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This study intended to evaluate the effects of inorganic trace elements such as magnesium (Mg), strontium (Sr), and zinc (Zn) on root canal dentin using an Artificial Neural Network (ANN). The authors obtained three hundred extracted human premolars from type II diabetic individuals and divided them into three groups according to the solutions used (Mg, Sr, or Zn). The authors subdivided the specimens for each experimental group into five subgroups according to the duration for which the authors soaked the teeth in the solution: 0 (control group), 1, 2, 5, and 10 min (n = 20). The authors then tested the specimens for root fracture resistance (RFR), surface microhardness (SμH), and tubular density (TD). The authors used the data obtained from half of the specimens in each subgroup (10 specimens) for the training of ANN. The authors then used the trained ANN to evaluate the remaining data. The authors analyzed the data by Kolmogorov–Smirnov, one-way ANOVA, post hoc Tukey, and linear regression analysis (P < 0.05). Treatment with Mg, Sr, and Zn significantly increased the values of RFR and SμH (P < 0.05), and decreased the values of TD in dentin specimens (P < 0.05). The authors did not notice any significant differences between evaluations by manual or ANN methods (P > 0.05). The authors concluded that Mg, Sr, and Zn may improve the RFR and SμH, and decrease the TD of root canal dentin in diabetic individuals. ANN may be used as a reliable method to evaluate the physical properties of dentin. [ABSTRACT FROM AUTHOR]

Background: This paper outlines the current status and mechanism for identifying dental implants, with emphasis on future direction and updated technology, and covers the existing factors influencing the identification of implant systems. Main body: A search was performed on the current methods of identifying dental implants between January 2000 through Feb 2020 using online databases for articles published in English. The search was performed using the Google, Rutgers library, PubMed, MEDLINE databases via OVID using the following keywords: implant types identification by x-ray imaging, forensic identification of dental implant, surface types, threaded, non-threaded, software identification, recent technologies, which evaluated different methods in the identification of dental implants and its clinical importance for the dentist and the patient. Of the 387 articles found in initial search results, 10 met the inclusion criteria set for this review. These 10 studies were directly related to the identification of different implant systems. Many studies have indicated identifying dental implants as problematic due to many confounding factors, and the difficulty in finding the specific parts for the dental implant itself. The contribution of digital dentistry is critical. Factors like increasing number of implant manufacturers, dental tourism, and cost, make it difficult to detect and match dental implants by dentists during the chairside time. Conclusion: These factors give rise to the need for a new system to help clinicians in decision making. Artificial intelligence seems to have shown potential to help in this case. However, detailed regulatory mechanisms are still needed for diagnosis and analysis. [ABSTRACT FROM AUTHOR]

Mobile peer-to-peer (MP2P) networks refer to the peer-to-peer overlay networks superimposing above the mobile ad-hoc networks. Heterogeneity of capacity and mobility of the peers as well as inherent limitation of resources along with the wireless networks characteristics are challenges on MP2P networks. In some MP2P networks, in order to improve network performances, special peers, are called super-peers, undertake to perform network managerial tasks. Selection of super-peers, due to their influential position, requires a protocol which considers the capacity of peers. Lack of general information about the capacity of other peers, as well as peers mobility along with dynamic nature of MP2P networks are the major challenges that impose uncertainty in decision making of the super-peer management algorithms. This paper proposes an adaptive super-peer selection algorithm considering peers capacity based on learning automata in MP2P networks, called SSBLA. In the proposed algorithm, each peer is equipped with a learning automaton which is used locally in the operation of super-peer selection by that peer. It has been shown that the suggested algorithm is superior to the existing algorithms. The results of the simulation show that the proposed algorithm can maximize capacity utilization by minimum number of super-peer and improve robustness against failures of super-peers while minimizing selection communication overhead. [ABSTRACT FROM AUTHOR]

Diabetes associated complications, including diabetic retinopathy and loss of vision, are major health concerns. Detecting early retinal vascular changes during diabetes is not well documented, and only few studies have addressed this domain. The purpose of this study was to noninvasively evaluate temporal changes in retinal vasculature at very early stages of diabetes using fundus images from preclinical models of diabetes. Non-diabetic and Akita/+ male mice with different duration of diabetes were subjected to fundus imaging using a Micron III imaging system. The images were obtained from 4 weeks- (onset of diabetes), 8 weeks-, 16 weeks-, and 24 weeks-old male Akita/+ and non-diabetic mice. In total 104 fundus images were subjected to analysis for various feature extractions. A combination of Canny Edge Detector and Angiogenesis Analyzer plug-ins in ImageJ were utilized to quantify various retinal vascular changes in fundus images. Statistical analyses were conducted to determine significant differences in the various extracted features from fundus images of diabetic and non-diabetic animals. Our novel image analysis method led to extraction of over 20 features. These results indicated that some of these features were significantly changed with a short duration of diabetes, and others remained the same but changed after longer duration of diabetes. These patterns likely distinguish acute (protective) and chronic (damaging) associated changes with diabetes. We show that with a combination of various plugging one can extract over 20 features from retinal vasculature fundus images. These features change during diabetes, thus allowing the quantification of quality of retinal vascular architecture as biomarkers for disease progression. In addition, our method was able to identify unique differences among diabetic mice with different duration of diabetes. The ability to noninvasively detect temporal retinal vascular changes during diabetes could lead to identification of specific markers important in the development and progression of diabetes mediated-microvascular changes, evaluation of therapeutic interventions, and eventual reversal of these changes in order to stop or delay disease progression. [ABSTRACT FROM AUTHOR]