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PURPOSE: Assessing the metastasis status of the sentinel lymph nodes (SLNs) for hematoxylin and eosin-stained frozen tissue sections by pathologists is an essential but tedious and time-consuming task that contributes to accurate breast cancer staging. This study aimed to review a challenge competition (HeLP 2019) for the development of automated solutions for classifying the metastasis status of breast cancer patients. Materials and Methods: A total of 524 digital slides were obtained from frozen SLN sections: 297 (56.7%) from Asan Medical Center (AMC) and 227 (43.4%) from Seoul National University Bundang Hospital (SNUBH), South Korea. The slides were divided into training, development, and validation sets, where the development set comprised slides from both institutions and training and validation set included slides from only AMC and SNUBH, respectively. The algorithms were assessed for area under the receiver operating characteristic curve (AUC) and measurement of the longest metastatic tumor diameter. The final total scores were calculated as the mean of the two metrics, and the three teams with AUC values greater than 0.500 were selected for review and analysis in this study. RESULTS: The top three teams showed AUC values of 0.891, 0.809, and 0.736 and major axis prediction scores of 0.525, 0.459, and 0.387 for the validation set. The major factor that lowered the diagnostic accuracy was micro-metastasis. CONCLUSION: In this challenge competition, accurate deep learning algorithms were developed that can be helpful for making a diagnosis on intraoperative SLN biopsy. The clinical utility of this approach was evaluated by including an external validation set from SNUBH.

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BACKGROUND: Video fluoroscopic swallowing study (VFSS) is considered as the gold standard diagnostic tool for evaluating dysphagia. However, it is time consuming and labor intensive for the clinician to manually search the recorded long video image frame by frame to identify the instantaneous swallowing abnormality in VFSS images. Therefore, this study aims to present a deep leaning-based approach using transfer learning with a convolutional neural network (CNN) that automatically annotates pharyngeal phase frames in untrimmed VFSS videos such that frames need not be searched manually. METHODS: To determine whether the image frame in the VFSS video is in the pharyngeal phase, a single-frame baseline architecture based the deep CNN framework is used and a transfer learning technique with fine-tuning is applied. RESULTS: Compared with all experimental CNN models, that fine-tuned with two blocks of the VGG-16 (VGG16-FT5) model achieved the highest performance in terms of recognizing the frame of pharyngeal phase, that is, the accuracy of 93.20 (±1.25)%, sensitivity of 84.57 (±5.19)%, specificity of 94.36 (±1.21)%, AUC of 0.8947 (±0.0269) and Kappa of 0.7093 (±0.0488). CONCLUSIONS: Using appropriate and fine-tuning techniques and explainable deep learning techniques such as grad CAM, this study shows that the proposed single-frame-baseline-architecture-based deep CNN framework can yield high performances in the full automation of VFSS video analysis.

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According to recent studies, patients with COVID-19 have different feature characteristics on chest X-ray (CXR) than those with other lung diseases. This study aimed at evaluating the layer depths and degree of fine-tuning on transfer learning with a deep convolutional neural network (CNN)-based COVID-19 screening in CXR to identify efficient transfer learning strategies. The CXR images used in this study were collected from publicly available repositories, and the collected images were classified into three classes: COVID-19, pneumonia, and normal. To evaluate the effect of layer depths of the same CNN architecture, CNNs called VGG-16 and VGG-19 were used as backbone networks. Then, each backbone network was trained with different degrees of fine-tuning and comparatively evaluated. The experimental results showed the highest AUC value to be 0.950 concerning COVID-19 classification in the experimental group of a fine-tuned with only 2/5 blocks of the VGG16 backbone network. In conclusion, in the classification of medical images with a limited number of data, a deeper layer depth may not guarantee better results. In addition, even if the same pre-trained CNN architecture is used, an appropriate degree of fine-tuning can help to build an efficient deep learning model.

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This study aimed to compare bone healing and implant stability for three types of dental implants: a threaded implant, a three-dimensional (3D)-printed implant without spikes, and a 3D-printed implant with spikes. In four beagle dogs, left and right mandibular premolars (2nd, 3rd, and 4th) and 1st molars were removed. Twelve weeks later, three types of titanium implants (threaded implant, 3D-printed implant without spikes, and 3D-printed implant with spikes) were randomly inserted into the edentulous ridges of each dog. Implant stability measurements and radiographic recordings were taken every two weeks following implant placement. Twelve weeks after implant surgery, the dogs were sacrificed and bone-to-implant contact (BIC) and bone area fraction occupied (BAFO) were compared between groups. At implant surgery, the primary stability was lower for the 3D-printed implant with spikes (74.05 ± 5.61) than for the threaded implant (83.71 ± 2.90) (p = 0.005). Afterwards, no significant difference in implants' stability was observed between groups up to post-surgery week 12. Histomorphometrical analysis did not reveal a significant difference between the three implants for BIC (p = 0.101) or BAFO (p = 0.288). Within the limits of this study, 3D-printed implants without spikes and threaded implants showed comparable implant stability measurements, BIC, and BAFO.

