Effect of Various Toothpaste Tablets on Gloss and Surface Roughness of Resin-based Composite Materials

OBJECTIVES

To evaluate the effect of various toothpaste tablets on gloss and surface roughness of resin-based composite.

METHODS AND MATERIALS

Sixty-four resin-based composite specimens were divided into four groups of 16 specimens each. Gloss and roughness were measured before and after simulated brushing with three types of toothpaste tablets and one conventional toothpaste: CT: Chewtab Toothpaste Tablets; AT: Anticavity Toothpaste Tablets; HC: Charcoal Toothpaste Tablets; CP: Cavity Protection toothpaste. The Kruskal- Wallis procedure was performed to compare the differences by groups. Post-hoc comparisons were conducted with Bonferroni corrections (α=0.05).

RESULTS

There was a significant drop in gloss for all groups. CT and AT maintained the highest gloss with means of 81.6 GU and 74.1 GU, respectively. The lowest gloss of 24.5 GU was observed for HC. There was a significant increase in roughness for all groups except for CT. CT had the lowest roughness with a mean of 0.034 μm, while HC had the highest roughness with a mean of 0.074 μm. There was a significant correlation between post-brushing gloss and post-brushing roughness (p<0.001, r=-0.884).

CONCLUSION

Chewtab Toothpaste Tablets had the least effect on gloss and roughness, while Charcoal Toothpaste Tablets had the most negative effect on the surface properties of resin-based composites.

Prediction of neovascular age-related macular degeneration recurrence using optical coherence tomography images with a deep neural network

Neovascular age-related macular degeneration (nAMD) can result in blindness if left untreated, and patients often require repeated anti-vascular endothelial growth factor injections. Although, the treat-and-extend method is becoming popular to reduce vision loss attributed to recurrence, it may pose a risk of overtreatment. This study aimed to develop a deep learning model based on DenseNet201 to predict nAMD recurrence within 3 months after confirming dry-up 1 month following three loading injections in treatment-naïve patients. A dataset of 1076 spectral domain optical coherence tomography (OCT) images from 269 patients diagnosed with nAMD was used. The performance of the model was compared with that of 6 ophthalmologists, using 100 randomly selected samples. The DenseNet201-based model achieved 53.0% accuracy in predicting nAMD recurrence using a single pre-injection image and 60.2% accuracy after viewing all the images immediately after the 1st, 2nd, and 3rd injections. The model outperformed experienced ophthalmologists, with an average accuracy of 52.17% using a single pre-injection image and 53.3% after examining four images before and after three loading injections. In conclusion, the artificial intelligence model demonstrated a promising ability to predict nAMD recurrence using OCT images and outperformed experienced ophthalmologists. These findings suggest that deep learning models can assist in nAMD recurrence prediction, thus improving patient outcomes and optimizing treatment strategies.

Developing and Evaluating an AI-Based Computer-Aided Diagnosis System for Retinal Disease: Diagnostic Study for Central Serous Chorioretinopathy

BACKGROUND

Although previous research has made substantial progress in developing high-performance artificial intelligence (AI)-based computer-aided diagnosis (AI-CAD) systems in various medical domains, little attention has been paid to developing and evaluating AI-CAD system in ophthalmology, particularly for diagnosing retinal diseases using optical coherence tomography (OCT) images.

OBJECTIVE

This diagnostic study aimed to determine the usefulness of a proposed AI-CAD system in assisting ophthalmologists with the diagnosis of central serous chorioretinopathy (CSC), which is known to be difficult to diagnose, using OCT images.

METHODS

For the training and evaluation of the proposed deep learning model, 1693 OCT images were collected and annotated. The data set included 929 and 764 cases of acute and chronic CSC, respectively. In total, 66 ophthalmologists (2 groups: 36 retina and 30 nonretina specialists) participated in the observer performance test. To evaluate the deep learning algorithm used in the proposed AI-CAD system, the training, validation, and test sets were split in an 8:1:1 ratio. Further, 100 randomly sampled OCT images from the test set were used for the observer performance test, and the participants were instructed to select a CSC subtype for each of these images. Each image was provided under different conditions: (1) without AI assistance, (2) with AI assistance with a probability score, and (3) with AI assistance with a probability score and visual evidence heatmap. The sensitivity, specificity, and area under the receiver operating characteristic curve were used to measure the diagnostic performance of the model and ophthalmologists.

