## 4 Practical Examples  
As precisely described in previous sections, a living system's homeostasis is the result of a jostling process between positive (hormetic) and negative (detrimental) actions. This can create the regular fluctuations around the homeostatic equilibrium [1; 12], sometimes spread over many generations [16; 17], which can be described by the presented simplified model.  
There are several experimental findings which are consistent with the proposed model on a very general level. Generally, many low-dose radiation studies show that the linearity in the dose-response function is broken [5], while the linear no-threshold (LNT) model can be applied to medium and high doses only. This suggests that some form of equilibrium exists between radiation's harmful effects and the organism's response (e.g., adaptive response) in the low-dose region--so this is the case which is described by the presented model. This homeostatic equilibrium is represented by the plateau (like in Figure 5 or Figure 6) in the radiation risk function in the low-dose range, which is widely discussed in the textbook by [PERSON] [5]. In other words: the equilibrium (homeostasis) is maintained for low doses, because the external stressor (here: the ionizing radiation) is too weak to disturb its balance. However, a high external stressor can simply destroy the equilibrium (homeostasis), which is usually described by the linear (LNT) model in a dose-response risk assessment function used, e.g., in radiation protection worldwide.  
One has to note that Equation (11) and Figure 5 represent an infinite number of solutions for the simple homeostatic balance condition \(P_{C}=P_{H}\) (Equation (1)). Moreover, the ratio from the left-hand side of Equation (10) does not need to be a constant value to keep the balance condition valid. This means that the beneficial or detrimental model's strength is just one, but not the only, condition for radiation risk assessment.  
This process can be presented in practice on widely scattered dose-response data of radioactive gas radon influence on the risk of lung cancer. As one can see in Figure 7, the radon risk values are distributed unsymmetrically, with a maximal value around \(1\) (which means no risk). This means that most of the collected results show homeostatic behavior of the organism(s).  
An analogical situation can be observed when epidemiological data on the dose-response relationship collected in the textbook of [PERSON] [5] are presented in the analogical plot (see Figure 8). This time, the data were spread over all types of cancers, because generally, the model does not focus on one type of cancer, but can be applied wider.  
Other examples cover the process of cellular carcinogenesis as a rapid phase transition process [18], or the quasi-Gaussian distributions of individual radiosensitivity, which is strictly connected with the strength of the organism's response [19; 20; 21], but the detailed explanation of this can be found in the literature [22].  
Figure 8: The distribution of 139 data points from 72 independent epidemiological studies on all types of cancer risk among people irradiated by low doses of ionizing radiation, collected by [PERSON] [5]. The average risk equals 0.92 \(\pm\) 0.21.  
Figure 7: Lung cancer risk distribution of 112 data points from 28 independent radon studies [23]. The average risk equals 0.98 \(\pm\) 0.01.