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Mycosis Fungoides (MF) makes up the most of the cutaneous lymphomas. As a malignant disease, the greatest diagnostical challenge is to timely differentiate MF from inflammatory diseases. Contemporary computational methods successfully identify cell nuclei in histological specimens. Deep learning methods are especially favored for such tasks. A deep learning model was used to detect nuclei Hematoxylin-Eosin(H-E) stained micrographs. Nuclear properties are extracted after detection. A multi-layer perceptron classifier is used to detect lymphocytes specifically among the detected nuclei. The comparisons for each property between MF and non-MF were carried out using statistical tests the results are compared with the findings in the literature to provide a descriptive analysis as well. Random forest classifier method is used to build a model to classify MF and non-MF lymphocytes. 10 nuclear properties were statistically significantly different between MF and non-MF specimens. MF nuclei were smaller, darker and more heterogenous. Lymphocyte detection algorithm had an average 90.5% prediction power and MF detection algorithm had an average 94.2% prediction power. This project aims to fill the gap between computational advancement and medical practice. The models could make MF diagnoses easier, more accurate and earlier. The results also challenge the manually examined and defined nuclear properties of MF with the help of data abundance and computer objectivity., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Both systemic inflammation and oxidative stress play crucial roles in the pathogenesis of vitiligo. In recent studies, monocyte to high-density lipoprotein cholesterol ratio (MHR), monocyte to lymphocyte ratio (MLR), neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), mean platelet volume (MPV) and plateletcrit (PCT) have been shown to reflect inflammation and oxidative stress in chronic inflammatory and autoimmune diseases. In this study, we aimed to investigate the hematological and inflammatory parameters in patients with vitiligo and to evaluate their possible relationship with disease severity. The parameters including MHR, MLR, NLR, PLR, MPV, and PCT were retrospectively investigated in patients with vitiligo and healthy controls. Disease severity was evaluated using the vitiligo extent tensity index (VETI) score. A total of 180 patients with vitiligo, and age-gender-matched 180 healthy controls were enrolled in the study. MHR, MLR, PLR, PCT values were found to be significantly higher in patients with vitiligo (p < 0.05). MPV and NLR values showed no statistically significant difference between the two groups. A positive correlation was also detected between MHR and MLR values, disease duration, and VETI score (p < 0.05). We suggest that MHR and MLR can be used as markers of inflammation and oxidative stress in patients with vitiligo. Both markers may also reflect disease severity.