RESULTS

The proposed system achieved a high detection performance (99% of the area under the curve) for CSC, outperforming the 66 ophthalmologists who participated in the observer performance test. In both groups, ophthalmologists with the support of AI assistance with a probability score and visual evidence heatmap achieved the highest mean diagnostic performance compared with that of those subjected to other conditions (without AI assistance or with AI assistance with a probability score). Nonretina specialists achieved expert-level diagnostic performance with the support of the proposed AI-CAD system.

CONCLUSIONS

Our proposed AI-CAD system improved the diagnosis of CSC by ophthalmologists, which may support decision-making regarding retinal disease detection and alleviate the workload of ophthalmologists.

Passivation effect on Cd0.95Mn0.05Te0.98Se0.02 radiation detection performance

CdTe-based detectors have the problem of Te-rich surface layers caused by Br etching, which is one of fabrication steps. Te-rich layer acts as a trapping center and serves as an additional source of charge carriers, thereby degrading transport property of charge carriers and enriching leakage current on surface of detector. To solve this problem, we introduced sodium hypochlorite (NaOCl) as a passivant, and investigated its effect on Cd0.95Mn0.05Te0.98Se0.02 (CMTS), by analyzing chemical state of surface and its performance. After passivation with NaOCl, the results of X-ray photoelectron spectroscopy (XPS) shows the formation of tellurium oxide and elimination of water on CMTS surface, and CMTS presented enhanced performance with Am-241 radioisotope. Consequently, it is demonstrated that the passivation with NaOCl reduces leakage current, compensates defect, and elevates transport of charge carriers, thereby decreasing charge loss of carriers and improving performance of CMTS detector.

Classifying neovascular age-related macular degeneration with a deep convolutional neural network based on optical coherence tomography images

Neovascular age-related macular degeneration (nAMD) is among the main causes of visual impairment worldwide. We built a deep learning model to distinguish the subtypes of nAMD using spectral domain optical coherence tomography (SD-OCT) images. Data from SD-OCT images of nAMD (polypoidal choroidal vasculopathy, retinal angiomatous proliferation, and typical nAMD) and normal healthy patients were analyzed using a convolutional neural network (CNN). The model was trained and validated based on 4749 SD-OCT images from 347 patients and 50 healthy controls. To adopt an accurate and robust image classification architecture, we evaluated three well-known CNN structures (VGG-16, VGG-19, and ResNet) and two customized classification layers (fully connected layer with dropout vs. global average pooling). Following the test set performance, the model with the highest classification accuracy was used. Transfer learning and data augmentation were applied to improve the robustness and accuracy of the model. Our proposed model showed an accuracy of 87.4% on the test data (920 images), scoring higher than ten ophthalmologists, for the same data. Additionally, the part that our model judged to be important in classification was confirmed through Grad-CAM images, and consequently, it has a similar judgment criteria to that of ophthalmologists. Thus, we believe that our model can be used as an auxiliary tool in clinical practice.

Assessing central serous chorioretinopathy with deep learning and multiple optical coherence tomography images

Central serous chorioretinopathy (CSC) is one of the most common macular diseases that can reduce the quality of life of patients. This study aimed to build a deep learning-based classification model using multiple spectral domain optical coherence tomography (SD-OCT) images together to diagnose CSC. Our proposed system contains two modules: single-image prediction (SIP) and a final decision (FD) classifier. A total of 7425 SD-OCT images from 297 participants (109 acute CSC, 106 chronic CSC, 82 normal) were included. In the fivefold cross validation test, our model showed an average accuracy of 94.2%. Compared to other end-to-end models, for example, a 3D convolutional neural network (CNN) model and a CNN-long short-term memory (CNN-LSTM) model, the proposed system showed more than 10% higher accuracy. In the experiments comparing the proposed model and ophthalmologists, our model showed higher accuracy than experts in distinguishing between acute, chronic, and normal cases. Our results show that an automated deep learning-based model could play a supplementary role alongside ophthalmologists in the diagnosis and management of CSC. In particular, the proposed model seems clinically applicable because it can classify CSCs using multiple OCT images simultaneously.

