Dental Fluorosis Cases in Turkey

Derin, Perihan; Yazici‐Karabulut, Benan; Demir‐Yetis, Ayşegul; Karabulut, Abdullah İzzeddin; Atasoy, Ayse Dilek; Yeşilnacar, Mehmet İ̇rfan; Bayhan, İbrahim; Alp, Seda; Özer, Hazal

doi:10.1002/9781119867371.ch22

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…The total score is the sum of all answered items, where a score of 0-6 indicates the absence of symptoms, while scores of 7-19, 20-34 and 35 to 60 indicate mild, moderate and severe depression respectively (Snaith, Harrop, Newby & Teale, 1986). The Turkish version of the scale was found to be valid and reliable in use with adults (Cronbach's alpha = 0.92) (Ozer, Demir, Tugal, Kabakci & Yazici, 2001). (Tran et al, 2013), and its psychometric properties are appropriate to the original version (FFMQ-S subscales' Cronbach's alphas range from 0.62 to 0.81; Tran et al, 2013).…”

Section: Methodsmentioning

confidence: 99%

The effect of emotion recognition and mindfulness on depression symptoms: A case–control study

Aydın,

Tvrtkovic,

Çakıroğlu

et al. 2023

Scandinavian J Psychology

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Abnormalities in emotion recognition (ER) are frequently reported in depression, with lower recognition accuracy in patients with major depressive disorder (MDD) when compared to healthy individuals. Mindfulness was found to directly impact the severity of depressive symptoms, by recognizing negative cognitions and dysfunctional reactions. The aims of this study were to compare ER and mindfulness levels between MDD patients and healthy controls (HCs), as well as to examine whether ER and mindfulness are related to symptom severity in MDD patients. Sixty‐eight patients with MDD and 93 HCs participated in the study. A sociodemographic form, reading the mind in the eyes test (RMET), five facet mindfulness questionnaire‐short form (FFMQ‐S) and the Montgomery–Asberg depression scale (MADRS) were administered. Group comparison in ER and mindfulness was performed using the multivariate analysis of covariance (MANCOVA). Bivariate correlations and hierarchical linear regression analyses were performed to assess the associations between depression severity, ER and mindfulness in the patient group. Higher level of mindfulness was found in HCs relative to MDD group, however, no ER difference was present between the groups. A positive association between depression severity and the non‐reactivity facet of mindfulness was found. On the other hand, ER was not significantly associated with symptom severity among individuals with MDD. Non‐reactivity, unlike other dimensions of mindfulness, seems to increase with the severity of depressive symptoms among MDD patients. A particular focus on this subdimension in mindfulness techniques may yield better outcomes in alleviation of depressive symptoms.

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Section: Methodsmentioning

confidence: 99%

The effect of emotion recognition and mindfulness on depression symptoms: A case–control study

Aydın,

Tvrtkovic,

Çakıroğlu

et al. 2023

Scandinavian J Psychology

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Integrating deep learning and regression models for accurate prediction of groundwater fluoride contamination in old city in Bitlis province, Eastern Anatolia Region, Türkiye

Demir Yetiş,

İlhan,

Kara

2024

Environ Sci Pollut Res

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Groundwater resources in Bitlis province and its surroundings in Türkiye’s Eastern Anatolia Region are pivotal for drinking water, yet they face a significant threat from fluoride contamination, compounded by the region’s volcanic rock structure. To address this concern, fluoride levels were meticulously measured at 30 points in June 2019 dry period and September 2019 rainy period. Despite the accuracy of present measurement techniques, their time-consuming nature renders them economically unviable. Therefore, this study aims to assess the distribution of probable geogenic contamination of groundwater and develop a robust prediction model by analyzing the relationship between predictive variables and target contaminants. In this pursuit, various machine learning techniques and regression models, including Linear Regression, Random Forest, Decision Tree, K-Neighbors, and XGBoost, as well as deep learning models such as ANN, DNN, CNN, and LSTM, were employed. Elements such as aluminum (Al), boron (B), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), phosphorus (Pb), lead (Pb), and zinc (Zn) were utilized as features to predict fluoride levels. The SelectKbest feature selection method was used to improve the accuracy of the prediction model. This method identifies important features in the dataset for different values of k and increases model efficiency. The models were able to produce more accurate predictions by selecting the most important variables. The findings highlight the superior performance of the XGBoost regressor and CNN in predicting groundwater quality, with XGBoost consistently outperforming other models, exhibiting the lowest values for evaluation metrics like mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) across different k values. For instance, when considering all features, XGBoost attained an MSE of 0.07, an MAE of 0.22, an RMSE of 0.27, a MAPE of 9.25%, and an NSE of 0.75. Conversely, the Decision Tree regressor consistently displayed inferior performance, with its maximum MSE reaching 0.11 (k = 5) and maximum RMSE of 0.33 (k = 5). Furthermore, feature selection analysis revealed the consistent significance of boron (B) and cadmium (Cd) across all datasets, underscoring their pivotal roles in groundwater contamination. Notably, in the machine learning framework evaluation, the XGBoost regressor excelled in modeling both the “all” and “rainy season” datasets, while the convolutional neural network (CNN) outperformed in the “dry season” dataset. This study emphasizes the potential of XGBoost regressor and CNN for accurate groundwater quality prediction and recommends their utilization, while acknowledging the limitations of the Decision Tree Regressor.

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Health risk assessment in an area of dental fluorosis disease from high fluoride drinking water: a case study from southeastern Türkiye

Karabulut

Derin

Yetiş

et al. 2023

International Journal of Environmental Health Research

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Dental Fluorosis Cases in Turkey

Cited by 3 publications

References 30 publications

The effect of emotion recognition and mindfulness on depression symptoms: A case–control study

The effect of emotion recognition and mindfulness on depression symptoms: A case–control study

Integrating deep learning and regression models for accurate prediction of groundwater fluoride contamination in old city in Bitlis province, Eastern Anatolia Region, Türkiye

Health risk assessment in an area of dental fluorosis disease from high fluoride drinking water: a case study from southeastern Türkiye

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