ent to detect sacrococcygeal epidural needle placement in dogs. The Institute of Medicine, the National Patient Safety Foundation, and The Joint Commission have advocated for increased systematic care review to inform future quality improvement. Developing a system to efficiently gather meaningful feedback, review care, and identify areas for improvement can take years to construct. Yet, these systems are vital to reducing future medical error. In this article, the authors present a refined intradepartmental system of retrospective care review. The team created and iteratively improved this model for more than 10 years. Herein, key aspects and benefits of the system are described. A successful care review system should include a broad catchment for cases to review, direct input from multidisciplinary staff involved in each case, a standardized evaluation and feedback process, a system to translate identified gaps into practice improvement, and development of a psychologically safe space for discussions to occur. Resources required to build this system include a quality specialist, a panel of physician and nurse reviewers, and administrative assistance. Blinding cases and electronic blinded polling technology can enhance participation and reduce bias in case assessment. The authors believe that this process for care review can help hospital systems of varying resource levels produce high-quality case review and thereby activate practice improvement to prevent downstream medical errors.The authors believe that this process for care review can help hospital systems of varying resource levels produce high-quality case review and thereby activate practice improvement to prevent downstream medical errors. Lateral radiography of the knee joint is frequently performed; however, the retake rate is high owing to positioning errors. read more Therefore, in this study, to reduce the required number and time of image retakes, we developed a system that can classify the tilting directions of lateral knee radiographs and evaluated the accuracy of the proposed method. Using our system, the tilting directions of a lateral knee radiographs were classified into four direction categories. The system was developed by training the DCNN based on 50 cases of Raysum images and tested on three types test dataset; ten more cases of Raysum images, one case of flexed knee joint phantom images and 14 rejected knee joint radiographs. To train a deep convolutional neural network (DCNN), we employed Raysum images created via three-dimensional (3D) X-ray computed tomography (CT); 11 520 Raysum images were created from 60 cases of 3D CT data by changing the projection angles. Thereby, we obtained pseudo images attached with correct labels that are essential for training. The overall accuracy on each test dataset was 88.5±7.0% (mean±standard deviation), 81.4±11.2%, and 73.3±9.2%. The larger the tilting degree of the knee joint, the higher the classification accuracy. DCNN could classify the tilting directions of a knee joint from lateral knee radiographs. Using Raysum images made it possible to facilitate creating dataset for training DCNN. The possibility was indicated for using support system of lateral knee radiographs. The system may also reduce the burden on patients and increase the work efficiency of radiological technologists.The system may also reduce the burden on patients and increase the work efficiency of radiological technologists. The timing of diagnosis of post-hepatectomy acute kidney injury (AKI) has rarely been investigated. The aim of this retrospective study was to reveal the differences between AKI subtypes following hepatectomy, as classified by timing of diagnosis. Post-hepatectomy AKI was classified as very transient AKI (vtAKI; criteria satisfied by the serum creatinine value immediately after surgery) or non-transient AKI (ntAKI; all other AKI types except for vtAKI). Multivariate logistic regression analyses for both AKI types were performed separately to identify differences in known perioperative AKI risk factors. The impacts of each AKI subtype on postoperative complications, hospital stay and renal outcome at discharge were also evaluated. AKI was diagnosed in 135 of 750 patients (18.0%); 82 and 53 patients were classified as vtAKI and ntAKI, respectively. In multivariate analysis, even among the perioperative factors associated with whole AKI, there were distinct relationships depending on vtAKI or ntAKI. Furthermore, only ntAKI was associated with postoperative complications, longer hospital stays and impaired renal function at discharge. Based on the results of this study, future post-hepatectomy AKI studies should only include ntAKI and exclude vtAKI, as vtAKI has minimal clinical impact despite accounting for a significant proportion of AKI patients. None.None.Methylotrophic bacterium Methylorubrum extorquens is a promising microorganism for the production of value-added compounds from methanol. This study focused on the development of a single-cell level biosensor system that detects methanol by using the intrinsic regulatory machinery which responds to the presence of methanol in this bacterium. A green fluorescent protein (GFP) gene located downstream of the promoter region of the serine glyoxylate aminotransferase gene (Psga) or the methanol dehydrogenase subunit 1 precursor gene (PmxaF) was inserted into the chromosome of M. extorquens wild-type strain AM1. The expression of GFP upon methanol exposure was measured by spectrofluorometer and fluorescence-activated cell sorting (FACS). The strain harboring Psga-gfp emitted fluorescence only when methanol was supplied to the culture medium, while the other strain harboring PmxaF-gfp showed high basal fluorescence even in the absence of methanol. The fluorescence intensity of the Psga-gfp strain depended on a methanol concentration higher than 25 μM, and the sensitivity and dose-dependency of this strain were much higher than previous systems using Escherichia coli. The methanol-sensing properties of the engineered M. extorquens strain were comparable to those of a methylotrophic yeast-based biosensor, suggesting the usefulness of methylotrophic microorganisms as platforms for single-cell sensing of C1 compounds. The constructed methanol sensor strain, coupled with flow cytometry techniques, provides a high-throughput and highly sensitive screening method for the selection of functional methanol-producing enzymes.