Diseases are quite common in fish farms because of changes in physico-chemical characteristics in the aquatic environment, and operational concerns, i.e., overstocking and feeding issues. In the present study, potential factors (water physico-chemical characteristics and heavy metal load) on the disease-causing state of the pathogenic bacteria Lactococcus garvieae and Vagococcus sp. were examined with machine learning techniques in a trout farm. Recording of physico-chemical characteristics of the water, fish sampling and bacteria identification were carried out at bimonthly intervals. A dataset was generated from the physico-chemical characteristics of the water and the occurrence of bacteria in the trout samples. The eXtreme Gradient Boosting (XGBoost) algorithm was used to determine the most important independent variables within the generated dataset. The most important seven features affecting bacteria occurrence were determined. The model creation process continued with these seven features. Three well-known machine learning techniques (Support Vector Machine, Logistic Regression and Naïve Bayes) were used to model the dataset. Consequently, all the three models have produced comparable results, and Support Vector Machine (93.3% accuracy) had the highest accuracy. Monitoring changes in the aquaculture environment and detecting situations causing significant losses through machine learning techniques have a great potential to support sustainable production.

Background: This study aims to identify predictors of postoperative atrial fibrillation in coronary artery bypass grafting patients using routinely collected preoperative tests. Methods: Between January 2020 and December 2023, a total of 50 patients with postoperative atrial fibrillation (POAF group; 39 males, 11 females; mean age: 65.9±8.3 years; range, 38 to 77 years) and 50 without postoperative atrial fibrillation (non-POAF group; 41 males, 9 females; mean age: 61.8±10.0 years; range, 41 to 81 years) were randomly selected from a group of patients undergoing two or three-vessel coronary artery bypass grafting. We analyzed preoperative laboratory, demographic and intraoperative data using machine learning models. Results: The overall incidence of postoperative atrial fibrillation was 21.69%. The three most effective biomarkers were magnesium, total iron binding capacity, and albumin, respectively. A total of 2.0 mg/dL value of magnesium was identified as a threshold value. Magnesium values below 2.0 mg/dL were considered atrial fibrillation-positive, accounting for 25% of the dataset. Total iron binding capacity values higher than 442 µg/dL were considered atrial fibrillation-positive, accounting for 12% of the dataset. The threshold value for albumin was 29 g/dL, and patients with values under this value were considered atrial fibrillation-positive, accounting for 4% of the dataset. Conclusion: Machine learning models demonstrate encouraging results in identifying risk factors for many entities. It is of utmost importance to establish a ranking among risk factors and determine threshold values to support clinicians in decision making. This is our first experience with machine learning in this patient group after cardiac surgery. Further studies are warranted to confirm these data.

This study was aimed to determine the current antibiotic resistance of Lactococcus garvieae, which causes significant economic losses in rainbow trout (Oncorhynchus mykiss) farms. For the study, three farms located around a stream were visited in two consecutive disease outbreaks. Bacteria were isolated from the diseased fish and all bacterial isolates were identified using conventional and molecular methods. Then, they were examined for 5 different antibiotic resistance genes and their antibiotic susceptibility profiles were determined using the disc diffusion technique. The results of the study revealed that all 16 isolates had a different antibiotic resistance profile and each should be considered as a separate strain. Additionally, they showed that there may be different L. garvieae strains within and between farms, using the same water source. Comparison of the results with previous studies from the region and different regions of the country revealed that antibiotic resistance is increasing in L. garvieae, and screenings on a limited number of isolates may not give an accurate picture of the final status of antibiotic resistance. These outcomes of the study are discussed in terms of treatment strategies that trout farms in the region should follow when treating lactococcosis.

Disease outbreaks negatively affect fish production. Antimicrobial agents used in the treatment of diseases become ineffective over time because of antibiotic resistance developed by bacteria distributed in the aquaculture environment. This study was conducted for 4 months (cold period) in a fish farm to detect the fish disease, cold water streptococcosis. In the study, four brood stock showing disease signs were detected. Bacteria isolates were obtained and identified as Vagococcus salmoninarum. Antimicrobial susceptibility of V. salmoninarum was tested and antibiotic resistance gene profiles of V. salmoninarum isolates were screened. The phylogenetic relation of the isolates with the previously reported strains was evaluated. Antibiotic resistance developed by pathogenic bacteria is distributed in the aquaculture environment. The transfer of resistance genes from one bacterium to another is very common. This situation causes the antimicrobial agents used in the treatment of diseases to become ineffective over time. The disc diffusion test showed that all four isolates developed resistance to 13 (FFC30, AX25, C30, E15, CF30, L2, OX1, S10, T30, CRO30, CC2, PT15 and TY15) of the evaluated antibiotics and were about to develop resistance to six others (AM 10, FM 300, CFP75, SXT25, APR15 and TE30). Furthermore, antibiotic resistance genes tetA, sul1, sul2, sul3, dhfr1, ereB and floR were detected in the isolated strain. Moreover, the phylogenetic analysis showed that isolated V. salmoninarum strain (ESN1) was closely related to the bacterial strains isolated from USA and Jura.