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Introduction:In recent years, the use of ceramic base zirconia is considered in dentistry for all ceramic restorations because of its chemical stability, biocompatibility, and good compressive as well as flexural strength. However, due to its chemical stability, there is a challenge with dental bonding. Several studies have been done to improve zirconia bonding but they are not reliable. The purpose of this research is to study the effect of plasma treatment on bonding strength of zirconia. Methods: In this in vitro study, 180 zirconia discs' (thickness was 0.85-0.9 mm) surfaces were processed with plasma of oxygen, argon, air and oxygen-argon combination with 90-10 and 80-20 ratio (n=30 for each group) after being polished by sandblast. Surface modifications were assessed by measuring the contact angle, surface roughness, and topographical evaluations. Cylindrical Panavia f2 resin-cement and Diafill were used for microshear strength bond measurements. The data analysis was performed by SPSS 20.0 software and one-way analysis of variance (ANOVA) and Tukey test as the post hoc. Results: Plasma treatment in all groups significantly reduces contact angle compare with control (P = 0.001). Topographic evaluations revealed coarseness promotion occurred in all plasma treated groups which was significant when compared to control (P < 0.05), except argon plasma treated group that significantly decreased surface roughness (P < 0.05). In all treated groups, microshear bond strength increased, except oxygen treated plasma group which decreased this strength. Air and argon-oxygen combination (both groups) significantly increased microshear bond strength (P < 0.05). Conclusion: According to this research, plasmatic processing with dielectric barrier method in atmospheric pressure can increase zirconia bonding strength.

Coronavirus disease, first detected in late 2019 (COVID-19), has spread fast throughout the world, leading to high mortality. This condition can be diagnosed using RT-PCR technique on nasopharyngeal and throat swabs with sensitivity values ranging from 30 to 70%. However, chest CT scans and X-ray images have been reported to have sensitivity values of 98 and 69%, respectively. The application of machine learning methods on CT and X-ray images has facilitated the accurate diagnosis of COVID-19. In this study, we reviewed studies which used machine and deep learning methods on chest X-ray images and CT scans for COVID-19 diagnosis and compared their performance. The accuracy of these methods ranged from 76% to more than 99%, indicating the applicability of machine and deep learning methods in the clinical diagnosis of COVID-19.

COVID-19 has produced a global pandemic affecting all over of the world. Prediction of the rate of COVID-19 spread and modeling of its course have critical impact on both health system and policy makers. Indeed, policy making depends on judgments formed by the prediction models to propose new strategies and to measure the efficiency of the imposed policies. Based on the nonlinear and complex nature of this disorder and difficulties in estimation of virus transmission features using traditional epidemic models, artificial intelligence methods have been applied for prediction of its spread. Based on the importance of machine and deep learning approaches in the estimation of COVID-19 spreading trend, in the present study, we review studies which used these strategies to predict the number of new cases of COVID-19. Adaptive neuro-fuzzy inference system, long short-term memory, recurrent neural network and multilayer perceptron are among the mostly used strategies in this regard. We compared the performance of several machine learning methods in prediction of COVID-19 spread. Root means squared error (RMSE), mean absolute error (MAE), R 2 coefficient of determination (R 2 ), and mean absolute percentage error (MAPE) parameters were selected as performance measures for comparison of the accuracy of models. R 2 values have ranged from 0.64 to 1 for artificial neural network (ANN) and Bidirectional long short-term memory (LSTM), respectively. Adaptive neuro-fuzzy inference system (ANFIS), Autoregressive Integrated Moving Average (ARIMA) and Multilayer perceptron (MLP) have also have R 2 values near 1. ARIMA and LSTM had the highest MAPE values. Collectively, these models are capable of identification of learning parameters that affect dissimilarities in COVID-19 spread across various regions or populations, combining numerous intervention methods and implementing what-if scenarios by integrating data from diseases having analogous trends with COVID-19. Therefore, application of these methods would help in precise policy making to design the most appropriate interventions and avoid non-efficient restrictions.

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Platelet-rich plasma (PRP) contains growth factors which positively affect cell proliferation, cell differentiation, chemotaxis and intracellular matrix synthesis. All these processes are involved in wound healing and tissue regeneration; thus, PRP as a source of growth factors can be used in periodontal regenerative therapies. The purpose of the present study was to assess the effect of various concentrations of activated and non-activated PRP on proliferation of osteoblasts and fibroblasts in vitro. PRP was obtained from three healthy volunteers. 75, 50, 25, and 10% concentrations of f PRP were prepared by dilution in Dulbecco's modified Eagle's medium. In activated PRP groups, PRP concentrations were activated by adding calcium gluconate. Human gingival fibroblast (HGF) cell line and MG-63 (osteosarcoma) human osteoblast-like cell line were used in the study. The MTT proliferation assay was used to assess the effect of different types of PRP concentrates on proliferation of HGF and MG-63 cells, in 24, 48 and 72 h. After 24, 48, and 72 h, the proliferation rate of both cell lines was higher in the positive control group, except in 72 h in HGF cell lines, that 10% non-activated PRP group and 10 and 25% activated PRP groups has higher proliferation rate than the positive control group, which it was not significant. Proliferation rate in cells with 10% activated PRP was highest among samples containing PRP. The current study failed to show the significant effect of activated or non-activated PRP on proliferation of HGFs or MG-63 osteoblast-like cells. However, our results showed that activated PRP had a greater effect than non-activated PRP.

Objective This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model’s performance using weighted kappa and Cohen’s kappa statistical analyses. Results The model’s validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model’s validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. Conclusions The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

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