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Gallbladder cancer is the most common cause of hilar biliary obstruction; however, it rarely causes combined biliary, duodenal, and colon triple obstruction. In this case, the quality of life for a patient with recurrent gallbladder cancer with combined duodenal, colonic, and biliary obstruction was improved by endoscopic and endosonographic palliation, despite its technical difficulty and complexity. Seven metal stents were implanted one by one using only endoscopic methods. Successful stent-in-stent placement and endoscopic ultrasound-guided stenting after failed ERCP improved the patient’s quality of life to the extent that there was no need for any external drainage.

Background Monovalent meningococcal conjugate vaccines against serogroups A or C have resulted in significant reduction of pharyngeal carriage rates against the vaccine serogroups. However, the effect of the quadrivalent meningococcal conjugate vaccine on meningococcal carriage has not yet been evaluated. Methods We assessed changes in pharyngeal carriage rates of Neisseria meningitidis after introduction of the quadrivalent meningococcal conjugate vaccine in Korean military trainees. Two rounds of oropharyngeal swabs of Korean military trainees were obtained, at the beginning and the end of the army training period. The first oropharyngeal swabs were collected prior to vaccination; the second round was performed 5 weeks later. Results In 2013, the overall N. meningitidis carriage rate was higher at the end of the training period (13.8%, 60 of 434 participants) than upon entry into military service (8.9%, 39 of 434 participants). Although the carriage rates of nongroupable and serogroup X isolates increased over the 5-week interval, carriage of the serogroup strains included in the quadrivalent meningococcal vaccine was reduced, from 7 to 2 isolates, after vaccination. However, in 2014, the overall carriage rate of N. meningitidis was significantly lower at the end of the training period (5.2%, 23 of 443 participants) than on entry into military service (12.6%, 56 of 443 participants) (p = 0.028). Carriage of vaccine serogroups isolates was significantly decreased, compared to that of non-vaccine serogroup B over the 5-week interval. Carriage isolates belonging to the ST-178, ST-31 and ST-41/44 were frequently identified clonal complex. Meningococcal carriage rate before and after Meningococcal vaccination in Korean military trainees Conclusion Meningococcal vaccination in Korean military trainees could lead to reduce pharyngeal carriage rates of vaccine serogroup meningococci. Therefore, the quadrivalent meningococcal conjugate vaccine is likely to prevent carriage acquisition of meningococci in closed populations such as military trainees. Disclosures All Authors: No reported disclosures.

Current guidelines for the therapeutic monitoring of vancomycin recommend dosing based on the area under the concentration-time curve (AUC) to achieve clinical efficacy while reducing nephrotoxicity. Although a wide range of nephrotoxicity thresholds have been reported, few studies have documented clinical outcomes based on AUC-guided vancomycin dosing in Korea.The aim of the study was to evaluate whether a relationship exists between AUC and treatment outcomes in vancomycin treated patients in methicillin-resistantThe records of adult patients with methicillin-resistantOf 118 patients, 61 (51.7%) experienced treatment failure and 42 (35.6%) developed acute kidney injury. The vancomycin AUC threshold for predicting acute kidney injury was 615.0 mg· hr/L. In the multivariate analysis, AUC ≥615.0 mg· hr/L was a significant risk factor for nephrotoxicity (adjusted odds ratio [aOR] = 5.24; 95% CI, 1.8-14.65). The lower threshold for treatment failure was not defined because it was not statistically significant. Risk factors for treatment failure included low body mass index (aOR = 0.82; 95% CI, 0.70-0.96), severity of acute illness represented by complicated infection (aOR = 77.56; 95% CI, 16.7-359.4) and comorbidities, such as solid organ tumors (aOR = 6.61; 95% CI, 1.19-36.81) and cerebrovascular disease (aOR = 6.05; 95% CI, 1.17-31.23).Although AUC-guided vancomycin dosing was associated with a reduced risk of acute kidney injury, its ability to predict clinical outcomes was modest. Further studies are needed to define the AUC therapeutic range to maximize efficacy and minimize nephrotoxicity. (

Although radar and AIS alarm functions have been used as support measures for collision prevention in many ships, they lacked the function to identify actual collision risk ships and inform the degree of risk and did not provide information on avoidance routes. This study aims to design a newly developed safer new optimal route for anti-collision based on the Collision Risk Zone assessment method, targeting mainly very small-sized ships that caused most of the collision accidents due to human errors. Designing a safer new optimal route is composed of three segments according to the ship’s characteristics. That is, empirical method for small-sized ships, collision risk zone method for larger ships and then combination method between empirical and collision risk method. In addition, this study analyzed the collision risk of various scenarios according to the navigation relationships and speed conditions between the two meeting ships. As a result, the model proposed in this study numerically calculated the risk of collision according to the navigation situation and could provide a safe optimal avoidance path accordingly. Therefore, this model can be applied to build the collision avoidance model of not only the current ship but remote-controlled ship, which is under compliance with COLREG regulations and involves the possibility of human error in an operation.

