Evaluation of Reparative Dentine Bridge Formation after Direct Pulp Capping with Biodentine.

AIM: To evaluate the capability of Biodentine for direct pulp capping in permanent teeth. MATERIALS AND METHODS: The pulp of 11 human intact premolars were mechanically exposed in standard cavity dimensions and then capped with Biodentine for direct pulp capping. After 9-12 weeks, all teeth were extracted. All teeth were scanned using intraoral radiograph or cone-beam computed tomography (CBCT) scan before, right after pulp capping therapy, and after 9-12 weeks on both patient real and extracted teeth for evaluation of reparative dentin bridge formation. All clinical symptoms and signs were also recorded. Data were collected and analyzed using the MedCal statistical software at the significance of 0.05. RESULTS: There was reparative dentin bridge formation in all experimental teeth, on both patient real and extracted teeth. CONCLUSIONS: The Biodentine could induce the formation of reparative dentin in direct pulp capping. The CBCT scan was the reliable modality for evaluation of dentin bridge formation.

Endodontic Length Measurements Using Different Modalities: An In Vitro Study.

INTRODUCTION: The aim of this study was to investigate and compare the accuracy of the 3D Endo software, conventional CBCT software Romexis Viewer, and the EAL E-Pex Pro in WL determination. MATERIALS AND METHODS: Three hundred and two root canals in 110 intact human extracted molars were accessed. The actual lengths (ALs) were measured. Root canal lengths were measured using 3D Endo with proposed length (3D-PL) by software, correct length (3D-CL), Romexis Viewer, and the E-Pex Pro. The percentages of the measurements in the range of ±0.5 mm to the AL were compared using the Fisher's exact test. The paired t test and Bland-Altman plots were calculated to detect the agreement of the four methods with the AL measurements. The statistical significance was set at P<0.05. RESULTS: The accuracies in the range of ±0.5 mm to the AL were 83.8%, 86.7%, 48.3%, and 99.7% for 3D-PL, 3D-CL, Romexis Viewer, and E-PexPro, respectively. There were agreements between 3D-PL and Romexis Viewer with the AL measurements. CONCLUSION: The CBCT measurements using 3D Endo with the proposed length by the software and Romexis Viewer with the voxel size of 0.15 mm agreed with the AL measurements of the root canals.

Evaluation of Roots and Canal Systems of Mandibular First Molars in a Vietnamese Subpopulation Using Cone-Beam Computed Tomography.

OBJECTIVES: The aim of this study is to determine the prevalence of the first lower molars that have two roots or three roots and the number of the root canals of the mandibular first molars in the Vietnamese subpopulation using cone-beam computed tomography (CBCT). MATERIALS AND METHODS: The study was conducted on 166 patients who had CBCT as indicated by dentists in Nguyen Trai Dental CT Center, Ho Chi Minh City using the Picasso Trio (Ewoo Vatech, Korea). The number of root canals of the first lower molars was examined by moving cross-sectional slices from the pulpal floor to the apex. The orifices, middle thirds, and apical thirds of the canals of the first lower molar were observed, and the root canals of each root of the mandibular first molars were observed in three planes. RESULTS: The prevalence of two, three, and four root canals of the mandibular first molars was 4.5%, 66.8%, and 28.9%, respectively. For the distal roots of these molars, a classification of Vertucci type I was the most common at a rate of 80.8%-97.6%. Whenever these teeth had three roots, a Vertucci type I was the classification of 100% of distolingual roots. CONCLUSION: Majority of the mandibular first molars has two roots and three canals. CBCT is appropriate equipment useful in investigating the complex root canal morphology of human teeth.

Hybrid Circuit of Memristor and Complementary Metal-Oxide-Semiconductor for Defect-Tolerant Spatial Pooling with Boost-Factor Adjustment.

