Model for Predicting In-Hospital Mortality of Physical Trauma Patients Using Artificial Intelligence Techniques: Nationwide Population-Based Study in Korea.

BACKGROUND: Physical trauma-related mortality places a heavy burden on society. Estimating the mortality risk in physical trauma patients is crucial to enhance treatment efficiency and reduce this burden. The most popular and accurate model is the Injury Severity Score (ISS), which is based on the Abbreviated Injury Scale (AIS), an anatomical injury severity scoring system. However, the AIS requires specialists to code the injury scale by reviewing a patient's medical record; therefore, applying the model to every hospital is impossible. OBJECTIVE: We aimed to develop an artificial intelligence (AI) model to predict in-hospital mortality in physical trauma patients using the International Classification of Disease 10th Revision (ICD-10), triage scale, procedure codes, and other clinical features. METHODS: We used the Korean National Emergency Department Information System (NEDIS) data set (N=778,111) compiled from over 400 hospitals between 2016 and 2019. To predict in-hospital mortality, we used the following as input features: ICD-10, patient age, gender, intentionality, injury mechanism, and emergent symptom, Alert/Verbal/Painful/Unresponsive (AVPU) scale, Korean Triage and Acuity Scale (KTAS), and procedure codes. We proposed the ensemble of deep neural networks (EDNN) via 5-fold cross-validation and compared them with other state-of-the-art machine learning models, including traditional prediction models. We further investigated the effect of the features. RESULTS: Our proposed EDNN with all features provided the highest area under the receiver operating characteristic (AUROC) curve of 0.9507, outperforming other state-of-the-art models, including the following traditional prediction models: Adaptive Boosting (AdaBoost; AUROC of 0.9433), Extreme Gradient Boosting (XGBoost; AUROC of 0.9331), ICD-based ISS (AUROC of 0.8699 for an inclusive model and AUROC of 0.8224 for an exclusive model), and KTAS (AUROC of 0.1841). In addition, using all features yielded a higher AUROC than any other partial features, namely, EDNN with the features of ICD-10 only (AUROC of 0.8964) and EDNN with the features excluding ICD-10 (AUROC of 0.9383). CONCLUSIONS: Our proposed EDNN with all features outperforms other state-of-the-art models, including the traditional diagnostic code-based prediction model and triage scale.

A Rare Case of Cardiac Neurofibroma in a Patient with Neurofibromatosis Type 1: Radiologic Findings.

Neurofibromatosis type 1 (NF1) is a relatively common inherited disorder characterized by the formation of neurofibromas, pigmentary abnormalities of the skin, Lisch nodules of the iris, and skeletal abnormalities. Multiple cutaneous neurofibromas are benign nerve sheath tumors and the main manifestation of NF1. Cardiac neurofibroma associated with NF1 is very rare, and few cases have been reported in the literature. Herein, we present the CT and MRI findings of a surgically confirmed left ventricular neurofibroma in a 32-year-old female with NF1.

Proteasomal ATPase-associated factor 1 negatively regulates proteasome activity by interacting with proteasomal ATPases.

The 26S proteasome, composed of the 20S core and the 19S regulatory complex, plays a central role in ubiquitin-dependent proteolysis by catalyzing degradation of polyubiquitinated proteins. In a search for proteins involved in regulation of the proteasome, we affinity purified the 19S regulatory complex from HeLa cells and identified a novel protein of 43 kDa in size as an associated protein. Immunoprecipitation analyses suggested that this protein specifically interacted with the proteasomal ATPases. Hence the protein was named proteasomal ATPase-associated factor 1 (PAAF1). Immunoaffinity purification of PAAF1 confirmed its interaction with the 19S regulatory complex and further showed that the 19S regulatory complex bound with PAAF1 was not stably associated with the 20S core. Overexpression of PAAF1 in HeLa cells decreased the level of the 20S core associated with the 19S complex in a dose-dependent fashion, suggesting that PAAF1 binding to proteasomal ATPases inhibited the assembly of the 26S proteasome. Proteasomal degradation assays using reporters based on green fluorescent protein revealed that overexpression of PAAF1 inhibited the proteasome activity in vivo. Furthermore, the suppression of PAAF1 expression that is mediated by small inhibitory RNA enhanced the proteasome activity. These results suggest that PAAF1 functions as a negative regulator of the proteasome by controlling the assembly/disassembly of the proteasome.