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    Different machine learning methods based prediction of mild cognitive impairment
    (2020) Doganer, Adem; Yaman, Selma; Eser, Nadire; Ozcan Metin, Tugba
    Aim: In this study benefits from different machine learning methods to analyze factors which affect young person’s scores of cognitive assessment. Material and Methods: This study was performed among 144 persons aged between 18 and 24 who study at Kahramanmaras Sutcu Imam University. Boosted Tree Regression (BTR), Random Forest Regression (RFR) and Support Vector Machine (SVM), which are among machine learning methods, were used in order to determine the factors affecting the score of cognitive assessment. K-10 fold cross validation method was also used. Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Correlation coefficients (R) metrics were used in order to measure prediction performances of machine learning methods.Results: MSE values were calculated as 9.66 for BTR, 9.78 for RFR, and 6.43 for SVM. MAE values were calculated as 2.06 for BTR, 2.05 for RFR, and 1.97 for SVM. RMSE values were calculated as 3.10 for BTR, 3.12 for RFR, and 2.53 for SVM. Finally, correlation coefficients were calculated as 0.289 for BTR, 0.371 for RFR and 0.546 for SVM. In addition, it was also found out that the most important variables which affected the scores of cognitive assessment were anti-depressant use, depression and obsession.Conclusion: It was demonstrated in this study that SVM displayed the lowest error rates and highest prediction performance in terms of determining the score of cognitive assessment. Therefore, SVM can be stated that it is the most suitable method for the prediction of cognitive impairment.
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    Evaluation of the effects of tadalafil on pain response in thermal plantar and dynamic plantar tests in rats
    (2020) Altintas Aykan, Duygun; Yaman, Selma
    Aim: Nitric oxide and its promoters, phosphodiesterase-related agents, have been demonstrated to have pivotal roles in pain modulation. Phosphodiesterase-5 (PDE5) inhibitors are important to enhance the effect of endogenously released nitric oxid. In this study, we aimed to evaluate the tadalafil efficacy, a PDE5 inhibitor, in central nociception models in rats.Material and Methods: Thirty-six rats were divided into six treatment groups. Mechanic plantar aesthesiometer and thermal plantar tests were employed to measure the pain thresholds to the mechanical and thermal stimulations. Correlations between the tadalafil doses, durations and behavioral pain responses were recorded, and compared with that of diclofenac, a nonsteroidal anti-inflammatory drug.Results: Tadalafil 1 and 10 mg/kg single doses; and tadalafil 1 and 10 mg/kg for 7 days exerted significant antinociceptive effects on the mechanic plantar aesthesiometer. However, tadalafildid not reveal significant amelioration in pain responses on the thermal plantar test.Tadalafil 1 mg/kg caused an insignificant amelioration in thermal latencies and withdrawal thresholds in comparison to 10 mg/kg doses.Conclusion: Our findings indicated that nociceptive effect of tadalafil due to thermal stimulation involves cyclic guanosine monophosphate (cGMP), while in mechanic hyperalgesiac GMP may not have a basic role in the primary sensory neurons sensitization. The increase in latencies and withdrawal thresholds with low dose tadalafil was remarkabl.