Patient-derived organoid biobank identifies epigenetic dysregulation of intestinal epithelial MHC-I as a novel mechanism in severe Crohn's Disease.

OBJECTIVE: Epigenetic mechanisms, including DNA methylation (DNAm), have been proposed to play a key role in Crohn's disease (CD) pathogenesis. However, the specific cell types and pathways affected as well as their potential impact on disease phenotype and outcome remain unknown. We set out to investigate the role of intestinal epithelial DNAm in CD pathogenesis. DESIGN: We generated 312 intestinal epithelial organoids (IEOs) from mucosal biopsies of 168 patients with CD (n=72), UC (n=23) and healthy controls (n=73). We performed genome-wide molecular profiling including DNAm, bulk as well as single-cell RNA sequencing. Organoids were subjected to gene editing and the functional consequences of DNAm changes evaluated using an organoid-lymphocyte coculture and a nucleotide-binding oligomerisation domain, leucine-rich repeat and CARD domain containing 5 (NLRC5) dextran sulphate sodium (DSS) colitis knock-out mouse model. RESULTS: We identified highly stable, CD-associated loss of DNAm at major histocompatibility complex (MHC) class 1 loci including NLRC5 and cognate gene upregulation. Single-cell RNA sequencing of primary mucosal tissue and IEOs confirmed the role of NLRC5 as transcriptional transactivator in the intestinal epithelium. Increased mucosal MHC-I and NLRC5 expression in adult and paediatric patients with CD was validated in additional cohorts and the functional role of MHC-I highlighted by demonstrating a relative protection from DSS-mediated mucosal inflammation in NLRC5-deficient mice. MHC-I DNAm in IEOs showed a significant correlation with CD disease phenotype and outcomes. Application of machine learning approaches enabled the development of a disease prognostic epigenetic molecular signature. CONCLUSIONS: Our study has identified epigenetically regulated intestinal epithelial MHC-I as a novel mechanism in CD pathogenesis.

RenseNet: A Deep Learning Network Incorporating Residual and Dense Blocks with Edge Conservative Module to Improve Small-Lesion Classification and Model Interpretation.

Deep learning has become an essential tool in medical image analysis owing to its remarkable performance. Target classification and model interpretability are key applications of deep learning in medical image analysis, and hence many deep learning-based algorithms have emerged. Many existing deep learning-based algorithms include pooling operations, which are a type of subsampling used to enlarge the receptive field. However, pooling operations degrade the image details in terms of signal processing theory, which is significantly sensitive to small objects in an image. Therefore, in this study, we designed a Rense block and edge conservative module to effectively manipulate previous feature information in the feed-forward learning process. Specifically, a Rense block, an optimal design that incorporates skip connections of residual and dense blocks, was demonstrated through mathematical analysis. Furthermore, we avoid blurring of the features in the pooling operation through a compensation path in the edge conservative module. Two independent CT datasets of kidney stones and lung tumors, in which small lesions are often included in the images, were used to verify the proposed RenseNet. The results of the classification and explanation heatmaps show that the proposed RenseNet provides the best inference and interpretation compared to current state-of-the-art methods. The proposed RenseNet can significantly contribute to efficient diagnosis and treatment because it is effective for small lesions that might be misclassified or misinterpreted.

All-in-one microfluidic design to integrate vascularized tumor spheroid into high-throughput platform.

The development of a scalable and highly reproducible in vitro tumor microenvironment (TME) platform still sheds light on new insights into cancer metastasis mechanisms and anticancer therapeutic strategies. Here, we present an all-in-one injection molded plastic array three-dimensional culture platform (All-in-One-IMPACT) that integrates vascularized tumor spheroids for highly reproducible, high-throughput experimentation. This device allows the formation of self-assembled cell spheroids on a chip by applying the hanging drop method to the cell culture channel. Then, when the hydrogel containing endothelial cells and fibroblasts is injected, the spheroid inside the droplet can be patterned together in three dimensions along the culture channel. In just two steps above, we can build a vascularized TME within a defined area. This process does not require specialized user skill and minimizes error-inducing steps, enabling both reproducibility and high throughput of the experiment. We have successfully demonstrated the process, from spheroid formation to tumor vascularization, using patient-derived cancer cells (PDCs) as well as various cancer cell lines. Furthermore, we performed combination therapies with Taxol (paclitaxel) and Avastin (bevacizumab), which are used in standard care for metastatic cancer. The All-in-One IMPACT is a powerful tool for establishing various anticancer treatment strategies through the development of a complex TME for use in high-throughput experiments.

Enhancement of Osteoblast Differentiation Using No-Ozone Cold Plasma on Human Periodontal Ligament Cells.