Distinguishing retinal angiomatous proliferation from polypoidal choroidal vasculopathy with a deep neural network based on optical coherence tomography

This cross-sectional study aimed to build a deep learning model for detecting neovascular age-related macular degeneration (AMD) and to distinguish retinal angiomatous proliferation (RAP) from polypoidal choroidal vasculopathy (PCV) using a convolutional neural network (CNN). Patients from a single tertiary center were enrolled from January 2014 to January 2020. Spectral-domain optical coherence tomography (SD-OCT) images of patients with RAP or PCV and a control group were analyzed with a deep CNN. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC) were used to evaluate the model's ability to distinguish RAP from PCV. The performances of the new model, the VGG-16, Resnet-50, Inception, and eight ophthalmologists were compared. A total of 3951 SD-OCT images from 314 participants (229 AMD, 85 normal controls) were analyzed. In distinguishing the PCV and RAP cases, the proposed model showed an accuracy, sensitivity, and specificity of 89.1%, 89.4%, and 88.8%, respectively, with an AUROC of 95.3% (95% CI 0.727-0.852). The proposed model showed better diagnostic performance than VGG-16, Resnet-50, and Inception-V3 and comparable performance with the eight ophthalmologists. The novel model performed well when distinguishing between PCV and RAP. Thus, automated deep learning systems may support ophthalmologists in distinguishing RAP from PCV.

Fast-ion Dα spectroscopy diagnostic at KSTAR

A fast-ion Dα (FIDA) diagnostics system was installed for core and edge measurements on KSTAR. This system has two tangential FIDA arrays that cover both blue- and redshifted Dα lines (cold: 656.09 nm) in active views along the neutral beam 1 A centerline. The spectral band is 647-662.5 nm, and it covers the Doppler shift of the emission from the maximum energy of the neutral beam (100 keV). A curved filter strip with a motorized stage adequately prevents saturation of the electron multiplying charge-coupled device signal by the cold Dα line from the plasma edge. From comparisons of the measured spectra and FIDASIM modeling code, the FIDA spectra are well matched quantitatively. Moreover, the first measurements show that the FIDA radiance agrees with the neutron rate in the time trace during external heating and perturbation. In addition, responses are observed in the core FIDA radiance during the edge-localized mode cycle.

Demonstration of a ring-FEL as an EUV lithography tool

This paper presents the required structure and function of a ring-FEL as a radiation source for extreme ultraviolet radiation lithography (EUVL). A 100 m-long straight section that conducts an extremely low emittance beam from a fourth-generation storage ring can increase the average power at 13.5 nm wavelength to up to 1 kW without degrading the beam in the rest of the ring. Here, simulation results for a ring-FEL as a EUVL source are described.

A166 ANALYSIS OF SYMPTOMS, DIAGNOSTIC PATTERNS, AND CANADIAN PROVIDER PERSPECTIVE OF ACUTE HEPATIC PORPHYRIA

Background

Acute hepatic porphyria (AHP) is a family of rare genetic diseases, the most common being acute intermittent porphyria (AIP). AHP results from enzyme deficiencies involved in haem synthesis, leading to accumulation of neurotoxic haem intermediates, aminolaevulinic acid (ALA) and porphobilinogen (PBG), causing potentially life-threatening attacks and chronic symptoms. Patients afflicted by AHP often remain without a proper diagnosis for up to 15 years due to lack of awareness and testing. First-line diagnostic biochemical tests include measuring spot urinary ALA and PBG as both are elevated in the majority of AHP patients.

Aims

The study aimed to describe physicians experience diagnosing AHP and characterize patients globally, including Canada.

