## 4 Discussion
This behaviour can be attributed to the consideration of free ion activity and is significantly enhanced in the MixTOX tool when incorporating deviations from both models. Our findings are in agreement with the observations of [PERSON] et al. [52], who reported similar joint toxic effects of Cd and Zn mixtures in their reference model. Furthermore, [PERSON] et al. [75] investigated mixtures of Ni and Co in the soil-dwelling annelid _Enchytraus crypticus_ and reported a shift from synergism at low concentrations to antagonism at high concentrations, indicating that the effects depend on metal uptake and elimination processes [76]. We encountered difficulty in distinguishing between the CA- and IA-based models, as both models performed comparably well in assessing the overall toxicity of 1a, 1b, 1c, and 1d mixtures. This observation is consistent with the findings of [PERSON] and [PERSON] [77] and [PERSON] et al. [73], who reported similar results, indicating that neither the CA nor the IA model showed significant superiority over the other. As a result, researchers are often faced with the challenge of decisively selecting or favouring one reference model for mixture toxicity over the other. Their toxicity often follows a dose-response curve, where low concentrations may be tolerable or even essential (as trace nutrients), while higher concentrations become toxic. Additionally, biological systems have the capacity to detoxify and excrete metals up to a certain limit. Once this threshold is exceeded, toxic effects become more pronounced. The type of interaction can shift from being tolerable or beneficial at low concentrations to being harmful at high concentrations. In our test model system, the response closely matched that of the CA model, making it difficult to distinguish the joint toxicity results from those predicted by the IA model. The difficulty arises from the unknown action of the chemicals; they may have a similar mode of action or different specific modes of action, contributing to the challenge of model selection. No prior total phenolic content (TPC) studies have been conducted on ciliate species. Before this study, numerous phenolic compounds demonstrated effective antioxidant properties in various organisms, including fungi, bacteria, various plant species, marine algae, and natural products, among others. Previous research has highlighted the antioxidant activity of marine algae extracts, particularly their polyphenols [78]. Additionally, phenolic content plays a crucial role in an organism's ability to withstand various environmental factors or stresses by acting as a component of the cell's antioxidant defence system. This aspect is particularly important for plants, fungi, and protozoa due to their hydroxyl radical scavenging activity. The presence of HMs and NPs can lead to the generation of reactive oxygen species (ROS) in _E. aeticulatus_, either directly or indirectly. This oxidative stress causes damage to the cells, prompting a strong antioxidant response. The response includes the activation of transcription factors, increased production of antioxidant and antioxidant enzymes, synthesis of metal-binding proteins (metallothioneeins and phytochemicals), and the activation of repair mechanisms to counteract oxidative damage. These defence mechanisms collectively assist the organism in managing and neutralizing the toxic effects of HMs and NPs. Thus, the defence mechanisms help the ciliate _E. aeticulatus_ cope with an increase in the production of antioxidants. Therefore, in our experiment, we evaluated the total phenolic content of _E. aeticulatus_ cell extracts to understand their antioxidant activity against different concentrations of HM and NP stresses, using a series of antioxidant assays. DPPH scavenging activity was assessed for both single and bimetallic mixtures at acute and chronic exposures.