Efficacy and safety of oxiracetam in patients with vascular cognitive impairment: A multicenter, randomized, double-blinded, placebo-controlled, phase IV clinical trial.

BACKGROUND: Oxiracetam may have a modest effect on preventing cognitive decline. Exercise can also enhance cognitive function. This trial aims to investigate the effect of oxiracetam on post-stroke cognitive impairment and explore whether this effect is modified by exercise. Furthermore, the mechanisms that mediate this effect will be investigated through a neural network analysis. METHODS: This is a multicenter, randomized, double-blind, placebo-controlled phase IV trial. Patients who complained of cognitive decline 3 months after stroke and had a high risk of cognitive decline were eligible. Patients were randomly assigned to receive either 800 mg of oxiracetam or placebo twice daily for 36 weeks. After randomization, a predetermined exercise protocol was provided to each participant, and the degree of physical activity was assessed using wrist actigraphy at 4, 12, 24, and 36 weeks. Resting-state functional MRI was obtained in baseline and 36-week follow-up. Co-primary endpoints are changes in the Mini-Mental State Examination and Clinical Dementia Rating-Sum of Boxes. Secondary endpoints include changes in the NINDS-CSN VCIHS-Neuropsychology Protocol, Euro QoL, patient's global assessment, and functional network connectivity. If there is a significant difference in physical activity between the two groups, the interaction effect between physical activity and the treatment group will be examined. A total of 500 patients were enrolled from February 2018, and the last patient's final follow-up was completed in September 2022. CONCLUSION: This trial is meaningful not only to prove the efficacy of oxiracetam, but also evaluate whether exercise can modify the effects of medication and how cognitive function can be restored. Trial registrationhttp://cris.nih.go.kr (KCT0005137).

Prediction of Intracranial Aneurysm Risk using Machine Learning.

An efficient method for identifying subjects at high risk of an intracranial aneurysm (IA) is warranted to provide adequate radiological screening guidelines and effectively allocate medical resources. We developed a model for pre-diagnosis IA prediction using a national claims database and health examination records. Data from the National Health Screening Program in Korea were utilized as input for several machine learning algorithms: logistic regression (LR), random forest (RF), scalable tree boosting system (XGB), and deep neural networks (DNN). Algorithm performance was evaluated through the area under the receiver operating characteristic curve (AUROC) using different test data from that employed for model training. Five risk groups were classified in ascending order of risk using model prediction probabilities. Incidence rate ratios between the lowest- and highest-risk groups were then compared. The XGB model produced the best IA risk prediction (AUROC of 0.765) and predicted the lowest IA incidence (3.20) in the lowest-risk group, whereas the RF model predicted the highest IA incidence (161.34) in the highest-risk group. The incidence rate ratios between the lowest- and highest-risk groups were 49.85, 35.85, 34.90, and 30.26 for the XGB, LR, DNN, and RF models, respectively. The developed prediction model can aid future IA screening strategies.

Author Correction: Vascular tortuosity of the internal carotid artery is related to the RNF213 c.14429G > A variant in moyamoya disease.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Solitary fibrous tumor of the lesser omentum mimicking stomach gastrointestinal stromal tumor.

Solitary fibrous tumor (SFT) is a mesenchymal tumor that rarely occurs in the abdomen. We report a very rare case of an abdominal SFT in the lesser omentum. A 39-year-old Korean man was referred to our center for management of a 9 cm incidental mass in the abdominal space found on a chest computed tomography (CT) during a routine medical examination. He had no symptoms, and there were no specific findings on physical examination. A contrast enhancement CT was performed, and an extraluminal gastrointestinal stromal tumor in the stomach or a pancreatic origin mass was suspected. Surgery was performed and an enclosed mass in the lesser omentum was observed, which was resected completely. The postoperative course was uneventful. Based on microscopy, the omental tumor was diagnosed as SFT.

Vascular tortuosity of the internal carotid artery is related to the RNF213 c.14429G > A variant in moyamoya disease.