Methods

Physicians (n=175) who actively managed AHP patients (with and without recurrent attacks) in the preceding year were recruited from 9/2017–10/2017 to complete an online survey collecting information on demographics, familiarity with AHP and diagnostic tests, perspective on symptoms important to diagnosis, referral patterns, and treatment preferences. Physicians also completed a chart review of 546 patients and reported anonymized data on demographics, medical history, attacks, and symptoms. Data was analysed using descriptive statistics.

Results

Canadian physicians (n=15) practiced a mean of 19.7 years, 67% worked in community settings, and 53% were gastroenterologists. Symptoms informing AHP diagnosis included fatigue (93%), sensory loss (87%), mental confusion (87%), Abdominal pain (80%), red/dark urine (80%), vomiting (73%). AHP diagnostic tests considered informative for diagnosis included urinary ALA (87%) and PBG (80%); however, several non-specific tests were also commonly considered informative of AHP. Chronic symptoms reported included fatigue (75%), nausea/vomiting (70%), weakness (66%), pain (58%), anxiety (54%), diarrhea (41%), constipation (40%). Canadian physicians reported a mean of 58% AHP patients they manage being initially misdiagnosed. Global patients (n=546) were aged 40 years (mean), female (52%), with AIP (83%). Canadian patients (n=38) were aged 41 years (mean), female (61%), with AIP (78%). Patients had mean of 3.4 attacks and 1.6 hospitalizations in the past year.

Conclusions

This study highlights the challenges diagnosing AHP due to non-specificity of symptoms and limited understanding of diagnostic procedures. Due to the frequent presentation of gastrointestinal symptoms, AHP should be included in gastroenterologists’ differential diagnosis of patients presenting with non-specific abdominal pain. Among patients diagnosed with AHP, both acute attacks and chronic symptoms were reported, implicating both in the disease.

Funding Agencies

Alnylam Pharmaceuticals

P3466Cardiac output in end-stage liver disease increases with the severity of liver dysfunction

Background

End-stage liver disease is associated with significant systemic and haemodynamic alterations that affect cardiac function. Cirrhotic cardiomyopathy remains an ill-defined entity among cardiologists. Understanding the complex interplay between liver dysfunction and cardiac function can lead to a better understanding of the compensatory mechanisms of the heart in liver failure.

Purpose

To investigate whether severity of liver disease affects baseline cardiac output in a large contemporary cohort of patients undergoing liver transplant work-up.

Methods

Consecutive patients that underwent pre-liver transplant (LT) workup between 2010–2017 were included. All patients underwent a resting echocardiogram. Cardiac output (CO) was prospectively recorded at baseline by pulsed-wave Doppler examination of the left ventricular outflow tract from the apical window and systemic vascular resistance (SVR) was calculated as 80 x (mean arterial pressure (MAP)/CO). Severity of liver disease was characterized by the model of end-stage liver disease (MELD) and Child-Pugh scores.

Results

560 patients were included (mean age 57.5±7.7, 74.8% male). Mean MELD score was 19±7 and Child-Pugh Score was 9±3. There was an inverse linear relationship between the severity of liver disease by the MELD score and baseline SVR (rho 0.40, P<0.001). As SVR reduced, there was also a significant rise in baseline CO with a strong inverse correlation between the two variables (rho 0.86, p<0.001). There was a significant linear correlation between the severity of liver disease and baseline CO with both the scores (MELD Score rho 0.42, p<0.001; Child Pugh rho 0.44, p<0.001) (Figure).

Baseline CO in LT Patients by Severity

Conclusions

Baseline CO increased with the severity of liver dysfunction due to a reduced afterload. A higher resting CO may lead to patients encroaching on their cardiac reserve at rest. This provides a pathophysiological insight suggesting a limited role for beta-blockers, particularly in patients with advanced liver cirrhosis.

P3465Inducible left ventricular outflow tract obstruction is associated with a higher incidence of perioperative cardiac arrest in liver transplantation

Background

Inducible left ventricular outflow tract obstruction (LVOTO) is not infrequently encountered in liver transplant (LT) candidates undergoing cardiac workup. While the impact of LVOTO on adverse cardiovascular haemodynamics is well reported, it is unclear whether it predisposes to perioperative cardiovascular complications following LT.

