E2-25K/Hip2 is an unusual ubiquitin-conjugating enzyme that interacts with the frameshift mutant of ubiquitin B (UBB ؉1 ) and has been identified as a crucial factor regulating amyloid-␤ neurotoxicity. To study the structural basis of the neurotoxicity mediated by the E2-25K-UBB ؉1 interaction, we determined the three-dimensional structures of UBB ؉1 , E2-25K and the ), and polyglutamine-expanded huntingtin (2). UBB ϩ1 was first identified as a frameshift mutant of the ubiquitin (Ub) B protein in the brains of neurodegenerative disease patients (3) and is composed of a Ub moiety (75 residues) with a 19-residue C-terminal extension. Neither A␤ nor UBB ϩ1 is found in young patients not suffering from dementia, but they are observed in older Alzheimer disease patients (4). The genes from which UBB ϩ1 mRNAs are transcribed contain several GAGAG motifs, and dinucleotide deletions (⌬GA) from within the GAGAG motif result in an abnormal C-terminal sequence. Normally these aberrant proteins are removed by the Ub-proteasome system (UPS), which executes the proteolytic degradation of aberrant proteins via a Ub-tagging mechanism (3, 5). E2-25K/ubiquitin, and E2-25K/UBBWithin the UPS, Ub tagging of target molecules entails enzymatic reactions catalyzed by the E1 (Ub-activating), E2 (Ubconjugating), and E3 (Ub-ligating) enzymes. Once E3 tags a target molecule with mono-or polyUb, the tagged molecule is recognized by the 26 S proteasome and degraded (6). In the healthy brain both ␤-amyloid precursor protein and UBB ϩ1 molecules are targets for the UPS and are degraded by the 26 S proteasome (7,8). In the brains of Alzheimer patients, however, both UBB ϩ1 and Ub are present within aggregation plaques also containing ␤-amyloid precursor protein, which is indicative of UPS dysfunction (9, 10). When at normal basal levels, UBB ϩ1 can be removed by the UPS. But when its expression is up-regulated, UBB ϩ1 inhibits the 26 S proteasome in a dose-dependent manner, resulting in the accumulation of aberrant proteins (11). The aberrant C terminus of UBB ϩ1 prevents its activation and, therefore, subsequent ligation to substrates due to , the frame shift mutant of ubiquitin B; UPS, ubiquitin-proteasome system; HSQC, heteronuclear single quantum correlation; TROSY, transverse relaxation optimized spectroscopy; DsRed, Discosoma sp. red fluorescent protein.

Purpose A blood transfusion after total knee arthroplasty (TKA) is associated with an increase in complication and infection rates. However, no studies have been conducted to predict transfusion after TKA using a machine learning algorithm. The purpose of this study was to identify informative preoperative variables to create a machine learning model, and to provide a web‐based transfusion risk‐assessment system for clinical use. Methods This study retrospectively reviewed 1686 patients who underwent TKA at our institution. Data for 43 preoperative variables, including medication history, laboratory values, and demographic characteristics, were collected. Variable selection was conducted using the recursive feature elimination algorithm. The transfusion group was defined as patients with haemoglobin (Hb) < 7 g/dL after TKA. A predictive model was developed using the gradient boosting machine, and the performance of the model was assessed by the area under the receiver operating characteristic curve (AUC). Data sets from an independent institution were tested with the model for external validation. Results Of the 1686 patients who underwent TKA, 108 (6.4%) were categorized into the transfusion group. Six preoperative variables were selected, including preoperative Hb, platelet count, type of surgery, tranexamic acid, age, and body weight. The predictive model demonstrated good predictive performance using the six variables [AUC 0.842; 95% confidence interval (CI) 0.820–0.856]. Performance was also good according to the external validation using 400 data from an independent institution (AUC 0.880; 95% CI 0.844–0.910). This web‐based blood transfusion risk‐assessment system can be accessed at http://safetka.net. Conclusions A web‐based predictive model for transfusion after TKA using a machine learning algorithm was developed using six preoperative variables. The model is simple, has been validated, showed good performance, and can be used before TKA to predict the risk of transfusion and guide appropriate precautions for high‐risk patients. Level of evidence Diagnostic level II.

