Introduction Angiogenesis plays a significant role in the development of tumor progression and inflammatory diseases. The role of IL-28A in angiogenesis and its precise regulatory mechanisms remain rarely elucidated. Objectives We report the novel regulatory role of IL-28A in physiological angiogenesis. The study aimed to elucidate the regulatory mechanisms involved in IL-28A-mediated angiogenesis and identify key genes associated with IL-28A-induced angiogenic responses. Methods To know the effect of IL-28A on angiogenesis, HUVECs were applied to perform proliferation, migration, invasion, tube formation, immunoblot, and EMSA. Gene expression changes in HUVECs following IL-28A treatment were analyzed by NGS. The functional role of HSP70-1 and IL-10Rβ in IL-28A-induced angiogenic responses was evaluated using PCR and siRNA knockdown. Animal studies were conducted by aortic ring ex vivo assays, Matrigel plug in vivo assays, and immunochemistry using HSP70-1 knockout and transgenic mice models. The efficacy of IL-28A in angiogenesis was confirmed in a hind-limb ischemia model. Results Autocrine/paracrine actions in HUVECs regulated IL-28A protein expression. Exogenous IL-28A increased the proliferation of HUVECs via eNOS/AKT and ERK1/2 signaling. IL-28A treatment promoted migration, invasion, and capillary tube formation of HUVECs through induction of the AP-1/NF-κB/MMP-2 network, which was associated with eNOS/AKT and ERK1/2 signaling. The efficacy of IL-28A-induced angiogenic potential was confirmed by aortic ring and Matrigel plug assay. HSP70-1 was identified as an IL-28A-mediated angiogenic effector gene using bioinformatics. Knockdown of HSP70-1 abolished angiogenic responses and eNOS/AKT signaling in IL-28A-treated HUVECs. IL-28A-induced microvessel sprouting formation was testified in HSP70-1-deficient and HSP70-1 transgenic mice. Flow recovery in hind-limb ischemia mice was accelerated by IL-28A injection. Finally, ablation of the IL-10Rβ gene impeded the angiogenic responses and eNOS/AKT signaling stimulated by IL-28A in HUVECs. Conclusion HSP70-1 drives the progression of angiogenesis by the IL-28A/IL-10Rβ axis via eNOS/AKT signaling and the AP-1/NF-κB/MMP-2 network.

In this study, an integrated deep learning framework was developed for classifying the periodontitis stages of each individual tooth using dental panoramic radiographs. Based on actual patient panoramic radiographs data, the bone loss by periodontitis and cementoenamel junction boundaries were detected, while the tooth number and tooth length were identified using data from AIHub, an open database platform. The two factors were integrated to classify and to evaluate the periodontitis staging on dental panoramic radiography. Periodontitis is classified into four stages based on the criteria of the radiographic bone level, as suggested at the relevant international conference in 2017. For the integrated deep learning framework developed in this study, the classification performance was evaluated by comparing the results of dental specialists, which indicated that the integrated framework had an accuracy of 0.929, with a recall and precision of 0.807 and 0.724, respectively, in average across all four stages. The novel framework was thus shown to exhibit a relatively high level of performance, and the findings in this study are expected to assist dental specialists with detecting the periodontitis stage and subsequent effective treatment. A systematic application will be developed in the future, to provide ancillary data for diagnosis and basic data for the treatment and prevention of periodontal disease.

Periodontal disease is a chronic but treatable condition which often does not cause pain during the initial stages of the illness.Lack of awareness of symptoms can delay initiation of treatment and worsen health.The aim of this study was to develop and compare different risk prediction models for periodontal disease using machine learning algorithms.We obtained information on risk factors for periodontal disease from the Korea National Health and Nutrition Examination Survey (KNHANES) dataset.Principal component analysis and an auto-encoder were used to extract data on risk factors for periodontal disease.A synthetic minority oversampling technique algorithm was used to solve the problem of data imbalance.We used a combination of logistic regression analysis, support vector machine (SVM) learning, random forest, and AdaBoost to classify and compare risk prediction models for periodontal disease.In cases where we used principal component analysis (PCA) to extract risk factors, the recall was higher than the feature selection method in the logistic regression and support-vector machine learning models.AdaBoost's recall was 0.98, showing the highest performance of both feature selection and PCA.The F1 score showed relatively high performance in Ada-Boost, logistic regression, and SVM learning models.By using the risk factors extracted from the research results and the predictive model based on machine learning, it will be able to help in the prevention and diagnosis of periodontal disease, and it will be used to study the relationship with various diseases related to periodontal disease.

