BackgroundMany studies have investigated the clinical benefits of Ologen for trabeculectomy. However, its benefits for Ahmed glaucoma valve (AGV) implantation have not been investigated as extensively. The aim of this study was to compare the 1-year outcomes of AGV implantation with and without Ologen adjuvant for the treatment of refractory glaucoma.MethodsThis retrospective study included a total of 20 eyes of 20 glaucoma patients, who were followed for at least 1-year after undergoing AGV implantation. In 12 eyes of 12 patients, conventional AGV (CAGV) surgery was performed, while in 8 eyes of 8 patients, Ologen-augmented AGV (OAGV) implantation was performed. The outcomes were evaluated according to intraocular pressure (IOP) and the number of IOP-lowering medications. Complete success was defined as IOP ≤ 21 mmHg without medications throughout the 1-year follow-up period, and qualified success was defined as IOP ≤ 21 mmHg with or without medications throughout the 1-year follow-up period.ResultsThe rate of complete success was significantly higher in the OAGV group (50.0%) than in the CAGV group (8.3%) (p = 0.035). There were no significant differences between the two groups in terms of qualified success or incidence of the early hypertensive phase. The IOP changes were similar between the groups within 1-year postoperatively, though the number of IOP-lowering medications was significantly lower in the OAGV group during the early hypertensive phase (p = 0.031, 0.031, and 0.025 at postoperative months 1, 2, and 3, respectively). When subjects were divided into groups according to the occurrence of the early hypertensive phase, the group with early hypertensive phase was more likely to use IOP-lowering medications at postoperative 6 months and 1 year (p = 0.002 and 0.005, respectively).ConclusionsOAGV surgery shows encouraging results for patients with refractory glaucoma, specifically with respect to the achievement of complete success and the reduction of the number of IOP-lowering medications during the early hypertensive phase. Furthermore, our results suggest that occurrence of the early hypertensive phase is predictive of which patients will require IOP-lowering medications at postoperative 6 months and 1 year.

Purpose: Growing evidence supports the efficacy and safety of high-dose-rate (HDR) brachytherapy as a boost or monotherapy in prostate cancer treatment. We initiated a new HDR prostate brachytherapy practice in April 2014. Here, we report the learning experiences, short-term safety, quality, and outcome. Methods and Materials: From April 2014 to December 2017, 164 men were treated with HDR brachytherapy with curative intent. Twenty-eight men (17.1%) underwent HDR brachytherapy as monotherapy, receiving 25 to 27 Gy in 2 fractions. Men treated with HDR brachytherapy as a boost received 19 to 21 Gy in 2 fractions. Fifty-two men (31.7%) had high-risk disease. HDR procedure times, dosimetry, and response were recorded and analyzed. Genitourinary (GU) and gastrointestinal (GI) toxicities were recorded according to the toxicity criteria of the Radiation Therapy Oncology Group. Results: Mean HDR procedure times decreased yearly from 179 minutes in 2014 to 115 minutes in 2017. Median follow-up was 18.6 months (range, 3-55 months). At last review, 79% of patients reported returning to baseline GU status, and 100% of patients noted no change in GI status from their baseline. Four patients experienced acute urinary retention. Treatment planning target volume (PTV) was defined as prostate with margins. Dosimetrically, 97.5% of all HDR implants had PTV D90 100%, 81.5% had PTV V100 95%, 73.6% had maximal urethral doses 120%, and 77.5% had rectal 1 mL dose 70% (all but one 10.8 Gy). The estimated 3-year overall survival was 98.7% (95% confidence interval, 91.4%-99.8%), and disease-free survival was 96.2% (95% confidence interval, 89.5%-98.7%). Conclusions: The low incidence of GU and GI complications in our cohort demonstrates that a HDR brachytherapy program can be successfully developed as a treatment option for patients with localized prostate cancer.

This study examines the forecasting ability of the adjusted implied volatility (AIV), which is suggested by Kang, Kim and Yoon (2009), using the horserace competition with historical volatility, model-free implied volatility, and BS implied volatility in the KOSPI 200 index options market. The adjusted implied volatility is applicable when investors are not risk averse or when underlying returns do not follow a normal distribution. This implies that AIV is consistent with the presence of risk premia for other risk such as volatility risk and jump risk. Using KOSPI 200 index options, it is shown that the AIV outperforms other volatility estimates in terms of the unbiasedness for future realized volatilities as well as the forecasting errors.

