Background: Tumor-like overgrowth lesions of the oral mucosa are pathological growths that project above the normal contour of the oral surface. A practical classification can be made according to the site of origin, the etiology and the histological appearance. The aim of this article is to evaluate and analyze patients with gingival and alveolar ridge tumor-like overgrowth lesions in terms of surgical treatment, diagnosis and outcome. Materials and Methods: Patients complaining of these lesions were treated by surgical excision under local or general anesthesia; the excised lesions were submitted for histopathological examination, during the follow up period the patients were examined for complications and recurrence. Results: Pyogenic granuloma was the most frequently encountered lesion, followed by peripheral giant cell granuloma, fibrous hyperplasia, peripheral ossifying fibroma and neurofibroma. Complications were minimal and recurrence occurred in one patient. Conclusion: Gingival and alveolar ridge overgrowths are common being mostly reactive rather than neoplastic in nature, global recurrence rate was 2.1%.
While conservative access preparations could increase fracture resistance of endodontically treated teeth, it may influence the shape of the prepared root canal. The aim of this study was to compare the prepared canal transportation and centering ability after continuous rotation or reciprocation instrumentation in teeth accessed through traditional or conservative endodontic cavities by using cone-beam computed tomography (CBCT).
Forty extracted intact, matured, and 2-rooted human maxillary first premolars were selected for this
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Copper (Cu) is an essential trace element for the efficient functioning of living organisms. Cu can enter the body in different ways, and when it surpasses the range of biological tolerance, it can have negative consequences. The use of different nanoparticles, especially metal oxide nanoparticles, is increasingly being expanded in the fields of industry and biomedical materials. However, the impact of these nanoparticles on human health is still not completely elucidated. This comparative study was conducted to evaluate the impacts of copper oxide nanoparticles (CuO NPs) and copper sulphate (CuSO4 0.5 (H2O)) on infertility and reproductive function in male albino mice BALB/c. Body weight, the weight of male reproductive organs, mal
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The significance of the study lies in identifying a scientific and objective indicator that clarifies the extent to which key cognitive and visual abilities contribute to skill performance in tennis. This enables coaches and instructors to design scientifically based educational and training units that incorporate these abilities according to their level of contribution, thereby positively impacting technical performance. The abundance of stimuli in tennis and the difficulty of controlling performance, due to the sport's ongoing developments, require a high level of cognitive and visual abilities. The researchers aimed to examine the problem of inadequate organization in educational content, where one aspect is emphasized over other
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New series of metal ions complexes have been prepared from the new ligand [4-Amino-N-(5-methyl-isaxazol-3-yl)-benzenesulfonamide] derived from Sulfamethoxazole and 3-aminophenol. Accordingly, mono-nuclear Mn(II), Fe(III), Co (II), and Rh(III) complexes were prepared by the reaction of previous ligand with MnCl2.4H2O, CoCl2.6H2O, FeCl3.6H2O and RhCl3H2O, respectively. The compounds have been characterized by Fourier-transform infrared (FTIR), ultraviolet–visible (UV–vis), mass, 1H-, and 13C-nuclear magnetic resonance (NMR) spectra and thermo gravimetric analysis (TGA& DSC) curve, Bohr magnetic (B.M.), elemental microanal
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Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne
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