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: Dental panoramic radiographs (DPRs) provide information required to potentially evaluate bone density changes through a textural and morphological feature analysis on a mandible. This study aims to evaluate the discriminating performance of deep convolutional neural networks (CNNs), employed with various transfer learning strategies, on the classification of specific features of osteoporosis in DPRs. For objective labeling, we collected a dataset containing 680 images from different patients who underwent both skeletal bone mineral density and digital panoramic radiographic examinations at the Korea University Ansan Hospital between 2009 and 2018. Four study groups were used to evaluate the impact of various transfer learning strategies on deep CNN models as follows: a basic CNN model with three convolutional layers (CNN3), visual geometry group deep CNN model (VGG-16), transfer learning model from VGG-16 (VGG-16_TF), and fine-tuning with the transfer learning model (VGG-16_TF_FT). The best performing model achieved an overall area under the receiver operating characteristic of 0.858. In this study, transfer learning and fine-tuning improved the performance of a deep CNN for screening osteoporosis in DPR images. In addition, using the gradient-weighted class activation mapping technique, a visual interpretation of the best performing deep CNN model indicated that the model relied on image features in the lower left and right border of the mandibular. This result suggests that deep learning-based assessment of DPR images could be useful and reliable in the automated screening of osteoporosis patients.

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PURPOSE: The aim of this study was to evaluate the effects of relining materials on the flexural strength of relined thermoplastic denture base resins (TDBRs). MATERIALS AND METHODS: For shear bond strength testing, 120 specimens were fabricated using four TDBRs (EstheShot-Bright, Acrytone, Valplast, Weldenz) that were bonded with three autopolymerizing denture relining resins (ADRRs: Vertex Self-Curing, Tokuyama Rebase, Ufi Gel Hard) with a bond area of 6.0 mm in diameter and were assigned to each group (n=10). For flexural strength testing, 120 specimens measuring 64.0×10.0×3.3 mm (ISO-1567:1999) were fabricated using four TDBRs and three ADRRs and were assigned to each group (n=10). The thickness of the specimens measured 2.0 mm of TDBR and 1.3 mm of ADRR. Forty specimens using four TDBRs and 30 specimens using ADRRs served as the control. All specimens were tested on a universal testing machine. For statistical analysis, Analysis of variance (ANOVA) with Tukey's test as post hoc and Spearman's correlation coefficient analysis (P=.05) were performed. RESULTS: Acry-Tone showed the highest shear bond strength, while Weldenz demonstrated the lowest bond strength between TDBR and ADRRs compared to other groups. EstheShot-Bright exhibited the highest flexural strength, while Weldenz showed the lowest flexural strength. Relined EstheShot-Bright demonstrated the highest flexural strength and relined Weldenz exhibited the lowest flexural strength (P<.05). Flexural strength of TDBRs (P=.001) and shear bond strength (P=.013) exhibited a positive correlation with the flexural strength of relined TDBRs. CONCLUSION: The flexural strength of relined TDBRs was affected by the flexural strength of the original denture base resins and bond strength between denture base resins and relining materials.

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[This corrects the article DOI: 10.1155/2017/7141296.].

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[This corrects the article DOI: 10.1155/2017/1373127.].

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PURPOSE: The object of the present study was to evaluate the shear bonding strength of composite to PEKK by applying several methods of surface treatment associated with various bonding materials. MATERIALS AND METHODS: One hundred and fifty PEKK specimens were assigned randomly to fifteen groups (n = 10) with the combination of three different surface treatments (95% sulfuric acid etching, airborne abrasion with 50 µm alumina, and airborne abrasion with 110 µm silica-coating alumina) and five different bonding materials (Luxatemp Glaze & Bond, Visio.link, All-Bond Universal, Single Bond Universal, and Monobond Plus with Heliobond). After surface treatment, surface roughness and contact angles were examined. Topography modifications after surface treatment were assessed with scanning electron microscopy. Resin composite was mounted on each specimen and then subjected to shear bond strength (SBS) test. SBS data were analyzed statistically using two-way ANOVA, and post-hoc Tukey's test (P<.05). RESULTS: Regardless of bonding materials, mechanical surface treatment groups yielded significantly higher shear bonding strength values than chemical surface treatment groups. Unlike other adhesives, MDP and silane containing self-etching universal adhesive (Single Bond Universal) showed an effective shear bonding strength regardless of surface treatment method. CONCLUSION: Mechanical surface treatment behaves better in terms of PEKK bonding. In addition, self-etching universal adhesive (Single Bond Universal) can be an alternative bonding material to PEKK irrespective of surface treatment method.

