Background: Ulcerative colitis disease is a chronic inflammatory condition that affects the gastrointestinal tract. In regulation of this inflammatory process, Interleukin-6, C-reactive proteins and albumin have a major role. Overproduction of IL-6 by immunocompetent cells contributes to activate the liver to produce CRP, transudation of plasma albumin and development of the inflammatory condition. Elevated levels of IL-6 in saliva could be expected, because the saliva-producing cells are part of the digestive system. The purpose of this study was to assess salivary IL-6, CRP and albumin in ulcerative colitis patients in relation to oral findings. Materials and methods: Forty eight saliva specimens collected from three groups of subjects (sixteen newly diagnosed UC patients, sixteen UC patients on medication and sixteen healthy subjects). The specimens were centrifuged and stored at -20°C then three ELISA kits were used for estimating the three variables. Results: There was a significant elevation of salivary IL-6, CRP and albumin level in both newly diagnosed and on medication groups in comparison to healthy persons. There was a significant elevation differences of salivary IL-6, CRP and albumin level between newly diagnosed and on medication groups. The prevalence of aphthus ulcer was highly significant in the newly diagnosed group in comparison to the other groups. Twenty five percent of patients on medication complain from candidiasis and only one patients with tempromandibular joints problem (hard clicking). Conclusions: Salivary IL-6, CRP and albumin are elevated simultaneously in UC patients, in both newly diagnosed and on medication groups, but the mean of variables in second group was lower than in the newly diagnosed group. There are no correlation between salivary IL-6, CRP and albumin with oral findings.
Reactive Powder Concrete (RPC) could be considered as the furthermost significant modern high compressive strength concrete. In this study, an experimental investigation on the impact of micro steel fiber volume fraction ratio and gamma ray irradiation duration influence upon the compressive strength of RPC is presented. Three volume fraction ratios (0.0, 1.0 and 1.5) % was implemented. For each percentage of the adopted fiber ratios, six different irradiation duration was considered; these are (1, 2, 3, 4, 5 and 6) days. Gamma source (Cs-137) of energy (0.662) MeV and activity (6) mci was used. In a case of zero volume fraction ratio, the experimental results showed that gamma ray had a significant influence on the reducing of the
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Background. Colorectal cancer, ranking second place in global cancer mortality, arises from diverse causes. There is growing recognition of the substantial involvement of the epigenetic modifications of histones at the DNA level in the occurrence of CRC. Aim. To assess the expression of p53, HDAC1, and HDAC3 proteins in a cohort of CRC patients and to analyze potential relationship between their expression and the stages of CRC progression. Materials and Methods. The retrospective investigation was carried out on 95 paraffin-embedded CRC tissue samples. The expression of p53, HDAC1, and HDAC3 was assessed immunohistochemically. Results. Notably, the expression of the p53 protein in CRC tissue samples exhibited a prominent correlatio
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This paper proposes a new approach, of Clustering Ultrasound images using the Hybrid Filter (CUHF) to determine the gender of the fetus in the early stages. The possible advantage of CUHF, a better result can be achieved when fuzzy c-mean FCM returns incorrect clusters. The proposed approach is conducted in two steps. Firstly, a preprocessing step to decrease the noise presented in ultrasound images by applying the filters: Local Binary Pattern (LBP), median, median and discrete wavelet (DWT),(median, DWT & LBP) and (median & Laplacian) ML. Secondly, implementing Fuzzy C-Mean (FCM) for clustering the resulted images from the first step. Amongst those filters, Median & Laplace has recorded a better accuracy. Our experimental evaluation on re
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The influx of data in bioinformatics is primarily in the form of DNA, RNA, and protein sequences. This condition places a significant burden on scientists and computers. Some genomics studies depend on clustering techniques to group similarly expressed genes into one cluster. Clustering is a type of unsupervised learning that can be used to divide unknown cluster data into clusters. The k-means and fuzzy c-means (FCM) algorithms are examples of algorithms that can be used for clustering. Consequently, clustering is a common approach that divides an input space into several homogeneous zones; it can be achieved using a variety of algorithms. This study used three models to cluster a brain tumor dataset. The first model uses FCM, whic
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