Vessels experience additional resistance by waves during navigation, which becomes a factor that increases energy consumption and exhaust gas emissions. Proper estimation and understanding of this additional resistance is an important task in the marine industry. In this study, we propose a machine-learning model that predicts added resistance in arbitrary wave headings using basic ship parameters. First, extensive model experimental data on added resistance for different ship types and sizes of ships were acquired. To build a proper machine learning model, algorithms such as extreme gradient boosting (XGB), random forest (RF), artificial neural network (ANN), k-nearest neighbor (ANN), gaussian process regression (GPR), and support vector regression (SVR) were considered. Through nested cross-validation, the evaluation and hyperparameter tuning of algorithms were performed together. As a result, SVR was selected among the candidate models due to high accuracy with robustness to the outliers. In the validation with test data of head waves and all wave headings, the R2 scores of the selected model were 0.6738–0.7584 and 0.6744–0.7449, respectively, which was better than estimation methods for added resistance in head waves such as STAWAVE-2 and Cepowski (2020), and similar accuracy to those applicable in arbitrary wave headings. Even estimation of added resistance in irregular waves of sea states, the relative deviation with the semi-empirical methods for arbitrary waves was not large, on average 10%.

In this paper, we perform a comprehensive study of various semi-empirical methods using publicly accessible experimental data on added resistance in waves with different ship types and conditions. Based on the analysis results, a new method (So-called “Combined method”) is proposed, combining two existing methods, which are available in arbitrary wave headings. The results from the two methods are combined smoothly using a tangent hyperbolic function according to wavelengths and wave headings. The coefficients constituting the function are tuned to minimize mean squared error between predictions and model experiments. Finally, the new Combined method is verified by full-scale measurements of a general cargo ship and a container ship, and it seems to give good agreement with measurements in all analysis areas, compared to existing semi-empirical methods. Especially, it showed better performance in estimating added wave resistance at high waves, resonance frequencies, arbitrary waves, and low speeds.

As interest in eco-friendly ships increases, methods for status monitoring and forecasting using in-service data from ships are being developed. Models for predicting the energy efficiency of a ship in real time need to effectively process the operational data and be optimized for such an application. This paper presents models that can predict fuel consumption using in-service data collected from a 13,000 TEU class container ship, along with statistical and domain-knowledge methods to select the proper input variables for the models. These methods prevent overfitting and multicollinearity while providing practical applicability. To implement the prediction model, either an artificial neural network (ANN) or multiple linear regression (MLR) were applied, where the ANN-based models showed the best prediction accuracy for both variable selection methods. The goodness of fit of the models based on ANN ranged from 0.9709 to 0.9936. Furthermore, sensitivity analysis of the draught under normal operating conditions indicated an optimal draught of 14.79 m, which was very close to the design draught of the target ship, and provides the optimal fuel consumption efficiency. These models could provide valuable information for ship operators to support decision making to maintain efficient operating conditions.

Vasoreactivity to hypercapnia has been used for assessing cerebrovascular tone and control altered by ischemic stroke. Despite the high prognostic potential, traits of hypercapnia-induced hemodynamic changes have not been fully characterized in relation with baseline vascular states and brain tissue damage. To monitor cerebrovascular responses, T2- and T2∗-weighted magnetic resonance imaging (MRI) images were acquired alternatively using spin- and gradient-echo echo plannar imaging (GESE EPI) sequence with 5% CO2gas inhalation in normal ( n = 5) and acute stroke rats ( n = 10). Dynamic relative changes in cerebrovascular volume (CBV), microvascular volume (MVV), and vascular size index (VSI) were assessed from regions of interest (ROIs) delineated by the percent decrease of apparent diffusion coefficient (ADC). The baseline CBV was not affected by middle cerebral artery occlusion (MCAO) whereas the baseline MVV in ischemic areas was significantly lower than that in the rest of the brain and correlated with ADC. Vasoreactivity to hypercapnic challenge was considerably attenuated in the entire ipsilesional hemisphere including normal ADC regions, in which unsolicited, spreading depression-associated increases of CBV and MVV were observed. The lesion-dependent inhomogeneity in baseline MVV indicates the effective perfusion reserve for accurately delineating the true ischemic damage while the cascade of neuronal depolarization is probably responsible for the hemispherically lateralized changes in overall neurovascular physiology.