OBJECTIVES In silico bioinformatical analysis suggested that the expression of two genes, CCL5 (C-C Motif Chemokine Receptor 5) and ep300 (Histone acetyltransferase p300), could be used as potential new biomarkers in differentiation between periapical granulomas and radicular cysts. Thus, we hypothesized that gene expression of CCL5 and ep300 in periapical lesions would classify the lesions as either granuloma or cyst. MATERIALS Patient samples (n=122) included 46 periapical granulomas, 38 radicular cysts and 38 healthy gingival samples as controls. Real-time PCR analysis of CCL5 and ep300 transcripts was compared to SDHA (Succinate dehydrogenase complex, subunit A) as the reference. Clinical parameters (e.g., intensity of inflammation and lesion size) were measured and correlated with CCL5 and ep300 expression. ROC (Receiver operating characteristic) and logistic regression analyses were used to establish the diagnostic character of ΔCt values. RESULTS Granulomas and radicular cysts had significantly higher expression of CCL5 and ep300 compared to controls (P<0.05). However, no differences were observed when comparing granulomas and radicular cysts. ROC analyses showed that CCL5 and ep300 have good diagnostic accuracy, but low accuracy for distinguishing between the lesions. CONCLUSIONS This study confirmed that expression of CCL5 and ep300 is relevant for the pathogenesis of periapical inflammatory lesions but cannot be used as a distinctive marker between these lesions.

Odontogenic cysts are a group of common pathological lesions of the jaw. Typically, they can be found randomly on X-rays as round benign lesions. However, some of them can behave aggressively with a tendency toward malig-nancy. Among odontogenic cysts with benign pathology, up to 60% of all jaw cysts are radicular cysts, which originate from root canal infection. Pathogenesis involves the interaction between osteoblasts, osteocytes, and osteoclasts as well as the expression of RANK-RANKL/OPG signaling system. Furthermore, collagenases (e.g., MMPs) are expressed in epithelial lining of the cyst. Among odontogenic cysts with potentially aggressive behavior, odontogenic keratocysts (OKCs) have a high rate of recurrence and very debatable treatment options; they can be associated with Gorlin syndrome. Keratocysts have developmental origin and show variability in their gene expression profiles. Their etiology is closely related to genetic factors, especially mutations in different members of Shh signaling pathway, including PTCH gene. cells proliferate, the epithelial nest is formed. When the epithelial nest reaches the size of 1 cm, the center becomes necrotic leading to the formation of future cystic cavity, which becomes lined with the epithelium. For unknown reasons, this epithelium starts secreting fluid, which is called cystic fluid. These steps lead to the formation of radicular cyst, a round cavity filled with fluid and lined with the epithelium and fibrous connective tissue. This description of the radicular cyst development is the prevailing theory.

BACKGROUND: AFP serum levels are considered as diagnostic and specific for hepatocellular carcinoma (HCC) in patients with liver cirrhosis (LC). AIM: This study aimed to examine the diagnostic value of AFP in the distinguishing of patients with HCC from patients with LC, and to analyse the potential correlation between AFP levels and liver disease stages. MATERIAL AND METHODS: Fifty patients with LC and fifty patients with HCC were included in this study. The majority of the patients were males, while the HBV aetiology was dominant. RESULTS: Significant differences between LC and HCC patients were detected for AST, ALT, GGT, bilirubin, AFP and AP. Patients with HCC had higher AFP values compared to LC. There was no significant correlation between the size of the tumour lesion and serum AFP levels. A positive correlation between AFP concentration and GGT activity was determined, as was the negative correlation between AFP and age of the subjects. The AFP value of 23.34 ng/m showed high sensitivity (84%) and specificity (82%). CONCLUSION: The size of the surface below the ROC curve (AUC) was 0.877 (0.80-0.95), which makes AFP a good biomarker and this diagnostic test is sufficient to separate patients with HCC and LC.

The usage of Artificial Neural Networks (ANNs) for genome-enabled classifications and establishing genome-phenotype correlations have been investigated more extensively over the past few years. The reason for this is that ANNs are good approximates of complex functions, so classification can be performed without the need for explicitly defined input-output model. This engineering tool can be applied for optimization of existing methods for disease/syndrome classification. Cytogenetic and molecular analyses are the most frequent tests used in prenatal diagnostic for the early detection of Turner, Klinefelter, Patau, Edwards and Down syndrome. These procedures can be lengthy, repetitive; and often employ invasive techniques so a robust automated method for classifying and reporting prenatal diagnostics would greatly help the clinicians with their routine work. The database consisted of data collected from 2500 pregnant woman that came to the Institute of Gynecology, Infertility and Perinatology “Mehmedbasic” for routine antenatal care between January 2000 and December 2016. During first trimester all women were subject to screening test where values of maternal serum pregnancy-associated plasma protein A (PAPP-A) and free beta human chorionic gonadotropin (β-hCG) were measured. Also, fetal nuchal translucency thickness and the presence or absence of the nasal bone was observed using ultrasound. The architectures of linear feedforward and feedback neural networks were investigated for various training data distributions and number of neurons in hidden layer. Feedback neural network architecture out performed feedforward neural network architecture in predictive ability for all five aneuploidy prenatal syndrome classes. Feedforward neural network with 15 neurons in hidden layer achieved classification sensitivity of 92.00%. Classification sensitivity of feedback (Elman’s) neural network was 99.00%. Average accuracy of feedforward neural network was 89.6% and for feedback was 98.8%. The results presented in this paper prove that an expert diagnostic system based on neural networks can be efficiently used for classification of five aneuploidy syndromes, covered with this study, based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics. Developed Expert System proved to be simple, robust, and powerful in properly classifying prenatal aneuploidy syndromes.

Patients with cancer in developing and low-income countries have limited access to targeted cancer therapies. The transitional nature of these economieshas influencedhealthcare funding,whichhas resulted in the unavailability of targeted cancer treatments. Besides the three studies that will be described here, to our knowledge, no literature exists on the clinical outcome of patients treated with delayed targeted cancer therapy. To raise awareness on the importance of timely targeted cancer treatment, we will discuss three key issues: (1) the low number of targeted cancer therapies for different cancers, (2) thedelay incancer treatment, and (3) the unavailability of cancer diagnostics.