Determination of the chemical composition of tea by chromatographic methods: a review

Despite the fact that mankind has been drinking tea for more than 5000 years, its chemical composition has been studied only in recent decades. These studies are primarily carried out using chromatographic methods. This review summarizes the latest information regarding the chemical composition of different tea grades by different chromatographic methods, which has not previously been reviewed in the same scope. Over the last 40 years, the qualitative and quantitative analyses of high volatile compounds were determined by GC and GC/MS. The main components responsible for aroma of green and black tea were revealed, and the low volatile compounds basically were determined by HPLC and LC/MS methods. Most studies focusing on the determination of catechins and caffeine in various teas (green, oolong, black and pu-erh) involved HPLC analysis. Knowledge of tea chemical composition helps in assessing its quality on the one hand, and helps to monitor and manage its growing, processing, and storage conditions on the other. In particular, this knowledge has enabled to establish the relationships between the chemical composition of tea and its properties by identifying the tea constituents which determine its aroma and taste. Therefore, assessment of tea quality does not only rely on subjective organoleptic evaluation, but also on objective physical and chemical methods, with extra determination of tea components most beneficial to human health. With this knowledge, the nutritional value of tea may be increased, and tea quality improved by providing via optimization of the growing, processing, and storage conditions.</p

Cancer rates over age, time, and place: insights from stochastic models of heterogeneous populations

Individuals at the same age in the same population differ along numerous risk factors that affect their chances of various causes of death. The frail and susceptible tend to die first. This differential selection may partially account for some of the puzzles in cancer epidemiology, including the lack of apparent progress in reducing cancer incidence and mortality rates over time. (AUTHORS)

Economic progress as cancer risk factor. II: Why is overall cancer risk higher in more developed countries?

Analysis of data on cancer incidence rates in different countries at different time periods revealed positive association between overall cancer risk and economic progress. Typical explanations of this phenomenon involve improved cancer diagnostics and elevated exposure to carcinogens in industrial countries. Here we provide evidence from human and experimental animal studies suggesting that some other factors associated with high economic development and Western life style may primarily increase the proportion of susceptible to cancer individuals in a population and thus contribute to elevated cancer risks in industrial countries. These factors include (but not limited to): (i) better medical and living conditions that “relax” environmental selection and increase share of individuals prone to chronic inflammation; (ii) several medicines and foods that are not carcinogenic themselves but affect the metabolism of established carcinogens; (iii) nutrition enriched with growth factors; (iv) delayed childbirth. The latter two factors may favor an increase in both cancer incidence rate and longevity in a population. This implies the presence of a trade-off between cancer and aging: factors that postpone aging may simultaneously enhance organism’s susceptibility to several cancers. Key words: cancer risk, individual susceptibility, economic progress, aging

A Bayesian correlated frailty model applied to Swedish breast cancer data

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Unobserved heterogeneity in a model with cure fraction applied to breast cancer

We suggest a cure-mixture model to analyze bivariate time-to-event data, as motivated by the paper of Chatterjee and Shih (2001, Biometrics 57, 779 - 786), but with a simpler estimation procedure and the correlated gamma-frailty model instead of the shared gamma-frailty model. This approach allows us to deal with left truncated and right censored lifetime data and accounts for heterogeneity as well as for an insusceptible (cure) fraction in the study population. We perform a simulation study to evaluate the properties of the estimates in the proposed model and apply it to breast cancer incidence data for 5,857 Swedish female monozygotic and dizygotic twin pairs from the so-called old cohort of the Swedish Twin Registry. This model is used to estimate the size of the susceptible fraction and the correlation between the frailties of the twin partners. Possible extensions, advantages and limitations of the proposed method are discussed.Sweden, breast, cancer, correlation, survival, twins

Modeling of immune life history and body growth: the role of antigen burden

In this paper, a recently developed mathematical model of age related changes in population of peripheral T cells (Romanyukha, Yashin, 2003) is used to describe ontogenetic changes of the immune system. The treatise is based on the assumption of linear dependence of antigen load from basal metabolic rate, which, in turn, depends on body mass following the allometric relationship – 3/4 power scaling law (Kleiber, 1932; West, Brown, 2005). Energy cost of antigen burden, i.e. the energy needed to produce and maintain immune cells plus the energy loss due to infectious diseases, is estimated and used as a measure of the immune system effectiveness. The dependence of optimal resource allocation from the parameters of antigen load is studied.

Modifications of the EM algorithm for survival influenced by an unobserved stochastic process

AbstractLet Y=(Yt)t≥0) be an unobserved random process which influences the distribution of a random variable T which can be interpreted as the time to failure. When a conditional hazard rate corresponding to T is a quadratic function of covariates, Y, the marginal survival function may be represented by the first two moments of the conditional distribution of Y among survivors. Such a representation may not have an explicit parametric form. This makes it difficult to use standard maximum likelihood procedures to estimate parameters - especially for censored survival data. In this paper a generalization of the EM algorithm for survival problems with unobserved, stochastically changing covariates is suggested. It is shown that, for a general model of the stochastic failure model, the smoothing estimates of the first two moments of Y are of a specific form which facilitates the EM type calculations. Properties of the algorithm are discussed