The pharmacokinetic of enrofloxacin was investigated in brown trout (Salmo trutta) following oral administration of 10, 20, and 40 mg/kg doses at 11 ± 1.5 °C. Furthermore, MICs of enrofloxacin against Aeromonas hydrophila and A. sobria were determined. The plasma concentrations of enrofloxacin and ciprofloxacin were determined using HPLC–UV and analyzed by non-compartmental method. Following oral administration at dose of 10 mg/kg, total clearance (CL/F), area under the concentration–time curve (AUC_0−∞) and peak plasma concentrations (C_max) were 41.32 mL/h/kg, 242.02 h*μg/mL and 4.63 μg/mL, respectively. When compared to 10 mg/kg dose, the dose-normalized AUC_0–∞ and C_max were increased by 56.30% and 30.08%, respectively, while CL/F decreased by 38.4% at 40 mg/kg dose, suggesting the non-linearity. Ciprofloxacin was not detected in the all of plasma samples. The MIC values of enrofloxacin were ranged 0.0625–4 μg/mL for A. hydrophila and 0.0625–2 μg/mL for A. sobria. The oral administration of enrofloxacin at 10 (for 192 h) and 20 (for 240 h) mg/kg doses provided the AUC of enrofloxacin equal to 1.23 and 1.96-fold MICs, respectively, for A. hydrophila and A. sobria with the MIC₉₀ values of 1 µg/mL. However, further researches are needed on the PK/PD study of enrofloxacin for the successful treatment of infections caused by A. hydrophila and A. sobria in brown trout.

Traditional water quality classification methods rely on fixed threshold values, which limits their ability to reflect the degree of deviation from these boundaries. This rigid approach often results in uncertainties when assessing the ecological status of rivers. Fuzzy logic, in contrast, provides a more flexible framework by incorporating gradual transitions between classes and accounting for the relative importance of parameters. In this study, a fuzzy logic-based classification system was developed to evaluate the water quality of the Gökırmak River (Türkiye) and was compared with the conventional water quality index defined by national standards. Ten physicochemical parameters (temperature, pH, dissolved oxygen, electrical conductivity, nitrate, nitrite, ammonium, phosphate, biochemical oxygen demand, and chemical oxygen demand) were monitored monthly at six stations for one year. The fuzzy logic model was constructed using triangular membership functions and a Mamdani inference system. Model performance was assessed by comparing fuzzy classification results with expert evaluations based on the Surface Water Regulation. The system achieved 90% agreement, calculated as the ratio of consistent classifications to the total number of cases, demonstrating that fuzzy logic can serve as a reliable tool in water quality assessment. The findings highlight that fuzzy logic-based approaches not only reduce classification uncertainties but also provide a decision support framework for sustainable water resource management. Further research should expand the dataset across longer time periods and incorporate retrospective records to improve generalizability.

This study aimed to develop an artificial neural network (ANN) model to estimate the nitrate content in the surface water of the Gökırmak River. Samplings were carried out during 12 months from six stations between 2020 and 2021. Nitrate content varied between 0.20 and 2.70 mg l-1 while the mean value was 1.18 mg l-1 during the study period. The developed model consists of two input layers (month and station) and one output layer (nitrate content). Feed-forward backprop was used as the network type. Levenberg-Marquardt (TRAINLM) was used as a training function, LEARNGDM was used as an adaption learning function and mean squared error (MSE) was used as a performance function. The number of neurons was 10 and TANSIG was selected as transfer function. Epoch number adjusted 1000 iterations. ANN model predicted the nitrate content between 0.24 and 2.61 with a mean value of 1.16 mg l-1. The results showed that the best validation performance is 0.61264 at epoch 30. R values are 0.96257 and 0.84231 for training and testing, respectively. R-value was found 0.85352 for all data. In conclusion, this study presents the conception of an artificial neural network (ANN) model designed to predict nitrate concentrations in river water. The developed ANN model provides reasonable results for predicting the nitrate content using only given time and location inputs. More inputs can be included in future studies to ensure higher accuracy in the development of ANN models.