Periodontitis is an inflammatory disease that leads to periodontal tissue destruction and bone resorption. Proliferation and differentiation of cells capable of differentiating into osteoblasts is important for reconstructing periodontal tissues destroyed by periodontitis. In this study, the effects of the nozone (no-ozone) cold plasma (NCP) treatment on osteoblastic differentiation in periodontal ligament (PDL) cells were investigated. To test the toxicity of NCP on PDL cells, various NCP treatment methods and durations were tested, and time-dependent cell proliferation was analyzed using a water-soluble tetrazolium salts-1 assay. To determine the effect of NCP on PDL cell differentiation, the cells were provided with osteogenic media immediately after an NCP treatment to induce differentiation; the cells were then analyzed using alkaline phosphatase (ALP) staining, an ALP activity assay, real time PCR, and Alizarin Red S staining. The NCP treatment without toxicity on PDL cells was the condition of 1-min NCP treatment immediately followed by the replacement with fresh media. NCP increased ALP, osteocalcin, osteonectin, and osteopontin expression, as well as mineralization nodule formation. NCP treatment promotes osteoblastic differentiation of PDL cells; therefore, it may be beneficial for treating periodontitis.

Development and Utilization of a Rapid and Accurate Epidemic Investigation Support System for COVID-19.

OBJECTIVES: In this pandemic situation caused by a novel coronavirus disease in 2019 (COVID-19), an electronic support system that can rapidly and accurately perform epidemic investigations, is needed. It would systematically secure and analyze patients' data (who have been confirmed to have the infection), location information, and credit card usage. METHODS: The "Infectious Disease Prevention and Control Act" in South Korea, established a legal basis for the securement, handling procedure, and disclosure of information required for epidemic investigations. The Epidemic Investigation Support System (EISS) was developed as an application platform on the Smart City data platform. RESULTS: The EISS performed the function of inter-institutional communication which reduced the processing period of patients' data in comparison to other methods. This system automatically marked confirmed cases' tracking data on a map and hot-spot analysis which lead to the prediction of areas where people may be vulnerable to infection. CONCLUSION: The EISS was designed and implemented for use during an epidemic investigation to prevent the spread of an infectious disease, by specifically tracking confirmed cases of infection.

A Robotic Context Query-Processing Framework Based on Spatio-Temporal Context Ontology.

Service robots operating in indoor environments should recognize dynamic changes from sensors, such as RGB-depth (RGB-D) cameras, and recall the past context. Therefore, we propose a context query-processing framework, comprising spatio-temporal robotic context query language (ST-RCQL) and a spatio-temporal robotic context query-processing system (ST-RCQP), for service robots. We designed them based on spatio-temporal context ontology. ST-RCQL can query not only the current context knowledge, but also the past. In addition, ST-RCQL includes a variety of time operators and time constants; thus, queries can be written very efficiently. The ST-RCQP is a query-processing system equipped with a perception handler, working memory, and backward reasoner for real-time query-processing. Moreover, ST-RCQP accelerates query-processing speed by building a spatio-temporal index in the working memory, where percepts are stored. Through various qualitative and quantitative experiments, we demonstrate the high efficiency and performance of the proposed context query-processing framework.

Short-term effects of the suboccipital muscle inhibition technique and cranio-cervical flexion exercise on hamstring flexibility, cranio-vertebral angle, and range of motion of the cervical spine in subjects with neck pain: A randomized controlled trial.

BACKGROUND: Cervical spinal instability often leads to neck pain and forward head posture (FHP). To improve neck pain and FHP, both the suboccipital muscle inhibition (SMI) technique and cranio-cervical flexion exercise (CCFE) have been used. However, little is known regarding the validity of hamstring flexibility in neck pain subjects after CCFE as a treatment index, and comparison between the SMI technique and CCFE for neck pain treatment. OBJECTIVE: This study aimed to determine the short-term effects of SMI technique and CCFE, which represent passive and active exercise programs, respectively, and whether these improve the straight leg raise (SLR) test results, popliteal angle (PA), cranio-vertebral angle (CVA), and cervical range of motion (CROM) and are equally effective in immediate enhancement of such measures in neck pain subjects. METHODS: Twenty subjects with neck pain were randomly allocated to the SMI and CCFE groups (n= 10 each). The outcomes were evaluated via clinical evaluation and SLR, PA, CVA, and CROM measurement before and after intervention. RESULTS: SLR test results, PA, CVA (sitting and standing), and CROM (flexion, extension, lateral flexion, and right and left rotations) after both interventions were significantly higher than those before the interventions (P< 0.05). The SLR test results (left), PA, and CVA (standing) after the SMI technique improved to levels similar to those observed after CCFE. CONCLUSIONS: Both the SMI technique and CCFE improve SLR test results, PA, CVA, and CROM and are equally effective in immediate enhancement of the hamstring flexibility, CVA, and CROM in subjects with neck pain.

AVQS: attack route-based vulnerability quantification scheme for smart grid.

A smart grid is a large, consolidated electrical grid system that includes heterogeneous networks and systems. Based on the data, a smart grid system has a potential security threat in its network connectivity. To solve this problem, we develop and apply a novel scheme to measure the vulnerability in a smart grid domain. Vulnerability quantification can be the first step in security analysis because it can help prioritize the security problems. However, existing vulnerability quantification schemes are not suitable for smart grid because they do not consider network vulnerabilities. We propose a novel attack route-based vulnerability quantification scheme using a network vulnerability score and an end-to-end security score, depending on the specific smart grid network environment to calculate the vulnerability score for a particular attack route. To evaluate the proposed approach, we derive several attack scenarios from the advanced metering infrastructure domain. The experimental results of the proposed approach and the existing common vulnerability scoring system clearly show that we need to consider network connectivity for more optimized vulnerability quantification.