Predicting clinical information using gene expression is challenging given the complexity and high dimensionality of gene data. This study propose a deep learning framework for cancer diagnosis through feature extraction and classifier based on various pre-trained autoencoder technologies for kidney cancer. It can be fine-tuned for any tasks and predict clinical information by neural network classifiers. Our model achieved micro and macro F1-scores of 96.2% and 95.8% for gender, 95.8% and 76.3% for race, and 99.8% and 99.6% for sample type predictions, respectively, which is much higher than the values of traditional dimensionality reduction and machine learning techniques. In the results, the conditional variational mutation autoencoder (CVAE) improved the macro F1 score, a difficult race prediction task, by 7.6%. Our results are useful for the prognosis as well as prevention and early diagnosis of kidney cancer.

The bladder cancer (BCa) microenvironment comprises heterogeneous tumor cell populations, the surrounding stroma and the extracellular matrix (ECM). Collagen, the scaffold of the tumor microenvironment, regulates ECM remodeling to promote tumor infiltration, angiogenesis, invasion and migration. The present study examined how collagen type VI‑α (COL6A) 1 and 2 function during BCa pathogenesis and progression, with the aim of facilitating the development of precision therapeutics, risk stratification and molecular diagnosis. COL6A1 and COL6A2 mRNA expression in non‑muscle invasive BCa (NMIBC) and MIBC tissue samples was measured using reverse transcription‑quantitative PCR. In addition, the tumor‑suppressive effects of COL6A1 and COL6A2 in human BCa EJ cells (MGH‑U1) were assessed. Compared with normal controls, COL6A1 and COL6A2 mRNA expression was downregulated in both NMIBC and MIBC tissue samples (P<0.05, respectively). COL6A1 and COL6A2 effectively inhibited the proliferation of human BCa EJ cells (MGH‑U1) and induced cell cycle arrest at the G1 phase. Additionally, COL6A1 and COL6A2 served roles in MAPK and AKT signaling by increasing p38 MAPK phosphorylation and decreasing AKT phosphorylation. Finally, COL6A1 and COL6A2 inhibited wound healing and invasion by suppressing the activity of matrix metalloproteinase (MMP)‑2 and MMP‑9. In conclusion, COL6A1 and COL6A2 may act as classical collagens by forming a physical barrier to inhibit BCa tumor growth and invasion.

Purpose: MicroRNAs (miRNAs) are small non-coding RNAs and are involved in the development, proliferation, and pathogenesis of prostate cancer (PCa). Urinary miRNAs are promising non-invasive biomarkers for PCa diagnosis because of their stability in urine. Here, we evaluated the diagnostic value of urinary miR-1913 to miR-3659 ratio in PCa patients and benign prostate hyperplasia (BPH) controls. Materials and Methods: Candidate miRNAs were identified from urinary microarray data and tested by real-time PCR. The urinary miR-1913 to miR-3659 expression ratio was selected and tested in 83 urine samples (44 PCa and 39 BPH) to confirm its validity as a non-invasive diagnostic biomarker for PCa. Results: The expression ratio of urinary miR-1913 to miR-3659 was significantly higher in PCa than in BPH (p=0.002) and showed a higher area under the receiver operating characteristic curve than prostate-specific antigen (PSA; 0.821 vs. 0.518) in patients within the PSA gray zone (tPSA: 3-10 ng/mL), with sensitivity of 75.0% and specificity of 78.6% (p=0.003). Conclusions: The urinary miR-1913 to miR-3659 expression ratio was increased in PCa and may serve as a useful supplemental biomarker to PSA for the diagnosis of PCa, particularly in patients within the PSA gray zone.

