DIC), followed by a discussion on some recently developed methods (viz. EBIC, regularization), which have occupied the minds of many statisticians. Then, we review high dimensional Bayesian methods with a particular emphasis on Bayesian regularization methods, which have been used extensively in recent years. We conclude by briefly addressing the asymptotic behaviors of Bayesian variable selection methods for high dimensional linear models under different regularity conditions.
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PMID:Bayesian Methods for High Dimensional Linear Models. 2451 33

Evidence accumulations models (EAMs) have become the dominant modeling framework within rapid decision-making, using choice response time distributions to make inferences about the underlying decision process. These models are often applied to empirical data as "measurement tools", with different theoretical accounts being contrasted within the framework of the model. Some method is then needed to decide between these competing theoretical accounts, as only assessing the models on their ability to fit trends in the empirical data ignores model flexibility, and therefore, creates a bias towards more flexible models. However, there is no objectively optimal method to select between models, with methods varying in both their computational tractability and theoretical basis. I provide a systematic comparison between nine different model selection methods using a popular EAM-the linear ballistic accumulator (LBA; Brown & Heathcote, Cognitive Psychology 57(3), 153-178 2008)-in a large-scale simulation study and the empirical data of Dutilh et al. (Psychonomic Bulletin and Review, 1-19 2018). I find that the "predictive accuracy" class of methods (i.e., the Akaike Information Criterion [AIC], the Deviance Information Criterion [DIC], and the Widely Applicable Information Criterion [WAIC]) make different inferences to the "Bayes factor" class of methods (i.e., the Bayesian Information Criterion [BIC], and Bayes factors) in many, but not all, instances, and that the simpler methods (i.e., AIC and BIC) make inferences that are highly consistent with their more complex counterparts. These findings suggest that researchers should be able to use simpler "parameter counting" methods when applying the LBA and be confident in their inferences, but that researchers need to carefully consider and justify the general class of model selection method that they use, as different classes of methods often result in different inferences.
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PMID:Assessing the practical differences between model selection methods in inferences about choice response time tasks. 3078 96