Background: Oral squamous cell carcinoma is the most prevalent malignant neoplasm of the oral cavity which results from accumulated genetic and epigenetic alterations. It is not always inexorable and may be reversible if early intervention in the process can occur to prevent further genetic mutation and disease progression. The FHIT gene is a tumor suppressor gene located in FRA3B region which is the most active common fragile site, where DNA damage leading to aberrant transcripts and translocations frequently occur. The WWOX is a tumor suppressor gene that plays a central role in tumor suppression through transcriptional repression and apoptosis, with its apoptotic function the more prominent of the two. This study aimed to evaluate and compare the immunohistochemical expression of FHIT and WWOX in normal oral mucosa, oral epithelial dysplasia and oral squamous cell carcinoma and to correlate the expression of the mentioned markers with the clinicopathological features and to show the expression of studied markers with each other. Materials and methods: Fifty formalin-fixed, paraffin embedded tissue blocks (10 cases of normal oral mucosa, 19 cases of oral epithelial dysplasia, and 21 cases of oral squamous cell carcinoma) were included in this study. Immunohistochemical staining was performed using anti FHIT polyclonal antibody, and anti WWOX polyclonal antibody. Results: Positive IHC of FHIT was detected with high score in all cases of NOM, 16 cases (84%) of OED and 18 cases (86%) of OSCC. For WWOX expression positive IHC detected with high score in all cases (100%) of NOM, 14 cases (74%) of OED and 15 cases (71%) of OSCC. There was statistically highly significant correlation of both markers in OED and non significant correlation in OSCC, with significant differences among studied groups. Conclusions: These results signifying both markers cooperative tumor suppressive role and potential pathological transition from normal oral mucosa to dysplastic epithelium and subsequently cause malignant oral lesions.
Abstract Background: Timely diagnosis of periodontal disease is crucial for restoring healthy periodontal tissue and improving patients’ prognosis. There is a growing interest in using salivary biomarkers as a noninvasive screening tool for periodontal disease. This study aimed to investigate the diagnostic efficacy of two salivary biomarkers, lactate dehydrogenase (LDH) and total protein, for periodontal disease by assessing their sensitivity in relation to clinical periodontal parameters. Furthermore, the study aimed to explore the impact of systemic disease, age, and sex on the accuracy of these biomarkers in the diagnosis of periodontal health. Materials and methods: A total of 145 participants were categorized into three groups based
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RA Ali, LK Abood, Int J Sci Res, 2017 - Cited by 2
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In this paper a new method is proposed to perform the N-Radon orthogonal frequency division multiplexing (OFDM), which are equivalent to 4-quadrature amplitude modulation (QAM), 16-QAM, 64-QAM, 256-QAM, ... etc. in spectral efficiency. This non conventional method is proposed in order to reduce the constellation energy and increase spectral efficiency. The proposed method gives a significant improvement in Bit Error Rate performance, and keeps bandwidth efficiency and spectrum shape as good as conventional Fast Fourier Transform based OFDM. The new structure was tested and compared with conventional OFDM for Additive White Gaussian Noise, flat, and multi-path selective fading channels. Simulation tests were generated for different channels
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Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor
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