Purpose

To investigate the effect of inducible left ventricular outflow tract obstruction on perioperative cardiovascular complications in a modern cohort of liver transplant patients.

Methods

Consecutive patients undergoing dobutamine stress echocardiography (DSE) were evaluated from a quaternary LT centre between 2010 and 2017. Inducible LVOTO was defined as LVOT gradient ≥36mmHg. Perioperative major adverse cardiovascular events (MACE) at 30 days and all-cause death were recorded from a prospectively maintained transplantation database and augmented by electronic medical record review.

Results

We evaluated 560 patients who underwent DSE as part of a workup for LT. Of these, 319 progressed to transplant. Inducible LVOTO was observed in 68 patients (21.3%). A higher baseline cardiac output (7.7 L/min vs 7.0 L/min, p=0.002) predicted for development of inducible LVOTO. Seventy-seven patients (4.1%) experienced a MACE including five deaths, 19 cases of heart failure, 11 cardiac arrests, 10 acute coronary syndromes and 46 cases of arrhythmias (VT/AF). Overall MACE occurred in 17/68 patients (25.0%) with LVOTO and 60/251 patients (23.9%) without LVOTO (p=0.85). However, there was a significantly increased risk of resuscitated perioperative cardiac arrest in patients with LVOTO (7.4% vs 2.4%, p=0.04). Patients with LVOTO also required significantly greater volumes of fluid intra-operatively (8.37L vs. 6.71L, p=0.043).

Cardiac Arrest in LT Patients with LVOTO

Conclusions

Inducible LVOTO is a frequent finding occurring in 21.3% of LT candidates. Despite higher intraoperative fluid resuscitation, LVOTO increased the risk of a perioperative cardiac arrest. Further studies are required to confirm these findings and to assess whether patients with LVOTO undergoing liver transplantation may benefit from heightened perioperative surveillance.

Airflow limitation and survival after surgery for non-small cell lung cancer: Results from a systematic review and lung cancer screening trial (NLST-ACRIN sub-study)

OBJECTIVE

Lung cancer remains the single greatest cause of cancer mortality where surgery for early stage non-small cell lung cancer achieves the greatest survival. While there is growing optimism for better outcomes with screening using annual computed tomography, the impact of co-existing airflow limitation on survival remains unknown. To compare survival in non-small cell lung cancer patients undergoing surgery stratified according to the presence or absence of pre-surgery airflow limitation.

MATERIALS AND METHODS

We undertook a systematic literature search of non-screen lung cancer that encompassed studies reported between January 1946 and January 2017. Full-text articles were identified following eligibility scoring, with data extracted and analysed using a standardised analytical method (PRISMA). The results of this systematic review in non-screen lung cancers were compared to real-world results from a lung cancer screening cohort (N = 10,054), where outcomes following surgery could be compared after stratification according to pre-surgery airflow limitation.

RESULTS

In the systematic review, 6899 subjects were included from 10 studies; 7 were retrospective, 3 were prospective. Overall survival was 950 (44%) in 2144 people with COPD and 2597 (55%) from 4755 controls (unadjusted P value <0.001). However, the overall meta-analysed random effects odds ratio for overall survival (N = 10) and 5-year survival (N = 4) comparing those with and without COPD was 0.91 (95% CI = 0.84-1.00) and 0.99 (95% CI = 0.79-1.24) respectively. There were no signs of significant heterogeneity (I² = 19.1%, P = 0.27) nor publication bias as assessed by funnel plot and Egger's test (P = 0.19). In the lung cancer screening sub-study of 10,054 screening participants we found no difference in 5-year survival in those with and without airflow limitation (84% and 81% respectively, P = 0.64).

CONCLUSION

Survival after surgery for non-small cell lung cancer is comparable between those with and without spirometry evidence of airflow limitation. This finding was replicated in lung cancer diagnosed during screening.