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The aim of this study was to evaluate the efficacy of growth factor loaded collagen membranes on new bone formation during horizontal bone augmentation. Mandibular defects (4 × 4 × 4 mm) were surgically prepared in six male beagle dogs, which were then protected with one of three types of membranes: (1) titanium mesh, (2) titanium reinforced collagen, or (3) rhBMP-2 loaded titanium reinforced collagen. Animals were euthanized 8 and 16 weeks after surgery, and nondecalcified specimens were prepared and histomorphologically investigated to determine the degree of osteogenesis. Data were analyzed with Friedman test. With respect to the degree of osteogenesis at earlier stage (8 weeks after surgery), there was significantly higher new bone ratio in rhBMP-2 loaded membrane group (p > 0.05). However, with respect to the long-term results (16 weeks after surgery), there were no significant differences among the three membranes (p > 0.05). Based on histomorphometric analysis, there were no significant differences in horizontal bone gaining ratio (p > 0.05).

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PURPOSE: The purpose of this pilot study was to evaluate and compare polyetherketoneketone (PEKK) with different framework materials for implant-supported prostheses by means of a three-dimensional finite element analysis (3D-FEA) based on cone beam computed tomography (CBCT) and computer-aided design (CAD) data. MATERIALS AND METHODS: A geometric model that consisted of four maxillary implants supporting a prosthesis framework was constructed from CBCT and CAD data of a treated patient. Three different materials (zirconia, titanium, and PEKK) were selected, and their material properties were simulated using FEA software in the generated geometric model. RESULTS: In the PEKK framework (ie, low elastic modulus) group, the stress transferred to the implant and simulated adjacent tissue was reduced when compressive stress was dominant, but increased when tensile stress was dominant. CONCLUSION: This study suggests that the shock-absorbing effects of a resilient implant-supported framework are limited in some areas and that rigid framework material shows a favorable stress distribution and safety of overall components of the prosthesis.

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The aim of this study was to evaluate the biomechanical behavior and long-term safety of high performance polymer PEKK as an intraradicular dental post-core material through comparative finite element analysis (FEA) with other conventional post-core materials. A 3D FEA model of a maxillary central incisor was constructed. A cyclic loading force of 50 N was applied at an angle of 45° to the longitudinal axis of the tooth at the palatal surface of the crown. For comparison with traditionally used post-core materials, three materials (gold, fiberglass, and PEKK) were simulated to determine their post-core properties. PEKK, with a lower elastic modulus than root dentin, showed comparably high failure resistance and a more favorable stress distribution than conventional post-core material. However, the PEKK post-core system showed a higher probability of debonding and crown failure under long-term cyclic loading than the metal or fiberglass post-core systems.

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Digital impressions can be a useful option that reduces patient discomfort and simplifies clinical procedures such as accurate impression recordings. In this report, a patient with a partial maxillectomy was managed with a metal frame fabricated from a digital impression through an intraoral scanner. The final impression employed the altered cast technique for the fabrication of the obturator.

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BACKGROUND: The purpose of this study was to evaluate the characteristics of Bennett lesions in baseball players compared with those without a Bennett lesion and to identify other possible factors associated with Bennett lesions on magnetic resonance imaging (MRI). METHODS: We investigated 388 male baseball players with a career >1 year. Demographic factors and a routine physical examination, including glenohumeral internal rotation difference, scapular dyskinesis, and various pathologic changes, were reviewed on MRI to identify relative factors for Bennett lesions. RESULTS: Of the 388 patients evaluated, 125 (32.2%) were diagnosed with Bennett lesions of the shoulder. No significant differences were observed between the groups in demographic factors, physical examination results, visual analog scale score, American Shoulder and Elbow Surgeons score, or prevalence of concomitant diseases. However, players with Bennett lesions had played baseball longer than those without the lesions (P < .001). CONCLUSION: An association was found between Bennett lesions and the length of time that a patient with a Bennett lesion had played baseball. The prevalence of pathologic lesions detected on MRI and the physical examination results were not different between players with and without Bennett lesions.