Recently, large-scale biodata has been generated using advanced biotechnology methods; thus, the importance of analyzing this technology has increased. Numerous data mining methods in the bioinformatics field have been developed for processing biodata. We analyzed gene expression data and clinical data of kidney cancer patients as the TCGA database. To predict the prognosis of kidney cancer patients, we extracted significant genes and then we applied a data mining-based classification method to the data. With principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO), we extracted significant genes and compared classification accuracy and performance with a classification algorithm. We combined clinical data from patients with kidney cancer and gene data to determine the optimal classification model. Also, with sample type and primary diagnosis, we estimated classification accuracy as risk factors. As experimental results, neural network algorithms and logistic regression had the best performance in classification accuracy. The LASSO method showed better classification performance than PCA method for significant gene extraction. We can apply the results to extract biomarkers to predict prognosis of kidney cancer which has a lot of causes and to prevent and diagnose kidney cancer.

Bladder cancer (BCa) is the most prevalent neoplasia of the urinary tract. Unfortunately, limited improvements in effective BCa management have meant that it remains a challenging disease. Cystoscopy has been the gold standard for BCa diagnosis and surveillance for over two centuries but is an invasive and expensive approach. Recently, liquid biopsy has been identified as a promising field of cancer research, due to its noninvasiveness and ease of sampling. Liquid biopsy samples could provide comprehensive information regarding the genetic landscape of cancer and could track genomic evolution of the disease over time. Exosomes, which contain RNAs, DNAs, and proteins, are a potential source of tumor biomarkers in liquid biopsy samples. In particular, exosomal miRNAs (exomiRs) hold great promise as biomarkers for tumor development and progression. In this review, we provide an overview of liquid biopsy biomarkers, with a particular focus on the use of exomiRs as biomarkers of cancer, and summarize their clinical implications for BCa. Finally, we discuss the future perspectives of these biomarkers in cancer research.

Recently, Numerous data mining methods in the bioinformatics field have been developed for processing biodata. We extracted significant genes (60,483 of gene expression data from TCGA) for the prognosis prediction of 1,157 patients using gene expression data from patients with kidney cancer and applied classification methods based on data mining. Significant genes were extracted using least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA), and classification accuracy and performance were compared using a classification algorithm. Combined clinical data from patients with kidney cancer and gene data were used to determine the optimal classification model and estimate classification accuracy as risk factors by sample type, primary diagnosis, tumor stage, and vital status representing the state of patients. Classification accuracy based on sample type showed the best performance, particularly for the logistic regression and support vector machine algorithms. These results can be applied to extract biomarkers for prognosis prediction of kidney cancer from various causes and for preventing kidney cancer and early diagnosis.

Recently, large-scale bioinformatics and genomic data have been generated using advanced biotechnology methods, thus increasing the importance of analyzing such data. Numerous data mining methods have been developed to process genomic data in the field of bioinformatics. We extracted significant genes for the prognosis prediction of 1157 patients using gene expression data from patients with kidney cancer. We then proposed an end-to-end, cost-sensitive hybrid deep learning (COST-HDL) approach with a cost-sensitive loss function for classification tasks on imbalanced kidney cancer data. Here, we combined the deep symmetric auto encoder; the decoder is symmetric to the encoder in terms of layer structure, with reconstruction loss for non-linear feature extraction and neural network with balanced classification loss for prognosis prediction to address data imbalance problems. Combined clinical data from patients with kidney cancer and gene data were used to determine the optimal classification model and estimate classification accuracy by sample type, primary diagnosis, tumor stage, and vital status as risk factors representing the state of patients. Experimental results showed that the COST-HDL approach was more efficient with gene expression data for kidney cancer prognosis than other conventional machine learning and data mining techniques. These results could be applied to extract features from gene biomarkers for prognosis prediction of kidney cancer and prevention and early diagnosis.

