Background. After tooth extraction, alveolar bone resorption is inevitable. This clinical phenomenon challenges dental surgeons aiming to restore esthetic and function. Alveolar ridge preservation can be applied to minimize dimensional changes with a new socket grafting material, an autogenous dentin graft, produced by mechanically and chemically processing natural teeth. This study assessed the safety and efficacy of using autogenous dentin biomaterial in alveolar ridge preservation. Materials and Methods. Patients with nonrestorable maxillary anterior teeth bounded by natural sound teeth were included in this study. After a detailed clinical and tomographic examination, eligibl

Due to the importance of nanotechnology because of its features and applications in various fields, it has become the focus of attention of the world and researchers. In this study, the concept of nanotechnology and nanomaterials was identified, the most important methods of preparing them, as well as the preparation techniques and the most important devices used in their characterization.

This research dealt with study of cladistics taxonomy of  five  species related to the genus Rumex L. and Polygonum L. from family polygonaceae in Iraq by using Mesquite software V.2.75. This  research support strongly delimiting  the species P. aviculare L. and P. lapathifolia L.as suggested in floras publication while R. dentatus L. is setted in single group whereas R. vesicarius L. and R. conglomeratus Murray were included in the same group. Also, this study involved characteristics of shape, dimensions, color, and ornamentation of seeds and fruits as  the seed forms were ranging from lenticular to trigonous. In terms of size calculations,  the seeds of R. vesicarius  was recorded the higher range (4.0- 4.5) mm in length w

The objective of this investigation was to study the effects of a mixture of three arbuscular mycorrhizae (Glomus etunicatum, G. leptotichum and Rhizophagus intraradices) on the development of fusarium wilt disease in tomato plants in the presence and absence of organic matter (peatmoss). Results indicated an increase in mycorrhizal root dry weight especially in the presence of the organic matter, on the other hand this parameter was significantly decreased when Fusarium oxysporum f. sp. Lycopersiciwas added simultaneously with the mycorrhiza, Moreover, mycorrhiza and organic matter significantly reduced the damping off seedling disease, disease severity and rate of infection of tomato leaves and roots caused by the pathogenic fungus, These

Lymphoma is a cancer arising from B or T lymphocytes that are central immune system ‎components. It is one of the three most common cancers encountered in the canine; ‎lymphoma affects middle-aged to older dogs and usually stems from lymphatic tissues, ‎such as lymph nodes, lymphoid tissue, or spleen. Despite the advance in the management of ‎canine lymphoma, a better understanding of the subtype and tumor aggressiveness is still ‎crucial for improved clinical diagnosis to differentiate malignancy from hyperplastic ‎conditions and to improve decision-making around treating and what treatment type to use. ‎This study aimed to evaluate a potential novel biomarker related to iron metabolism,

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with