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Salivary Interleukin-6 Level in Iraqi Patients with Oral Lichen Planus receiving Platelet-Rich Plasma Injections
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Background: Interleukin-6 (IL-6) is a cytokine that has several functions, including stimulating growth and inhibiting cell death. It has the potential to operate as a biomarker for the accurate prediction of disease severity and activity, platelets-rich plasma was used in the treatment of oral lichen planus and can change the salivary IL-6 level.

Objectives: To study the clinical outcome of intralesional platelets-rich plasma in patients with oral lichen planus and to measure salivary IL-6 levels before and after the treatment with platelets-rich plasma were the aims of this study.

Subjects and Methods: In this clinical trial, for each patient a standardized case sheet was filled including demography, social, medical, and medication history. Before receiving, each patient was examined for phenotype, color, size, and site of oral lichen planus lesions. Patient's salivary samples were taken between 8 and 11 a.m. Three to four milliliters of saliva was obtained from each patient. The ELISA kit for IL-6 using sandwich-ELISA technique, to measure salivary IL-6 before and after PRP injections.

Results: Thirteen oral lichen planus patients were took part in this study, six males (46.2%) and seven females (53.8%). The patients were between 32 and 91 years of age, with a mean age of 60.2(±13.9) years. All symptomatic and most of the hyperemic ones showed improvement after PRP injections, while the size of the lesions was resistant to change. Mean salivary IL-6 was 44.27 pg/mL (±43.24) before PRP injections and (69.74±59.86 pg/mL) after PRP injections. No significant difference was found, however IL-6 was higher after PRP injections. In relation to color changes after PRP, there was a significant changes in IL-6 level compared with that before PRP injections. Similarly, a significant relation was found between IL-6 level and signs and symptoms; pain and burning sensation. No association was found in salivary IL-6 level in relation to lesion phenotype, size, and location.

Conclusion: All symptomatic OLP lesions that were treated with intralesional PRP responded very well, similarly almost all hyperemic lesions turned into normal mucosal color after completing the course of treatment. The majority of OLP lesions showed an increased salivary IL-6 levels after PRP treatment. 

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Publication Date
Tue Aug 01 2023
Journal Name
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Decoding transient sEMG data for intent motion recognition in transhumeral amputees
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Thu Jul 01 2021
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A new proposed statistical feature extraction method in speech emotion recognition
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Publication Date
Wed Oct 01 2025
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Advances in Piezoelectric Energy Harvesting: Materials, Configurations, and Power Optimisation Strategies
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Mon Jan 01 2024
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Explainable Artificial Intelligence In The Digital Sustainability Administration
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Publication Date
Sun Aug 01 2021
Journal Name
Bulletin Of Electrical Engineering And Informatics
Robust speaker verification by combining MFCC and entrocy in noisy conditions
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Automatic speaker recognition may achieve remarkable performance in matched training and test conditions. Conversely, results drop significantly in incompatible noisy conditions. Furthermore, feature extraction significantly affects performance. Mel-frequency cepstral coefficients MFCCs are most commonly used in this field of study. The literature has reported that the conditions for training and testing are highly correlated. Taken together, these facts support strong recommendations for using MFCC features in similar environmental conditions (train/test) for speaker recognition. However, with noise and reverberation present, MFCC performance is not reliable. To address this, we propose a new feature 'entrocy' for accurate and robu

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Publication Date
Sat Feb 12 2022
Journal Name
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The Possibility of Minimizing Rutting Distress in Asphalt Concrete Wearing Course
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The excessive permanent deformation (rutting) in asphalt-concrete pavements resulting from frequent repetitions of heavy axle loads is studied in this paper. Rutting gradually develops with additional load applications and appears as longitudinal depressions in the wheel path. There are many causes of the rutting of asphalt roads, such as poor asphalt mixing and poor continuous aggregate gradation. All factors affecting the mixture resistance to permanent deformation must be discussed, and all must be properly considered to reduce the rutting propensity of asphalt-aggregate mixtures. In this study, several mixtures were produced with the most common techniques in rutting resistance (using the most effective additives for each mixture), and

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Sun Jan 01 2023
Journal Name
E3s Web Of Conferences
Mitigation of Reflection Cracking in Asphalt Concrete Overlay on Rigid Pavements
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Reflective cracking is one of the primary forms of deterioration in pavements. It is widespread when Asphalt concrete (AC) overlays are built over a rigid pavement with discontinuities on its surface. Thus, this research work aims to reduce reflection cracks in asphalt concrete overlay on the rigid pavement. Asphalt Concrete (AC) slab specimens were prepared in three thicknesses (4, 5, and 6 cm). All these specimens were by testing machine designed and manufactured at the Engineering Consulting Office of the University of Baghdad to examine for the number of cycles and loads needed to propagate the reflection cracking in the asphalt concert mixture at three temperatures (20, 30, and 30°C). It was noticed that the higher thickness A

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Publication Date
Sun Apr 02 2023
Journal Name
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Current concepts in the pathogenesis of periodontitis: from symbiosis to dysbiosis
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Mon Feb 27 2023
Journal Name
Applied Sciences
Comparison of ML/DL Approaches for Detecting DDoS Attacks in SDN
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Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an

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