The Risk Function of Breast and Ovarian Cancers in the Avrami-Dobrzynski Cellular Phase-Transition Model.

Specifying the role of genetic mutations in cancer development is crucial for effective screening or targeted treatments for people with hereditary cancer predispositions. Our goal here is to find the relationship between a number of cancerogenic mutations and the probability of cancer induction over the lifetime of cancer patients. We believe that the Avrami-Dobrzynski biophysical model can be used to describe this mechanism. Therefore, clinical data from breast and ovarian cancer patients were used to validate this model of cancer induction, which is based on a purely physical concept of the phase-transition process with an analogy to the neoplastic transformation. The obtained values of model parameters established using clinical data confirm the hypothesis that the carcinogenic process strongly follows fractal dynamics. We found that the model's theoretical prediction and population clinical data slightly differed for patients with the age below 30 years old, and that might point to the existence of an ancillary protection mechanism against cancer development. Additionally, we reveal that the existing clinical data predict breast or ovarian cancers onset two years earlier for patients with BRCA1/2 mutations.

Biophysical Modeling of the Ionizing Radiation Influence on Cells Using the Stochastic (Monte Carlo) and Deterministic (Analytical) Approaches.

This review article describes our simplified biophysical model for the response of a group of cells to ionizing radiation. The model, which is a product of 10 years of studies, acts as (a) a comprehensive stochastic approach based on the Monte Carlo simulation with a probability tree and (b) the thereof derived detailed deterministic models describing the selected biophysical and radiobiological phenomena in an analytical manner. Specifically, the presented model describes effects such as the risk of neoplastic transformation of cells relative to the absorbed radiation dose, the dynamics of tumor development, the priming dose effect (also called the Raper-Yonezawa effect) based on the introduced adaptive response approach, and the bystander effect. The model is also modifiable depending on users' potential needs.

The radiation adaptive response and priming dose influence: the quantification of the Raper-Yonezawa effect and its three-parameter model for postradiation DNA lesions and mutations.

The priming dose effect, called also the Raper-Yonezawa effect or simply the Yonezawa effect, is a special case of the radiation adaptive response phenomenon (radioadaptation), which refers to: (a) faster repair of direct DNA lesions (damage), and (b) DNA mutation frequency reduction after irradiation, by applying a small priming (conditioning) dose prior to the high detrimental (challenging) one. This effect is observed in many (but not all) radiobiological experiments which present the reduction of lesion, mutation or even mortality frequency of the irradiated cells or species. Additionally, the multi-parameter model created by Dr. Yonezawa and collaborators tried to explain it theoretically based on experimental data on the mortality of mice with chronic internal irradiation. The presented paper proposes a new theoretical approach to understanding and explaining the priming dose effect: it starts from the radiation adaptive response theory and moves to the three-parameter model, separately for two previously mentioned situations: creation of fast (lesions) and delayed damage (mutations). The proposed biophysical model was applied to experimental data-lesions in human lymphocytes and chromosomal inversions in mice-and was shown to be able to predict the Yonezawa effect for future investigations. It was also found that the strongest radioadaptation is correlated with the weakest cellular radiosensitivity. Additional discussions were focussed on more general situations where many small priming doses are used.

Analysis of Indoor Radon Data Using Bayesian, Random Binning, and Maximum Entropy Methods.

Three statistical methods: Bayesian, randomized data binning and Maximum Entropy Method (MEM) are described and applied in the analysis of US radon data taken from the US registry. Two confounding factors-elevation of inhabited dwellings, and UVB (ultra-violet B) radiation exposure-were considered to be most correlated with the frequency of lung cancer occurrence. MEM was found to be particularly useful in extracting meaningful results from epidemiology data containing such confounding factors. In model testing, MEM proved to be more effective than the least-squares method (even via Bayesian analysis) or multi-parameter analysis, routinely applied in epidemiology. Our analysis of the available residential radon epidemiology data consistently demonstrates that the relative number of lung cancers decreases with increasing radon concentrations up to about 200 Bq/m3, also decreasing with increasing altitude at which inhabitants live. Correlation between UVB intensity and lung cancer has also been demonstrated.

Meta-analysis of thirty-two case-control and two ecological radon studies of lung cancer.

A re-analysis has been carried out of thirty-two case-control and two ecological studies concerning the influence of radon, a radioactive gas, on the risk of lung cancer. Three mathematically simplest dose-response relationships (models) were tested: constant (zero health effect), linear, and parabolic (linear-quadratic). Health effect end-points reported in the analysed studies are odds ratios or relative risk ratios, related either to morbidity or mortality. In our preliminary analysis, we show that the results of dose-response fitting are qualitatively (within uncertainties, given as error bars) the same, whichever of these health effect end-points are applied. Therefore, we deemed it reasonable to aggregate all response data into the so-called Relative Health Factor and jointly analysed such mixed data, to obtain better statistical power. In the second part of our analysis, robust Bayesian and classical methods of analysis were applied to this combined dataset. In this part of our analysis, we selected different subranges of radon concentrations. In view of substantial differences between the methodology used by the authors of case-control and ecological studies, the mathematical relationships (models) were applied mainly to the thirty-two case-control studies. The degree to which the two ecological studies, analysed separately, affect the overall results when combined with the thirty-two case-control studies, has also been evaluated. In all, as a result of our meta-analysis of the combined cohort, we conclude that the analysed data concerning radon concentrations below ~1000 Bq/m3 (~20 mSv/year of effective dose to the whole body) do not support the thesis that radon may be a cause of any statistically significant increase in lung cancer incidence.

Modeling of Irradiated Cell Transformation: Dose- and Time-Dependent Effects.

We report here on various biophysical aspects of irradiated cells, beginning with a phenomenological description of radiation-induced cancer cells. This description includes detrimental factors such as chromosomal aberrations, as well as beneficial factors, such as adaptive response. Also discussed here is the dose- and time-dependent evolution of cancer cells using a purely mathematical approach. The general dose-response shape, which is sigmoidal, is shown to be modified by such mechanisms as adaptive response or bystander effect. The many aspects of the sigmoid function, which most appropriately demonstrates the relationships among irradiated organisms, are discussed here as well. Finally, the balance equation is presented as the most general relationship for irradiated cell behavior.

Mechanism of phenol photodegradation in the presence of pure and modified-TiO2: A review.

In recent years, the application of heterogeneous photocatalytic water purification processes has gained wide attention due to its effectiveness in degrading and mineralizing the recalcitrant organic compounds as well as the possibility of utilizing the solar UV and visible-light spectrum. By far, titania has played a much larger role in this scenario compared to other semiconductor photocatalysts due to its costly effectiveness, inert nature and photostability. A substantial amount of research has focused on the enhancement of TiO(2) photocatalysis by modification with metal, non-metal and ion doping. This paper aims to review and summarize the recent works on the titanium dioxide (TiO(2)) photocatalytic oxidation of phenol and discusses various mechanisms of phenol photodegradation (indicating the intermediates products) and formation of OH radicals. Phenol degradation pathway in both systems, TiO(2)/UV and doped-TiO(2)/Vis, are described.