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Inflammatory bowel disease (IBD), caused by environmental factors associated with the host’s genetic traits, is represented by Crohn’s disease and ulcerative colitis. Despite the increasing number of patients with IBD, the current treatment is limited to symptomatic therapy. A complex IBD model mimicking the human IBD etiology is required to overcome this limitation. Herein, we developed novel complex IBD models using interleukin 2 receptor subunit gamma (Il2rg)-deficient mice, high-fat diet, dextran sodium sulfate, and Citrobacter rodentium . The more IBD factors applied complexly, colon length shortened and inflammation worsened. The levels of pro-inflammatory cytokines increased with increased IBD factors. Anti-inflammatory cytokine decreased in all factors application but increased in Il2rg deficiency and Westernized diet combination. Additionally, the pro-inflammatory transcription factors and leaky intestinal epithelial marker were upregulated by a combination of IBD factors. Species diversity decreased with IBD factors. Phylogenetic diversity decreased as IBD factors were applied but increased with combined Il2rg deficiency and Westernized diet. The more IBD factors applied complexly, the more severe the dysbiosis. Finally, we developed a novel complex IBD model using various IBD factors. This model more closely mimic human IBD based on colonic inflammation and dysbiosis than the previous models. Based on these results, our novel complex IBD model could be a valuable tool for further IBD research.

Carcinogenicity tests predict the tumorigenic potential of various substances in the human body by studying tumor induction in experimental animals. There is a need for studies that explore the use of FVB/N-Trp53 em2Hwl /Korl (FVB-Trp53 +/- ) mice, created by TALEN-mediated gene targeting in Korea, in carcinogenicity tests. This study was performed to determine whether FVB-Trp53 +/- mice are a suitable model for short-term carcinogenicity studies. To compare the carcinogenicity at different concentrations, 25, 50, and 75 mg/kg of N-methyl-N-nitrosourea (MNU), a known carcinogen, were administered intraperitoneally to FVB-Trp53 +/- and wild-type male mice. After 26 weeks, the survival rate was significantly reduced in FVB-Trp53 +/- mice compared to the wild-type mice in the 50 and 75 mg/kg groups. The incidence of thymic malignant lymphoma (TML) in the 50 and 75 mg/kg groups was 54.2 and 59.1% in FVB-Trp53 +/- male mice, respectively. TML metastasized to the lungs, spleen, lymph nodes, liver, kidney, and heart in FVB-Trp53 +/- male mice. Furthermore, the incidence of primary lung tumors, such as adenomas and adenocarcinomas, was 65.4, 62.5, and 45.4% in the FVB-Trp53 +/- mice of the 25, 50, and 75 mg/kg groups, respectively. The main tumor types in FVB-Trp53 +/- mice were TML and primary lung tumors, regardless of the dose of MNU administered. These results suggest that systemic tumors may result from malfunctions in the p53 gene and pathway, which is an important factor in the pathogenesis of human cancers. Therefore, FVB-Trp53 heterozygous mice are suitable for short-term carcinogenicity tests using positive carcinogens, and that the best result using MNU, a positive carcinogen, might have a single dose of 50 mg/kg.

Coronavirus disease (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is currently spreading globally. To overcome the COVID-19 pandemic, preclinical evaluations of vaccines and therapeutics using K18-hACE2 and CAG-hACE2 transgenic mice are ongoing. However, a comparative study on SARS-CoV-2 infection between K18-hACE2 and CAG-hACE2 mice has not been published. In this study, we compared the susceptibility and resistance to SARS-CoV-2 infection between two strains of transgenic mice, which were generated in FVB background mice. K18-hACE2 mice exhibited severe weight loss with definitive lethality, but CAG-hACE2 mice survived; and differences were observed in the lung, spleen, cerebrum, cerebellum, and small intestine. A higher viral titer was detected in the lungs, cerebrums, and cerebellums of K18-hACE2 mice than in the lungs of CAG-hACE2 mice. Severe pneumonia was observed in histopathological findings in K18-hACE2, and mild pneumonia was observed in CAG-hACE2. Atrophy of the splenic white pulp and reduction of spleen weight was observed, and hyperplasia of goblet cells with villi atrophy of the small intestine was observed in K18-hACE2 mice compared to CAG-hACE2 mice. These results indicate that K18-hACE2 mice are relatively susceptible to SARS-CoV-2 and that CAG-hACE2 mice are resistant to SARS-CoV-2. Based on these lineage-specific sensitivities, we suggest that K18-hACE2 mouse is suitable for highly susceptible model of SARS-CoV-2, and CAG-hACE2 mouse is suitable for mild susceptible model of SARS-CoV-2 infection.

Rapid advances in modern computing and information technology have enabled millions of people to interact online via various social network and gaming services. The widespread adoption of such online services have made possible analysis of large-scale archival data containing detailed human interactions, presenting a very promising opportunity to understand the rich and complex human behavior. In collaboration with a leading global provider of Massively Multiplayer Online Role-Playing Games (MMORPGs), here we present a network science-based analysis of the interplay between distinct types of user interaction networks in the virtual world. We find that their properties depend critically on the nature of the context-interdependence of the interactions, highlighting the complex and multilayered nature of human interactions, a robust understanding of which we believe may prove instrumental in the designing of more realistic future virtual arenas as well as provide novel insights to the science of collective human behavior.