Hierarchical Temporal Memory (HTM) has been known as a software framework to model the brain's neocortical operation. However, mimicking the brain's neocortical operation by not software but hardware is more desirable, because the hardware can not only describe the neocortical operation, but can also employ the brain's architectural advantages. To develop a hybrid circuit of memristor and Complementary Metal-Oxide-Semiconductor (CMOS) for realizing HTM's spatial pooler (SP) by hardware, memristor defects such as stuck-at-faults and variations should be considered. For solving the defect problem, we first show that the boost-factor adjustment can make HTM's SP defect-tolerant, because the false activation of defective columns are suppressed. Second, we propose a memristor-CMOS hybrid circuit with the boost-factor adjustment to realize this defect-tolerant SP by hardware. The proposed circuit does not rely on the conventional defect-aware mapping scheme, which cannot avoid the false activation of defective columns. For the Modified subset of National Institute of Standards and Technology (MNIST) vectors, the boost-factor adjusted crossbar with defects = 10% shows a rate loss of only ~0.6%, compared to the ideal crossbar with defects = 0%. On the contrary, the defect-aware mapping without the boost-factor adjustment demonstrates a significant rate loss of ~21.0%. The energy overhead of the boost-factor adjustment is only ~0.05% of the programming energy of memristor synapse crossbar.

Partial-Gated Memristor Crossbar for Fast and Power-Efficient Defect-Tolerant Training.

A real memristor crossbar has defects, which should be considered during the retraining time after the pre-training of the crossbar. For retraining the crossbar with defects, memristors should be updated with the weights that are calculated by the back-propagation algorithm. Unfortunately, programming the memristors takes a very long time and consumes a large amount of power, because of the incremental behavior of memristor's program-verify scheme for the fine-tuning of memristor's conductance. To reduce the programming time and power, the partial gating scheme is proposed here to realize the partial training, where only some part of neurons are trained, which are more responsible in the recognition error. By retraining the part, rather than the entire crossbar, the programming time and power of memristor crossbar can be significantly reduced. The proposed scheme has been verified by CADENCE circuit simulation with the real memristor's Verilog-A model. When compared to retraining the entire crossbar, the loss of recognition rate of the partial gating scheme has been estimated only as small as 2.5% and 2.9%, for the MNIST and CIFAR-10 datasets, respectively. However, the programming time and power can be saved by 86% and 89.5% than the 100% retraining, respectively.

Memristor-CMOS Hybrid Circuit for Temporal-Pooling of Sensory and Hippocampal Responses of Cortical Neurons.

As a software framework, Hierarchical Temporal Memory (HTM) has been developed to perform the brain's neocortical functions, such as spatial and temporal pooling. However, it should be realized with hardware not software not only to mimic the neocortical function but also to exploit its architectural benefit. To do so, we propose a new memristor-CMOS (Complementary Metal-Oxide-Semiconductor) hybrid circuit of temporal-pooling here, which is composed of the input-layer and output-layer neurons mimicking the neocortex. In the hybrid circuit, the input-layer neurons have the proximal and basal/distal dendrites to combine sensory information with the temporal/location information from the brain's hippocampus. Using the same crossbar architecture, the output-layer neurons can perform a prediction by integrating the temporal information on the basal/distal dendrites. For training the proposed circuit, we used only simple Hebbian learning, not the complicated backpropagation algorithm. Due to the simple hardware of Hebbian learning, the proposed hybrid circuit can be very suitable to online learning. The proposed memristor-CMOS hybrid circuit has been verified by the circuit simulation using the real memristor model. The proposed circuit has been verified to predict both the ordinal and out-of-order sequences. In addition, the proposed circuit has been tested with the external noise and memristance variation.

Asymmetrical Training Scheme of Binary-Memristor-Crossbar-Based Neural Networks for Energy-Efficient Edge-Computing Nanoscale Systems.

For realizing neural networks with binary memristor crossbars, memristors should be programmed by high-resistance state (HRS) and low-resistance state (LRS), according to the training algorithms like backpropagation. Unfortunately, it takes a very long time and consumes a large amount of power in training the memristor crossbar, because the program-verify scheme of memristor-programming is based on the incremental programming pulses, where many programming and verifying pulses are repeated until the target conductance. Thus, this reduces the programming time and power is very essential for energy-efficient and fast training of memristor networks. In this paper, we compared four different programming schemes, which are F-F, C-F, F-C, and C-C, respectively. C-C means both HRS and LRS are coarse-programmed. C-F has the coarse-programmed HRS and fine LRS, respectively. F-C is vice versa of C-F. In F-F, both HRS and LRS are fine-programmed. Comparing the error-energy products among the four schemes, C-F shows the minimum error with the minimum energy consumption. The asymmetrical coarse HRS and fine LRS can reduce the time and energy during the crossbar training significantly, because only LRS is fine-programmed. Moreover, the asymmetrical C-F can maintain the network's error as small as F-F, which is due to the coarse-programmed HRS that slightly degrades the error.