Recent studies have implicated RNF213 mutations in the pathogenesis of moyamoya disease (MMD). However, the underlying mechanism of disease development is not fully elucidated. Nonetheless, a possible relationship between vascular morphology and hemodynamics related with MMD has been proposed. Here, we aimed to investigate the relationship between a variant of RNF213 and the morphology of the internal carotid artery (ICA). We enrolled bilateral MMD patients who had undergone genetic testing for RNF213. Patients were divided into mutant and wild-type groups. Six anatomy-specific three-dimensional coordinates were collected using magnetic-resonance angiography. From these, five vectors between two adjacent points and four angles between two adjacent vectors were calculated. The tortuosity was defined as the ratio between the actual and the linear length of the ICAs. Among 58 patients, 44 and 14 belonged to the mutant and wild-type groups, respectively. The tortuosity of ICAs was significantly lower in the mutant group (p = 0.010). The change in blood flow direction was more prominent in the wild-type group (p = 0.002). The tortuosity was significantly lower in MMD patients than normal controls (p < 0.001). Our results indicate that RNF213 could play a role in the lower tortuosity observed in patients with RNF213 mutation.

Machine learning for detecting moyamoya disease in plain skull radiography using a convolutional neural network.

BACKGROUND: Recently, innovative attempts have been made to identify moyamoya disease (MMD) by focusing on the morphological differences in the head of MMD patients. Following the recent revolution in the development of deep learning (DL) algorithms, we designed this study to determine whether DL can distinguish MMD in plain skull radiograph images. METHODS: Three hundred forty-five skull images were collected as an MMD-labeled dataset from patients aged 18 to 50 years with definite MMD. As a control-labeled data set, 408 skull images of trauma patients were selected by age and sex matching. Skull images were partitioned into training and test datasets at a 7:3 ratio using permutation. A total of six convolution layers were designed and trained. The accuracy and area under the receiver operating characteristic (AUROC) curve were evaluated as classifier performance. To identify areas of attention, gradient-weighted class activation mapping was applied. External validation was performed with a new dataset from another hospital. FINDINGS: For the institutional test set, the classifier predicted the true label with 84·1% accuracy. Sensitivity and specificity were both 0·84. AUROC was 0·91. MMD was predicted by attention to the lower face in most cases. Overall accuracy for external validation data set was 75·9%. INTERPRETATION: DL can distinguish MMD cases within specific ages from controls in plain skull radiograph images with considerable accuracy and AUROC. The viscerocranium may play a role in MMD-related skull features. FUND: This work was supported by grant no. 18-2018-029 from the Seoul National University Bundang Hospital Research Fund.

Clinical Significance of Preoperative Virtual Colonoscopy for Evaluation of the Proximal Colon in Patient With Obstructive Colorectal Cancer.

PURPOSE: Virtual colonoscopy is the most recently developed tool for detecting colorectal cancers and polyps, but its effectiveness is limited. In our study, we compared the result of preoperative virtual colonoscopy to result of preoperative and postoperative colonoscopy. We evaluated also the accuracy of preoperative virtual colonoscopy in patients who had obstructive colorectal cancer that did not allow passage of a colonoscope. METHODS: A total of 164 patients who had undergone preoperative virtual colonoscopy and curative surgery after the diagnosis of a colorectal adenocarcinoma between November 2008 and August 2013 were pooled. We compared the result of conventional colonoscopy with that of virtual colonoscopy in the nonobstructive group and the results of preoperative virtual colonoscopy with that of postoperative colonoscopy performed at 6 months after surgery in the obstructive group. RESULTS: Of the 164 patients, 108 were male and 56 were female patients. The mean age was 62.7 years. The average sensitivity, specificity, and accuracy of virtual colonoscopy for all patients were 31.0%, 67.2%, and 43.8%, respectively. In the nonobstructive group, the average sensitivity, specificity, and accuracy were 36.6%, 66.2%, and 48.0%, respectively, whereas in the obstructive group, they were 2%, 72.4%, and 25.4%. Synchronous cancer was detected via virtual colonoscopy in 4 of the 164 patients. CONCLUSION: Virtual colonoscopy may not be an effective method for the detection of proximal colon polyps, but it can be helpful in determining the therapeutic plan when its results are correlated with the results of other studies.

Variable-color light-emitting diodes using GaN microdonut arrays.

Microdonut-shaped GaN/Inx Ga1-x N light-emitting diode (LED) microarrays are fabricated for variable-color emitters. The figure shows clearly donut-shaped light emission from all the individual microdonut LEDs. Furthermore, microdonut LEDs exhibit spatially-resolved blue and green EL colors, which can be tuned by either controlling the external bias voltage or changing the size of the microdonut LED.