Arterial hypotension during the early phase of anesthesia can lead to adverse outcomes such as a prolonged postoperative stay or even death. Predicting hypotension during anesthesia induction is complicated by its diverse causes. We investigated the feasibility of developing a machine-learning model to predict postinduction hypotension. Naïve Bayes, logistic regression, random forest, and artificial neural network models were trained to predict postinduction hypotension, occurring between tracheal intubation and incision, using data for the period from between the start of anesthesia induction and immediately before tracheal intubation obtained from an anesthesia monitor, a drug administration infusion pump, an anesthesia machine, and from patients' demographics, together with preexisting disease information from electronic health records. Among 222 patients, 126 developed postinduction hypotension. The random-forest model showed the best performance, with an area under the receiver operating characteristic curve of 0.842 (95% confidence interval [CI]: 0.736-0.948). This was higher than that for the Naïve Bayes (0.778; 95% CI: 0.65-0.898), logistic regression (0.756; 95% CI: 0.630-0.881), and artificial-neural-network (0.760; 95% CI: 0.640-0.880) models. The most important features affecting the accuracy of machine-learning prediction were a patient's lowest systolic blood pressure, lowest mean blood pressure, and mean systolic blood pressure before tracheal intubation. We found that machine-learning models using data obtained from various anesthesia machines between the start of anesthesia induction and immediately before tracheal intubation can predict hypotension occurring during the period between tracheal intubation and incision.

Hypotensive events in the initial stage of anesthesia can cause serious complications in the patients after surgery, which could be fatal. In this study, we intended to predict hypotension after tracheal intubation using machine learning and deep learning techniques after intubation one minute in advance. Meta learning models, such as random forest, extreme gradient boosting (Xgboost), and deep learning models, especially the convolutional neural network (CNN) model and the deep neural network (DNN), were trained to predict hypotension occurring between tracheal intubation and incision, using data from four minutes to one minute before tracheal intubation. Vital records and electronic health records (EHR) for 282 of 319 patients who underwent laparoscopic cholecystectomy from October 2018 to July 2019 were collected. Among the 282 patients, 151 developed post-induction hypotension. Our experiments had two scenarios: using raw vital records and feature engineering on vital records. The experiments on raw data showed that CNN had the best accuracy of 72.63%, followed by random forest (70.32%) and Xgboost (64.6%). The experiments on feature engineering showed that random forest combined with feature selection had the best accuracy of 74.89%, while CNN had a lower accuracy of 68.95% than that of the experiment on raw data. Our study is an extension of previous studies to detect hypotension before intubation with a one-minute advance. To improve accuracy, we built a model using state-of-art algorithms. We found that CNN had a good performance, but that random forest had a better performance when combined with feature selection. In addition, we found that the examination period (data period) is also important.

As more documents appear on the Internet, it becomes important to detect malware within the documents. Malware of non-executables might be more dangerous because people usually open them without worrying about inherent danger. Recently, deep learning models are used to analyze byte streams of the non-executables for malware detection. Although they have shown successful results, they are commonly designed for stream-level detection, but not for file-level detection. In this paper, we propose a new method that aggregates the stream-level results to get file-level results for malware detection. We demonstrate its effectiveness by experimental results with our annotated dataset, and show that it gives performance gain of 3.37–5.89% of F1 scores.

Ordinary users encounter various documents on the network every day, such as news articles, emails, and messages, and most are vulnerable to malicious attacks. Malicious attack methods continue to evolve, making neural network-based malware detection increasingly appealing to both academia and industry. Recent studies have leveraged byte sequences within files to detect malicious activities, primarily using convolutional neural networks to capture local patterns in the byte sequences. Meanwhile, in natural language processing, Transformer-based language models have demonstrated superior performance across various tasks and have been applied to other domains, such as image analysis and speech recognition. In this paper, we introduce a novel Transformer-based language model for malware detection that processes byte sequences as input. We propose two new pre-training strategies: real-or-fake prediction and same-sequence prediction. Including conventional pre-training strategies such as masked language modeling and next-sentence prediction, we explore all possible combinations of these approaches. By compiling existing byte sequences for malware detection, we construct a benchmark consisting of three file types (PDF, HWP, and MS Office) for pre-training and fine-tuning. Our empirical results demonstrate that our language model outperforms convolutional neural networks in the malware detection task, achieving a macro F1 score improvement of approximately 2.7%p∼11.1%p. We believe our language model will serve as a foundation model for malware detection services, and will extend our research to develop a more powerful encoder-based model that can process longer byte sequences.

In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learning algorithm, especially a recurrent neural network (RNN), that consists of a sequence processing layer and a classification layer for the glucose prediction. We tested a simple RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) and varied the architectures to determine the one with the best performance. For that, we collected data for a week using a continuous glucose monitoring device. Type-2 inpatients are usually experiencing bad health conditions and have a high variability of glucose level. However, there are few studies on the Type-2 glucose prediction model while many studies performed on Type-1 glucose prediction. This work has a contribution in that the proposed model exhibits a comparative performance to previous works on Type-1 patients. For 20 in-hospital patients, we achieved an average root mean squared error (RMSE) of 21.5 and an Mean absolute percentage error (MAPE) of 11.1%. The GRU with a single RNN layer and two dense layers was found to be sufficient to predict the glucose level. Moreover, to build a personalized model, at most, 50% of data are required for training.

Malware detection of non-executables has recently been drawing much attention because ordinary users are vulnerable to such malware. Hangul Word Processor (HWP) is software for editing non-executable text files and is widely used in South Korea. New malware for HWP files continues to appear because of the circumstances between South Korea and North Korea. There have been various studies to solve this problem, but most of them are limited because they require a large amount of effort to define features based on expert knowledge. In this study, we designed a convolutional neural network to detect malware within HWP files. Our proposed model takes a raw byte stream as input and predicts whether it contains malicious actions or not. To incorporate highly variable lengths of HWP byte streams, we propose a new padding method and a spatial pyramid average pooling layer. We experimentally demonstrate that our model is not only effective, but also efficient.