PurposeAcute kidney injury (AKI) is a deleterious complication after total knee arthroplasty (TKA). The purposes of this study were to identify preoperative risk factors and develop a web‐based prediction model for postoperative AKI, and assess how AKI affected the progression to ESRD. MethodThe study included 5757 patients treated in three tertiary teaching hospitals. The model was developed using data on 5302 patients from two hospitals and externally validated in 455 patients from the third hospital. Eighteen preoperative variables were collected and feature selection was performed. A gradient boosting machine (GBM) was used to predict AKI. A tenfold‐stratified area under the curve (AUC) served as the metric for internal validation. Calibration was performed via isotonic regression and evaluated using a calibration plot. End‐stage renal disease (ESRD) was followed up for an average of 41.7 months. ResultsAKI develops in up to 10% of patients undergoing TKA, increasing the risk of progression to ESRD. The ESRD odds ratio of AKI patients (compared to non‐AKI patients) was 9.8 (95% confidence interval 4.3–22.4). Six key predictors of postoperative AKI were selected: higher preoperative levels of creatinine in serum, the use of general anesthesia, male sex, a higher ASA class (> 3), use of a renin–angiotensin–aldosterone system inhibitor, and no use of tranexamic acid (all p < 0.001). The predictive performance of our model was good (area under the curve 0.78 [95% CI 0.74–0.81] in the developmental cohort and improved in the external validation cohort (0.89). Our model can be accessed at https://safetka.net. ConclusionsA web‐based predictive model for AKI after TKA was developed using a machine‐learning algorithm featuring six preoperative variables. The model is simple and has been validated to improve both short‐ and long‐term prognoses of TKA patients. Postoperative AKI may lead to ESRD, which surgeons should strive to avoid. Level of evidenceDiagnostic level II.

Objectives: This study aimed to develop and validate a new method for measuring injury severity, the excess mortality ratio-adjusted Injury Severity Score (EMR-ISS), using the International Classification of Diseases 10th Edition (ICD-10).Methods: An injury severity grade similar to the Abbreviated Injury Scale (AIS) was converted from the ICD-10 codes on the basis of quintiles of the EMR for each ICD-10 code. Like the New Injury Severity Score (NISS), the EMR-ISS was calculated from three maximum severity grades using data from the Korean National Injury Database. The EMR-ISS was then validated using the Hosmer-Lemeshow goodness-of-fit chi-square (HL chi-square, with lower values preferable), the area under the receiver operating characteristic curve (AUC-ROC), and the Pearson correlation coefficient to compare it with the International Classification of Diseases 9th Edition-based Injury Severity Score (ICISS). Nationwide hospital discharge abstract data (DAD) from stratified-sample general hospitals (n = 150) in 2004 were used for an external validation.Results: The total number of study subjects was 29,282,531, with five subgroups of particular interest identified for further study: traumatic brain injury (TBI, n = 3,768,670), traumatic chest injury (TCI, n = 1,169,828), poisoning (n = 251,565), burns (n = 869,020), and DAD (n = 26,374). The HL chi-square was lower for EMR-ISS than for ICISS in all groups: 42,410.8 versus 55,721.9 in total injury, 7,139.6 versus 20,653.9 in TBI,6,603.3 versus 4,531.8 Conclusions: The EMR-ISS showed better calibration and discrimination power for prediction of death than the ICISS in most injury groups. The EMR-ISS appears to be a feasible tool for passive injury surveillance of large data sets, such as insurance data sets or community injury registries containing diagnosis codes. Additional further studies for external validation on prospectively collected data sets should be considered.