Diagnosis of traumatic brain injury using miRNA signatures in nanomagnetically isolated brain-derived extracellular vesicles

The accurate diagnosis and clinical management of traumatic brain injury (TBI) is currently limited by the lack of accessible molecular biomarkers that reflect the pathophysiology of this heterogeneous disease. To address this challenge, we developed a microchip diagnostic that can characterize TBI more comprehensively using the RNA found in brain-derived extracellular vesicles (EVs). Our approach measures a panel of EV miRNAs, processed with machine learning algorithms to capture the state of the injured and recovering brain. Our diagnostic combines surface marker-specific nanomagnetic isolation of brain-derived EVs, biomarker discovery using RNA sequencing, and machine learning processing of the EV miRNA cargo to minimally invasively measure the state of TBI. We achieved an accuracy of 99% identifying the signature of injured vs. sham control mice using an independent blinded test set (N = 77), where the injured group consists of heterogeneous populations (injury intensity, elapsed time since injury) to model the variability present in clinical samples. Moreover, we successfully predicted the intensity of the injury, the elapsed time since injury, and the presence of a prior injury using independent blinded test sets (N = 82). We demonstrated the translatability in a blinded test set by identifying TBI patients from healthy controls (AUC = 0.9, N = 60). This approach, which can detect signatures of injury that persist across a variety of injury types and individual responses to injury, more accurately reflects the heterogeneity of human TBI injury and recovery than conventional diagnostics, opening new opportunities to improve treatment of traumatic brain injuries.

Sexually Violent Predator Law in the United States

Sexually violent predator (SVP) laws in the United States are controversial. They tend to be opposed by academics, libertarians, and professional organisations but are supported by states and the Supreme Court. This study reviews the history of SVP legislation, compares features of SVP laws among states, and summarise requirements by the Supreme Court that shaped these laws. Features of SVP laws include identification of sexual offenders with mental abnormality that predisposes them to sexually offending behaviours in the future, and standards of proof for conditional or unconditional release. A comprehensive understanding of all statutes can inform policymakers about SVP laws, whether supportive or restrictive of such legislation.

Considerations of the q-profile control in KSTAR for advanced tokamak operation scenarios

The q-profile control is essential for tokamaks exploring the advanced tokamak scenarios, which is expected to be able to provide a possible route toward a steady-state high performance operation in a fully non-inductive current drive state. This is because the pressure and current profiles must remain optimal for the scenario during the injection of large amounts of heating and current drive. Here, essential tools for the q-profile control are the motional Stark effect diagnostic for measuring the radial magnetic pitch angle profile and a state-of-the-art plasma control system. The pulse duration of the H-mode discharge at KSTAR has been extended year by year with improved control performance, and the experiment of internal transport barrier (ITB) formation in a weakly reversed q-profile with a marginal neutral beam injection majority heating successfully demonstrated that the ITB is an alternative candidate to achieve a high performance regime in KSTAR. These recent achievements are attributed to reliable profile measurement, which means that profile feedback control has become a necessary step to ensure a robust and reliable approach to advanced scenarios as the next step of research in KSTAR. In this paper, we discuss the technical and conceptual requirements for q-profile control according to the upgrade plan for heating and current drive systems in the coming years.

Four-dimensional calibration turntable of the motional Stark effect diagnostic on EAST

The motional Stark effect (MSE) diagnostic is applied to measure the safety factor q and current density profile of a tokamak device, which are important parameters in realizing the high-performance and long-pulse steady state of a tokamak. A single-channel MSE diagnostic based on dual photoelastic modulators, whose sightline meets with the neutral beam injection at a major radius of R = 2.12 m, has been built for the D window of the Experimental Advanced Superconducting Tokamak (EAST). According to the requirements of MSE diagnostic polarimetric calibration, a high-precision four-dimensional calibration turntable, driven by four stepping motors and controlled by software running on the computer, was designed for EAST. The turntable allows us to rapidly calibrate the MSE diagnostic in a series of positions and angles during EAST maintenance. The turntable can move in four dimensions of translation, yaw, pitch, and roll of the polarizer and can create linearly polarized light at any given angle with accuracy of ∼0.05° for the MSE system offline calibration. The experimental results of the MSE diagnostic calibration in the laboratory show that the turntable has the advantages of high positioning accuracy, flexible spatial movement, and convenient control and fully meets the calibration requirements of an MSE diagnosis system.