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    Assessment of quantitative zonal parameters of prostate gland in discrimination of normal, benign, and malignant conditions: are these the more reliable parameters in the diagnosis of prostate cancer?
    (Verduci Publisher, 2023) Karaca, L.; Ozdemir, Z. M.; Kahraman, A.; Celik, H.; Kaya, S.
    OBJECTIVE: Prostate cancer diagnosis and treatment are increasing in current public healthcare programs. An improved resolution multiparametric magnetic resonance imaging (MRI) has shown the potential to enhance the detection and differentiation of this medical condition. In this study, MR perfusion parameters were investigated in different ages and diseases to differentiate clinically significant prostate cancer.PATIENTS AND METHODS: From January 2017 to December 2022, 72 consecutive patients, who had undergone multiparametric MR imaging were enrolled in this study. Four different patient groups were formed: (1) those with prostate cancer, (2) those with prostatitis, (3) those with benign prostate hyperplasia (BPH), and (4) a control group. Quantitative dynamic contrast-enhanced (DCE)-MRI pharmacokinetic parameters included Ktrans, Kep, Ve, and iAUG. Different measurements were obtained from both the peripheral and transitional zones (PZ and TZ, respectively). Means values were compared between groups based on a univariate analysis.RESULTS: Ktrans and Kep values in the PZ were found to be statistically significantly lower in the control group (p = 0. 003 and p = 0. 011, respectively). It was seen that Ktrans and Ve measurements obtained from PZ had a statistically significant determinant in detecting malignancy (p = 0. 013 and p = 0. 036, respectively). It was seen that Ktrans, Ve, and iAUG obtained from the TZ showed a statistically significant difference in prostate cancer (p = 0.025, p = 0.005, and p = 0. 011, respectively) in contrast to other cases. Peripheral Ve values were statistically significantly lower than those measured Ve values from the TZ in prostate cancer cases (p = 0.002) in contrast to the other cases.CONCLUSIONS: Quantitative DCE-MRI parameters may vary according to age, disease, and zonal anatomy. These differences may contribute to the diagnosis of clinically relevant prostate cancer.
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    Enhanced prediction of ozone concentrations using an artificial neural network model
    (Plapiqui(Uns-Conicet), 2025) Uguz, G.; Karadurmus, E.; Kaya, S.; Goz, E.; Akyazi, H.; Yuceer, M.
    The primary goal of this research was to develop an Artificial Neural Network (ANN) model to predict ozone (O3) concentrations using hourly data obtained from a monitoring station in Samsun City, located in the Middle Black Sea Region of Turkey. The dataset utilized encompassed the years from 2016 to 2020. The ANN architecture incorporated eleven input nodes representing various parameters: month, hour, concentrations of particulate matter (PM2.5 and PM10), nitrogen oxides (NOx), wind direction, relative humidity, air temperature, wind speed, cabin temperature of the measuring station, and air pressure. The focus of the model's output was on predicting the O3 concentration. During the training and testing phases, the ANN model displayed outstanding performance, as evidenced by correlation coefficients nearing one. The model also registered minimal values for Mean Absolute Percentage Error (MAPE, %), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). In the training phase, the model achieved a Training R-value of 0.9993, an RMSE of 0.7424, a M APE of 4.3221 %, and a MAE of 0.5301. The testing phase showed equally strong results, with a Test R-value of 0.9990, an RMSE of 0.8595, a M APE of 4.5642 %, and an MAE of 0.5823. These outcomes emphasize the model's ability to accurately predict ozone concentrations, markedly enhancing the precision compared to previous models based on traditional statistical methods. The findings of this study highlight the potential of this ANN model in providing precise ozone concentration readings in the atmosphere. The proposed ANN model distinguishes itself from previous studies by incorporating more representative variables as inputs, significantly boosting prediction accuracy. Additionally, the removal of outliers during preprocessing enhances data quality, thereby increasing the reliability of the predictions. Despite its simple structure, the model demonstrates high performance, making it both innovative and effective in comparison to earlier models. Moreover, the model's superior performance may reduce the need for additional measurement devices at newly established monitoring stations.
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    Investigation of rheological and mechanical properties of wood flour reinforced polypropylene
    (Wiley-V C H Verlag Gmbh, 2018) Guldas, A.; Zeyveli, M.; Kaya, S.; Altug, M.
    This study aims to investigate the rheological and mechanical properties of polypropylene when reinforced by wood flour. In the study, wood flour (WF) was added as filling material to polypropylene (PP), and its effects were investigated. The grain size of the wood flour was 300m. Wood flour was mixed into polypropylene material at different rates of weight. Viscosity changes of the polypropylene at various temperatures and pressure values of the concentration rates were documented. As a result, viscosity increases as the amount of wood flour filling is increased; yet as temperature, pressure and shear rate are increased, viscosity decreases. The study also focuses on the changes in its mechanical properties of polypropylene reinforced with wood flour. The tensile strength of reinforced polypropylene at varying percentages (wt. 5 %, wt. 10 % and wt. 15 %) was observed to decrease by 11 %, 16 % and 21 %, respectively, compared to that of non-reinforced polypropylene. Additionally, impact tests showed that the highest energy absorption was in 10% wood flour reinforced polypropylene.
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    Relationship between chronic otitis media with effusion and overweight or obesity in children
    (Cambridge Univ Press, 2017) Kaya, S.; Selimoglu, E.; Cureoglu, S.; Selimoglu, M. A.
    Objectives: Otitis media with effusion and obesity are both common in childhood and might share some immunological alterations. This study aimed to investigate the relationship between chronic otitis media with effusion and childhood overweight or obesity, including the potential effects of adenoid or tonsillar hypertrophy on that relationship. Methods: This study included 60 children with chronic otitis media with effusion and 86 healthy children aged from 2 to 10 years. Measures of height and weight were used to calculate the body mass index, weight for height and weight z score. Results: The prevalence of overweight or obesity was higher in children with chronic otitis media with effusion, according to the weight for height percentiles (p = 0.012). However, neither the presence of adenoid or tonsillar hypertrophy nor the degree of adenoid hypertrophy was associated with overweight or obesity. Conclusion: Overweight and obesity might be risk factors for developing chronic otitis media with effusion, or vice versa.