OBJECTIVE: The aims of this study were to compare the shear bond strength between orthodontic metal brackets and glazed zirconia using different types of primer before applying resin cement and to determine which primer was more effective. METHODS: Zirconia blocks were milled and embedded in acrylic resin and randomly assigned to one of four groups: nonglazed zirconia with sandblasting and zirconia primer (NZ); glazed zirconia with sandblasting, etching, and zirconia primer (GZ); glazed zirconia with sandblasting, etching, and porcelain primer (GP); and glazed zirconia with sandblasting, etching, zirconia primer, and porcelain primer (GZP). A stainless steel metal bracket was bonded to each target surface with resin cement, and all specimens underwent thermal cycling. The shear bond strength of the specimens was measured by a universal testing machine. A scanning electron microscope, three-dimensional optical surface-profiler, and stereoscopic microscope were used to image the zirconia surfaces. The data were analyzed with one-way analyses of variance and the Fisher exact test. RESULTS: Group GZ showed significantly lower shear bond strength than did the other groups. No statistically significant differences were found among groups NZ, GP, and GZP. All specimens in group GZ showed adhesive failure between the zirconia and resin cement. In groups NZ and GP, bonding failed at the interface between the resin cement and bracket base or showed complex adhesive and cohesive failure. CONCLUSIONS: Porcelain primer is the more appropriate choice for bonding a metal bracket to the surface of a full-contour glazed zirconia crown with resin cement.
Adhesives ; Crowns ; Dental Porcelain* ; Resin Cements ; Stainless Steel

Acetaminophen or paracetamol (N-acetyl-para-aminophenol [APAP]) is a safe and effective antipyretic and analgesic drug and is a representative nonprescription drug. However, APAP is one of the most common nonprescription drugs used for intentional overdose or suicide, thereby resulting in hundreds of deaths annually in the United States. Moreover, the misuse of nonprescription drugs is a cause of increasing concern in Korea with the revision of the Pharmaceutical Affairs Law in 2012. Generally, the mortality rate of APAP overdose is extremely low due to the well-established treatment guidelines and availability of antidotes. However, it should not be overlooked because of the high number of either accidental or intentional APAP overdose cases recorded every day. To achieve a good prognosis, individuals with APAP overdose must be immediately identified and brought to the hospital. Herein, we report an autopsy case of an individual with APAP overdose who died due to acute liver injury.

OBJECTIVE: To evaluate the impact of the adaptive iterative dose reduction (AIDR) three-dimensional (3D) algorithm in CT on noise reduction and the image quality compared to the filtered back projection (FBP) algorithm and to compare the effectiveness of AIDR 3D on noise reduction according to the body habitus using phantoms with different sizes. MATERIALS AND METHODS: Three different-sized phantoms with diameters of 24 cm, 30 cm, and 40 cm were built up using the American College of Radiology CT accreditation phantom and layers of pork belly fat. Each phantom was scanned eight times using different mAs. Images were reconstructed using the FBP and three different strengths of the AIDR 3D. The image noise, the contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR) of the phantom were assessed. Two radiologists assessed the image quality of the 4 image sets in consensus. The effectiveness of AIDR 3D on noise reduction compared with FBP were also compared according to the phantom sizes. RESULTS: Adaptive iterative dose reduction 3D significantly reduced the image noise compared with FBP and enhanced the SNR and CNR (p < 0.05) with improved image quality (p < 0.05). When a stronger reconstruction algorithm was used, greater increase of SNR and CNR as well as noise reduction was achieved (p < 0.05). The noise reduction effect of AIDR 3D was significantly greater in the 40-cm phantom than in the 24-cm or 30-cm phantoms (p < 0.05). CONCLUSION: The AIDR 3D algorithm is effective to reduce the image noise as well as to improve the image-quality parameters compared by FBP algorithm, and its effectiveness may increase as the phantom size increases.
*Algorithms ; Animals ; Body Size ; Image Processing, Computer-Assisted/*methods ; *Phantoms, Imaging/standards ; Radiation Dosage ; Signal-To-Noise Ratio ; Subcutaneous Fat, Abdominal/*radiography ; Swine ; Tomography, X-Ray Computed/*methods

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.