Cytochrome P450-mediated metabolism of N-(2-methoxyphenyl)-hydroxylamine, a human metabolite of the environmental pollutants and carcinogens o-anisidine and o-nitroanisole

N-(2-methoxyphenyl)hydroxylamine is a human metabolite of the industrial and environmental pollutants and bladder carcinogens 2-methoxyaniline (o-anisidine) and 2-methoxynitrobenzene (o-nitroanisole). Here, we investigated the ability of hepatic microsomes from rat and rabbit to metabolize this reactive compound. We found that N-(2-methoxyphenyl)hydroxylamine is metabolized by microsomes of both species mainly to o-aminophenol and a parent carcinogen, o-anisidine, whereas 2-methoxynitrosobenzene (o-nitrosoanisole) is formed as a minor metabolite. Another N-(2-methoxyphenyl)hydroxylamine metabolite, the exact structure of which has not been identified as yet, was generated by hepatic microsomes of rabbits, but its formation by those of rats was negligible. To evaluate the role of rat hepatic microsomal cytochromes P450 (CYP) in N-(2-methoxyphenyl)hydroxylamine metabolism, we investigated the modulation of its metabolism by specific inducers of these enzymes. The results of this study show that rat hepatic CYPs of a 1A subfamily and, to a lesser extent those of a 2B subfamily, catalyze N-(2-methoxyphenyl)hydroxylamine conversion to form both its reductive metabolite, o-anisidine, and o-aminophenol. CYP2E1 is the most efficient enzyme catalyzing conversion of N-(2-methoxyphenyl)hydroxylamine to o-aminophenol

Analysis of factors affecting apartment prices in Prague over the years 2006-2021

Bakalářská práce se zabývá vývojem pražského realitního trhu a faktory, které ho ovlivňovaly v letech 2006–2021. Teoretická část objasňuje a definuje samotný realitní trh, s ním související pojmy a jednotlivé faktory nejprve na poptávkové a následně na nabídkové straně. V praktické části je nejprve sledován vývoj poptávkových a nabídkových faktorů pomocí grafické analýzy. Grafická analýza se poté podrobně věnuje samotnému vývoji cen bytů v Praze. Následuje statistická analýza závislosti mezi ukazatelem cen bytů a ukazateli jednotlivých faktorů pomocí Pearsonova korelačního koeficientu. Výsledky odhalují, že mezi nejsilnější faktory patří disponibilní důchod, nezaměstnanost, růst populace a státní výdaje. Z výsledků je vyvozeno, jakým způsobem může státní aparát bojovat proti neudržitelně vysokým cenám bytů. Jako nejlepší způsob je identifikována podpora výstavby bytů.This bachelor’s thesis deals with the development of the Prague real estate market and the factors influencing it in the years 2006–2021. The theoretical part clarifies and defines the real estate market itself, terms related to it, and individual factors, first on the demand side and then on the supply side. In the practical part, the development of demand and supply factors is first monitored using graphical analysis. The graphic analysis is then devoted in detail to the development of apartment prices in Prague. The following is a statistical analysis of the dependence between the indicator of apartment prices and the indicators of individual factors using the Pearson correlation coefficient. The results reveal that disposable income, unemployment, population growth and government spending are among the strongest drivers of apartment prices. The results point to how the government can fight unsustainably high housing prices. Supporting the construction of apartment is identified as the best way

Advanced Statistical Analysis of 3D Kinect Data: A Comparison of the Classification Methods

This paper focuses on the statistical analysis of mimetic muscle rehabilitation after head and neck surgery causing facial paresis in patients after head and neck surgery. Our work deals with an evaluation problem of mimetic muscle rehabilitation that is observed by a Kinect stereo-vision camera. After a specific brain surgery, patients are often affected by face palsy, and rehabilitation to renew mimetic muscle innervation takes several months. It is important to be able to observe the rehabilitation process in an objective way. The most commonly used House–Brackmann (HB) scale is based on the clinician’s subjective opinion. This paper compares different methods of supervised learning classification that should be independent of the clinician’s opinion. We compare a parametric model (based on logistic regression), non-parametric model (based on random forests), and neural networks. The classification problem that we have studied combines a limited dataset (it contains only 122 measurements of 93 patients) of complex observations (each measurement consists of a collection of time curves) with an ordinal response variable. To balance the frequencies of the considered classes in our data set, we reclassified the samples from HB4 to HB3 and HB5 to HB6—it means that only four HB grades are used for classification algorithm. The parametric statistical model was found to be the most suitable thanks to its stability, tractability, and reasonable performance in terms of both accuracy and precision