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There seemed to be characteristic risk factors in cirrhotic patients for posthepatectomy complications because these patients have less hepatic reserve as compared with noncirrhotic patients. The aim of the current study was to identify these characteristic risk factors in cirrhotic patients. We performed 419 primary hepatectomies for hepatocellular carcinoma. The patients were divided into the cirrhotic group (n = 198) and the noncirrhotic group (n = 221), and the risk factors for posthepatectomy complications were compared between the groups. Thirty-six cirrhotic patients (18.2%) experienced Clavien's Grade III or more complications. Tumor size, intraoperative blood loss, duration of operation, major hepatectomy (two or more segments), and necessity of blood transfusion were found to be significant risk factors in univariate analyses. Multivariate analysis revealed that major hepatectomy and intraoperative blood loss were independent risk factors for posthepatectomy complications in patients with cirrhosis. On the other hand, the duration of operation was only an independent risk factor for posthepatectomy complication in noncirrhotic patients. Cirrhotic patients should avoid a major hepatectomy and undergo a limited resection preserving as much liver tissue as possible and meticulous surgical procedures to lessen intraoperative blood loss are mandatory to prevent major posthepatectomy complications. [ABSTRACT FROM AUTHOR]

Reconstruction-based change detection methods are robust for camera motion. The methods learn reconstruction of input images based on background images. Foreground regions are detected based on the magnitude of the difference between an input image and a reconstructed input image. For learning, only background images are used. Therefore, foreground regions have larger differences than background regions. Traditional reconstruction-based methods have two problems. One is over-reconstruction of foreground regions. The other is that decision of change detection depends on magnitudes of differences only. It is difficult to distinguish magnitudes of differences in foreground regions when the foreground regions are completely reconstructed in patch images. We propose the framework of a reconstruction-based change detection method for a free-moving camera using patch images. To avoid over-reconstruction of foreground regions, our method reconstructs a masked central region in a patch image from a region surrounding the central region. Differences in foreground regions are enhanced because foreground regions in patch images are removed by the masking procedure. Change detection is learned from a patch image and a reconstructed image automatically. The decision procedure directly uses patch images rather than the differences between patch images. Our method achieves better accuracy compared to traditional reconstruction-based methods without masking patch images. [ABSTRACT FROM AUTHOR]

A thermal camera captures the temperature distribution of a scene as a thermal image. In thermal images, facial appearances of different people under different lighting conditions are similar. This is because facial temperature distribution is generally constant and not affected by lighting condition. This similarity in face appearances is advantageous for face detection. To detect faces in thermal images, cascade classifiers with Haar-like features are generally used. However, there are few studies exploring the local features for face detection in thermal images. In this paper, we introduce two approaches relying on local features for face detection in thermal images. First, we create new feature types by extending Multi-Block LBP. We consider a margin around the reference and the generally constant distribution of facial temperature. In this way, we make the features more robust to image noise and more effective for face detection in thermal images. Second, we propose an AdaBoost-based training method to get cascade classifiers with multiple types of local features. These feature types have different advantages. In this way we enhance the description power of local features. We did a hold-out validation experiment and a field experiment. In the hold-out validation experiment, we captured a dataset from 20 participants, comprising 14 males and 6 females. For each participant, we captured 420 images with 10 variations in camera distance, 21 poses, and 2 appearances (participant with/without glasses). We compared the performance of cascade classifiers trained by different sets of the features. The experiment results showed that the proposed approaches effectively improve the performance of face detection in thermal images. In the field experiment, we compared the face detection performance in realistic scenes using thermal and RGB images, and gave discussion based on the results. [ABSTRACT FROM AUTHOR]

Abstract   Stenotrophomonas maltophilia (previously named Xanthomonas maltophilia) is an aerobic, nonfermentive, Gram-negative bacillus that is widespread in the environment. It is considered to be an organism with a limited pathogenic potential, which is rarely capable of causing diseases in humans other than in those who are in an immunocompromised state. In this study, we outline the case of a patient with Stenotrophomonas maltophilia bacteremia after living donor liver transplantation, which showed the clinical signs of severe sepsis and was resistant to almost all antibiotics. However, we successfully treated the patient with the antibiotics trimethoprim-sulfamethoxazole (TMP/SMX) and minocycline hydrochloride (MINO), and performed endotoxin-absorbing therapy using polymyxin B (PMX) to remove the endotoxin from Gramnegative bacillus as well as continuous hemodiafiltration (CHDF) to remove inflammatory cytokines. To the best of our knowledge, this is the first report on the treatment of Stenotrophomonas maltophilia bacteremia after living donor liver transplantation. [ABSTRACT FROM AUTHOR]