Screening for genes or markers relevant to bladder cancer (BC) tumorigenesis and progression is of vital clinical significance. The present study used reverse-transcription quantitative PCR reaction assays to examine the expression of mRNA encoding Rho GTPase-activating protein 9 (ARHGAP9) in BC tissue samples and to determine whether ARHGAP9 is an independent prognostic biomarker for non-muscle invasive BC (NMIBC) and muscle invasive BC (MIBC). The results revealed that the downregulation of ARHGAP9 expression in the tissue of patients with NMIBC or MIBC was significantly associated with a poor prognosis. In patients with NMIBC, a high expression of ARHGAP9 was significantly associated with prolonged recurrence-free survival, whereas in MIBC patients, it was significantly associated with an increased progression-free and cancer-specific survival. The risk of cancer-specific death was 2.923 times higher (95% confidence interval, 1.192-7.163) when ARHGAP9 levels were decreased. In conclusion, lower expressions of ARHGAP9 correlated with BC prognosis, indicating that it may be a useful marker for guiding treatment application.

The incidence of stomach cancer has been found to be gradually decreasing; however, it remains one of the most frequently occurring malignant cancers in Korea. According to statistics of 2017, stomach cancer is the top cancer in men and the fourth most important cancer in women, necessitating methods for its early detection and treatment. Considerable research in the field of bioinformatics has been conducted in cancer studies, and bioinformatics approaches might help develop methods and models for its early prediction. We aimed to develop a classification method based on deep learning and demonstrate its application to gene expression data obtained from patients with stomach cancer. Data of 60,483 genes from 334 patients with stomach cancer in The Cancer Genome Atlas were evaluated by principal component analysis, heatmaps, and the convolutional neural network (CNN) algorithm. We combined the RNA-seq gene expression data with clinical data, searched candidate genes, and analyzed them using the CNN deep learning algorithm. We performed learning using the sample type and vital status of patients with stomach cancer and verified the results. We obtained an accuracy of 95.96% for sample type and 50.51% for vital status. Despite overfitting owing to the limited number of patients, relatively accurate results for sample type were obtained. This approach can be used to predict the prognosis of stomach cancer, which has many types and underlying causes.

OBJECTIVES:The aim of this research was to determine intra-oral factors that affect halitosis in young women.METHODS:This study was performed between March 2014 to May 2014, and included 35 women in their 20s with good oral health. Correlation and logistic regression analyses were performed to investigate the change in halitosis immediately, and 1 hour after scaling.RESULTS:In both oral gas (OG) and extraoral gas (EG) groups, halitosis was reduced after scaling compared to before scaling. The logistic regression analysis of oral state factors in OG showed that as oral fluid [odds ratio (OR) = 0.792, p = 0.045] and dental plaque (OR = 0.940, p = 0.016) decreased by 1 unit, the OR in the OG group decreased (> 50). In addition, as glucose levels in the oral cavity (OR = 1.245, p = 0.075) and tongue coating index (OR = 2.912, p = 0.064) increased by 1 unit, the OR in the OG group increased (> 50). Furthermore, in the EG group, as oral fluid (OR = 0.66, p = 0.01) and dental plaque (OR = 0.95, p = 0.04) decreased, the OR in the EG group decreased (> 50) significantly.CONCLUSION:To control halitosis, it is necessary to increase oral fluid and decrease the amount of tongue plaque. Furthermore, maintaining a healthy oral environment, aided by regular scaling and removal of dental plaque, may significantly control halitosis.

The most common symptom of bladder cancer (BC) is hematuria. However, not all patients with hematuria are diagnosed with BC. Here, we explored a novel method to discriminate BC from hematuria under nonmalignant conditions by measuring differences in urinary cell‐free microRNA (miRNA) expression between patients with BC and those with hematuria. A multicenter study was performed using 543 urine samples obtained from the National Biobank of Korea, including 326 BC, 174 hematuria and 43 pyuria without cancer. The urinary miR‐6124 to miR‐4511 ratio was considerably higher in BC than in hematuria or pyuria, and enabled the discrimination of BC from patients with hematuria at a sensitivity of >90% (p < 0.001). Conclusively, the proposed noninvasive diagnostic tool based on the expression ratio of urinary cell‐free miR‐6124 to miR‐4511 can reduce unnecessary cystoscopies in patients with hematuria undergoing evaluation for BC, with a minimal loss in sensitivity for detecting cancer.

