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    Clinical features and comorbidities in psoriasis. A retrospective study
    (Termedia Publishing House Ltd, 2022) Cicekliyurt, Meliha Merve Hiz; Ogretmen, Zerrin
    Introduction: Patients with psoriasis may develop several comorbidities. Objective: To determine the clinical characteristics and comorbidities associated with psoriasis. Material and methods: This retrospective case-control study involved 422 adult patients with psoriasis and 444 healthy individuals. The inclusion criteria for patients were: over 18 years old and at least one-year history of confirmed psoriasis. Data, such as age, gender, body mass index, smoking, and alcohol consumption habits were analyzed in addition to detailed physical and dermatological examination. Results: The common comorbidities in patients with psoriasis were depression (n = 144, 34%), hypertension (n = 168, 39.81%), diabetes mellitus (n = 100, 23.7%), coronary artery disease (n = 59, 13.9%) and metabolic syndrome (n = 67, 15.88%). The most common conditions in the control group were hypertension (n = 62, 13.96%), hyperlipidemia (n = 62, 13.96%), diabetes mellitus (n = 42; 9.46%), metabolic syndrome (n = 32; 7.21%) and coronary artery disease (5.41%). Patients with psoriasis are at a higher risk for obesity compared to healthy controls (OR = 1.99; p < 0.0001). In addition, smoking and alcohol consumption were significantly higher in patients with psoriasis (p < 0.0001). Conclusions: These results indicate an increased prevalence of obesity, hyperlipidemia, hypertension, cardiovascular diseases, diabetes, metabolic syndrome, and depression in patients with psoriasis.
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    DNA methylation in bryophytes as a biomarker for monitoring environmental pollution
    (Natl Inst Science Communication-Niscair, 2022) Cicekliyurt, Meliha Merve Hiz; Yayintas, Ozlem Tonguc
    Rapidly growing industrialization and increased need for transportation have led to environmental pollution, particularly heavy metals. Efficient monitoring would help planning effective strategies to curb such increasing pollution. In this context, we studied the epigenetic changes in the bryophyte Greater Fork-moss, Dicranum majus Turner so as to use to monitor the environmental stress conditions due to accumulation of heavy metals and toxic organic compounds. The hypothesis is that the DNAm (DNA methylation) signatures reflect changes in the environmental conditions, and thus could serve as an alternate monitoring tool to study environmental pollution. The vegetative form of D. majus was collected from two different geographical locations where one was near the main road (MR) and another in the forest area (FS). DNAm rate was found 10.41 +/- 2.009 and 23.37 +/- 2.94 in MR and FS, respectively (P <0.005). The only difference between the two samples were traffic related pollutants. Thus, the reuslts suggest that vehicle pollution induces epigenetic changes in bryophytes, particularly DNA methylation, and could serve as a valuable biomarker to assess pollution risk due to vehicle traffic.
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    Machine Learning-Based Validation of LDHC and SLC35G2 Methylation as Epigenetic Biomarkers for Food Allergy
    (Mdpi, 2025) Kilicarslan, Sabire; Cicekliyurt, Meliha Merve Hiz; Kilicarslan, Serhat; Hassan, Dina S. M.; Samee, Nagwan Abdel; Kurtoglu, Ahmet
    Background: Food allergies represent a growing global health concern, yet the current diagnostic methods often fail to distinguish between true allergies and food sensitivities, leading to misdiagnoses and inadequate treatment. Epigenetic alterations, such as DNA methylation (DNAm), may offer novel biomarkers for precise diagnosis. Methods: This study employed a computational machine learning framework integrated with DNAm data to identify potential biomarkers and enhance diagnostic accuracy. Differential methylation analysis was performed using the limma package to identify informative CpG features, which were then analyzed with advanced algorithms, including SVM (polynomial and RBF kernels), k-NN, Random Forest, and artificial neural networks (ANN). Deep learning via a stacked autoencoder (SAE) further enriched the analysis by uncovering epigenetic patterns and reducing feature dimensionality. To ensure robustness, the identified biomarkers were independently validated using the external dataset GSE114135. Results: The hybrid machine learning models revealed LDHC and SLC35G2 methylation as promising biomarkers for food allergy prediction. Notably, the methylation pattern of the LDHC gene showed significant potential in distinguishing individuals with food allergies from those with food sensitivity. Additionally, the integration of machine learning and deep learning provided a robust platform for analyzing complex epigenetic data. Importantly, validation on GSE114135 confirmed the reproducibility and reliability of these findings across independent cohorts. Conclusions: This study demonstrates the potential of combining machine learning with DNAm data to advance precision medicine in food allergy diagnosis. The results highlight LDHC and SLC35G2 as robust epigenetic biomarkers, validated across two independent datasets (GSE114134 and GSE114135). These findings underscore the importance of developing clinical tests that incorporate these biomarkers to reduce misdiagnosis and lay the groundwork for exploring epigenetic regulation in allergic diseases.