BackgroundAlzheimer's disease (AD) is a neurodegenerative disease characterized by the progressive loss of neurons. To predict such progression, deep neural network models with longitudinal data have been used recently. However, there is a limit that they didn't consider graph topology of brain network, while it is well‐known that neurodegeneration progresses along the large‐scale brain network. In this study, we proposed a graph neural network (GNN) based deep recurrent model to predict longitudinal progression of neurodegeneration in mild cognitive impairment (MCI) patients.MethodWe used 1,146 T1‐weighted magnetic resonance imaging from Alzheimer’s Disease Neuroimaging Initiative. The images were of 320 MCI subjects at baseline with longitudinal data at 2‐year intervals. The length of the data ranged from 3 to 7 years, and an average length is 3.58. We extracted cortical thicknesses using FreeSurfer v6.0 and Desikan‐Killiany atlas. Additionally, we acquired diffusion tensor imaging and resting‐state functional magnetic resonance imaging from Human Connectome Project to extract normative averaged structural connectivity (SC) and resting‐state functional connectivity (rsFC). We also used spatial connectivity of which the element for comparison. We adopted graph‐LSTM model in our problem setting: we used mean cortical thickness as node feature and connectivity as adjacency matrix. We performed 5‐fold cross‐validation and evaluated the performance using the test dataset.ResultWe compared performance of our model with two baseline models, which have been used to predict disease progression but couldn’t learn graph topology of brain, such as RNN and LSTM. The loss was significantly lower at our model than at both baseline models by about one seventh. We implemented additional studies using three distinct connectivities as mentioned above. When using SC and rsFC as adjacency matrix, model performances were increased by about one third compared to using spatial connectivity. In addition, raw cortical thicknesses between prediction and observation were not significantly different in most regions except right anterior cingulate in year 8. Conclusions: We predicted progression of neurodegeneration using graph‐LSTM considering brain networks. We found substituting GNN for embedding layers of LSTM improved performance. We also identified utilizing SC and rsFC as underlying structure of neurodegeneration decreased the prediction error.

Background The heterogeneity of Alzheimer’s disease is important theme for studying the epidemiology of the disease. Practically, understanding disease phenotypes helps deciding treatment before proceeding to dementia. Generally, it is known that AD’s subtype can be divided into three. Method We used 660 mild cognitive impairment (MCI) patients’ T1‐weighted magnetic resonance images (MRI) from Alzheimer’s Disease Neuroimaging Initiative (ADNI). Inputs are atrophy vectors which mean excluding the positive part and resigned W‐scores calculated from 81,924 vertex‐level cortical‐thicknesses extracted from T1. Unlike existing clustering‐only or separated clustering‐classification models, we used a model which is jointly optimized with respect to both clustering and classification. The model is comprised of three parts: 1) feature extractor, 2) clustering module, and 3) classifier. In first part, we extract important features using simple neural network. Then, we do clustering using the deep‐features and obtain pseudo‐labels in latent space. Finally, we implement classification at last layer with the answers which are assigned from clustering module. It is end‐to‐end training being able to find the solution of combined objective function. Results Results were represented from 10 fold cross‐validation. Average log‐likelihood of Gaussian mixture and normalized mutual information of labels between adjacent iterations are used as clustering performance metric, cross‐entropy loss and accuracy as classification. It is difficult for such models to converge according to referenced studies. But, in this study, all the metrics were well‐converged. Atrophy maps show group comparison results of cortical thicknesses between each subtype and cognitive normal (CN) patient with FDR correction and W‐score averaged in each subtype. The demographic table about Aβ positivity and dementia conversion percentages without missing information and neuropsychological test scores table of each subtypes showed that subtype‐I has highest degree of disease severity, then subtype‐II and finally III. External validation with 662 aMCI patients’ data from Samsung Medical Center (SMC) used in similar study represented bigger gap of dementia conversion percentages between subtype‐I and II. Conclusions In this study, we made clustering‐classification model for individualized subtype prediction. It is end‐to‐end training method optimizing clustering and classification loss simultaneously. The results showed our model exceeds existing subtype clustering model.