Objective: The attention deficit hyperactivity disorder has a negative impact on the child’s educational life and relationships with the social environment during childhood and adolescence. The connection between temperament traits and The attention deficit hyperactivity disorder has been proven by various studies. As far as we know, there is no machine learning study to diagnose. The attention deficit hyperactivity disorder in a dataset created using temperament characteristics. Methods: Machine learning-based semi-automatic/fully automatic expert decision support systems are frequently used for the diagnosis of various diseases. In this study, it was aimed to reveal the success of a semi-automatic expert decision support system in the diagnosis of attention deficit hyperactivity disorder by using temperament characteristics. The high classification success achieved is a resource for a potential diagnosis of attention deficit hyperactivity disorder expert decision support system. In this respect, this study includes original qualities and innovations. Results: Many different deep learning methods were used in the research. Deep learning methods are models that achieve high success by using a large number of images in various image processing competitions. The images of the signals in the data set were first obtained by Continuous Wavelet Transform. The highest classification success in our data set was obtained with the Squeeze Net model with 88.33%. Conclusion The model we propose shows that an automatic system based on artificial intelligence can be created, as well as revealing the relationship between temperament characteristics in the diagnosis of attention deficit hyperactivity in the data set we created.

The aim of this study was to evaluate the synergistic potentiation effect of ineffective doses of dexmedetomidine on antinociception induced by morphine and fentanyl in acute pain model in rats. Seventy albino Wistar rats were separated into 7 groups. Data for the control and sham groups were recorded. The ineffective dose of dexmedetomidine was investigated and found to be 3 micro g/kg. Each group was administered the following medications: 3 mg/kg morphine (intraperitoneal) to Group 3, 5 microg/kg fentanyl (intraperitoneal) to Group 4, dexmedetomidine 3 micro g/kg (subcutaneously) to Group 5, dexmedetomidine 3 microg/kg (subcutaneous)+3 mg/kg morphine (intraperitoneal) to Group 6 and finally 3 microg/kg dexmedetomidine (subcutaneous)+5 microg/kg fentanyl (intraperitoneal) to Group 7. Just before the application and 15, 30, 60, 90 and 120 min after the administration of medication, two measurements of tail flick (TF) and hot plate (HP) tests were performed. The averages of the measurements were recorded. TF and HP latencies were the main outcomes. The analgesic effect of the combinations with dexmedetomidine+morphine (Group 6) and dexmedetomidine+fentanyl (Group 7), compared to the analgesic effect of morphine alone and fentanyl alone was significantly higher at 15, 30, 60 and 90 minutes after administration. In this study, dexmedetomidine in ineffective doses, when combined with morphine and fentanyl, potentiates the effects of both morphine and fentanyl.
Acute Pain* ; Animals ; Dexmedetomidine* ; Fentanyl* ; Morphine* ; Rats ; Rats, Wistar