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CdTe nanowires were synthesized from individual nanoparticles via self-assembly at a specific wavelength of light. The wavelength of 500 nm resulted in a self-assembly of nanoparticles into nanowires. Most of the produced nanowires were straight and long in shape and their length ranged from 300 nm to 20 µm. The oxidation of Te2- in CdTe nanoparticles under the visible light resulted in the assembly of nanowires consisting of several layers of individual nanoparticles. Transmission electron microscopy and scanning electron microscopy were performed to characterize the synthesized nanostructures. Energy-dispersive X-ray demonstrated the atomic percentage of nanowires. Photoluminescence showed that the wavelength of the nanostructures is slightly blue-shifted from 555 to 548 nm.

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OBJECTIVE: The purpose of this study was to evaluate the antibacterial ability of three MTA (MTA-Angelus, Endocem MTA, and ProRoot MTA) against five typical oral bacteria (Streptococcus mutans, Enterococcus faecalis, Lactobacillus rhamnosus, Lactobacillus paracasei, and Porphyromonas gingivalis). DESIGN: For disc diffusion test, each test material was placed into agar plates after inoculation of each bacterial strain. The zones of inhibition of bacterial growth were then measured. Antibacterial broth test was performed by adding the test material into the media. Colony-forming units were counted after incubation with bacteria. The data were analyzed using ANOVA and the Tukey's test. RESULTS: Disc diffusion test showed that the antibacterial activity against S. mutans, L. rhamnosus, L. paracasei, and P. gingivalis ranked in decreasing order of MTA-Angelus>ProRoot MTA>Endocem MTA (p<0.05). An inhibitory effect against E. faecalis was only observed in Endocem MTA. Antibacterial broth test showed that the antibacterial activity against all bacteria was Endocem MTA>MTA-Angelus>ProRoot MTA (p<0.05). CONCLUSION: Discrepant results were obtained from the disc diffusion and antibacterial broth test, with MTA-Angelus and Endocem MTA being most effective, respectively. Both tests revealed that the most resistant bacteria was E. faecalis, which was not susceptible at all, except to Endocem MTA in disc diffusion test.

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The aim of this study was to compare the coronal sealing ability of six different dental materials: Three MTA-based cements and three established restorative materials by in vitro dye penetration method. For in vitro infiltration experiments, seventy extracted single-rooted human teeth were used. After crowns of teeth were reduced, root canals were prepared, and filled with gutta-percha cone. Teeth were randomly divided into 6 groups with 10 teeth per group. The orifice of each tooth was prepared to 3 mm depth and filled with the following materials: (I) ProRoot WMTA; (II) EndoCem Zr; (III) Angelus White; (IV) LuxaCore; (V) Fuji II LC; and (VI) Elite. After 5,000 cycles of thermocycling between 5°C and 55°C, dye penetration of each specimen was measured. The order of less dye infiltration of coronal filling materials was: ProRoot WMTA

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Cadmium telluride (CdTe) nanowires were successfully synthesized from individual nanoparticles via self-assembly, and the evolutionary process was investigated. The oxidation of tellurium ions in CdTe nanoparticles under dark conditions led to the assembly of straight nanowires made of several layers of individual nanoparticles. Transmission electron microscopy and scanning electron microscopy were performed to characterize the synthesized nanostructures. The length of the NWs assembled from CdTe NPs ranged from 0.5 to 30 µm. Unlike generally prepared NWs, these NWs were made from individual NPs layered on top of each other. Remarkably, the assembly of individual NPs formed bundles during the intermediate steps before they unraveled into individual NWs. Both control of the amount of stabilizer and oxidation of Te ions acted as driving forces to form NWs. Thus, small modifications in synthesis yielded a major difference in the final nanomaterial structure. The suggested synthetic procedure provides a viable pathway for the fabrication of nanomaterials.

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Kinetic studies of the reactions of alpha-chloroacetanilides (YC6H4NRC(=O)CH2Cl; R = H (5) and CH3 (6)) with benzylamines (NH2CH2C6H4X) were carried out in dimethyl sulfoxide at 55.0 degrees C. The Brønsted betaX values were in the range from 0.6 to 0.9 and cross-interaction constants phoXY were positive: phoXY = +0.21 and +0.18 for 5 and 6, respectively. The rates were faster with 6 than with 5 and inverse secondary kinetic isotope effects involving deuterated benzylamine (ND2CH2C6H4X) nucleophiles, kH/kD < 1.0, were obtained. Based on these and other results, a stepwise mechanism with rate-limiting expulsion of the chloride leaving group from a zwitterionic tetrahedral intermediate, T+/-, is proposed. In this mechanism, a prior carbonyl addition to T+/- is followed by a bridged type transition state to expel the chloride. An enolate-like transition state in which the developing negative charge on C(alpha) delocalizes toward the carbonyl group (nC-->pi*(C=O) interaction) is not feasible for the present series of reactions due to a stronger charge transfer involving the lone pair on the anilino nitrogen (nAN-->pi*(C=O) interaction).