N-terminal pro-brain natriuretic peptide (NT-proBNP) is a well-known biomarker for the diagnosis and prognosis of heart failure, and is directly associated with myocardial dysfunction. We evaluated the prognostic value of NT-proBNP for major adverse cardiac events (MACEs) among patients with non-ST-segment elevation myocardial infarction (NSTEMI) from the Korea Acute Myocardial Infarction Registry during their mid-term follow-up period. In this paper, we analyzed NT-proBNP according to various MACE and level of NT-proBNP. We used multivariate logistic regression to determine the risk factors according to MACE type and NT-proBNP levels, and to identify the cutoff value for each MACE by using the receiver operating characteristic (ROC) curve. NT-proBNP was a significant variable among cardiac deaths (p = 0.016), myocardial infarction (p = 0.000), and coronary artery bypass grafting (CABG) (p = 0.000) in patients with MACE compared with those without MACE. Two-vessel coronary artery disease (CAD) (p = 0.037) and the maximum creatinine kinase (max-CK) (p = 0.031) produced significant results in repeat percutaneous coronary intervention. The area under the ROC curve was found to be statistically significant for cardiac death and CABG. NT-proBNP is a useful predictor for 12-month MACEs among patients with NSTEMI and in those with heart failure. We propose that a new index incorporating NT-proBNP, max-CK, and CAD vessel will be useful as a prognostic indicator of MACEs in the future.

We report a pivotal role for IL-5 as an angiogenic activator. IL-5 increased proliferation, migration and colony tube formation in HUVECs associated with the phosphorylation of ERK and AKT/eNOS, and promoted microvessel sprouting from an angiogenesis animal model. The angiogenic effects were confirmed in IL-5-deficient mice and addition of IL-5 antibody. HSP70-1 was identified via expression profiling following IL-5 stimulation. A siRNA knockdown of HSP70-1 suppressed angiogenic responses and eNOS phosphorylation induced by IL-5. HSP70-1 overexpression enhanced IL-5-induced angiogenic responses. In addition, IL-5-induced neo-vascular formation was verified in both HSP70-1 knockout and HSP70-1 transgenic mice. Furthermore, transcription factor AP-1 was a main factor in IL-5-induced HSP70-1 in response to ERK and AKT signaling pathway. Angiogenic responses induced by VEGF had no effect in either HSP70-1 siRNA in vitro or HSP70-1 knockout mice. IL-5-induced angiogenic responses depended on the binding of IL-5Rα. Our data demonstrate that binding of IL-5 to IL-5Rα receptors enhances angiogenic responses by stimulating the expression of HSP70-1 via the eNOS signaling pathway.

Amid growing concern over the changing climate, environment, and health care, the interconnectivity between cardiovascular diseases, coupled with rapid industrialization, and a variety of environmental factors, has been the focus of recent research. It is necessary to research risk factor extraction techniques that consider individual external factors and predict diseases and conditions. Therefore, we designed a framework to collect and store various domains of data on the causes of cardiovascular disease, and constructed a big data integrated database. A variety of open source databases were integrated and migrated onto distributed storage devices. The integrated database was composed of clinical data on cardiovascular diseases, national health and nutrition examination surveys, statistical geographic information, population and housing censuses, meteorological administration data, and Health Insurance Review and Assessment Service data. The framework was composed of data, speed, analysis, and service layers, all stored on distributed storage devices. Finally, we proposed a framework for a cardiovascular disease prediction system based on lambda architecture to solve the problems associated with the real-time analyses of big data. This system can be used to help predict and diagnose illnesses, such as cardiovascular diseases.

National research management organizations need to ensure that research proposals are reviewed fairly and efficiently, which requires the selection of suitable reviewers. In particular, reviewing research proposals in a particular area necessitates the selection of a group with the most reasonable standard for recommending an expert in that area. In this study, we develop an automatic matching system that matches a research proposal with a reviewer who can evaluate it most effectively, using keywords with fuzzy weights based on databases in the corresponding field of research. All functions that we developed were based on the MapReduce framework created by Hadoop, which was verified to enhance matching performance and ensure expandability. This enabled us to select suitable researchers from existing research projects, papers and research reviewer databases. Our system can influence the operation of the national research management system and contribute to academic development.

The present study examined the utility of fibroblast growth factor receptor 3 (FGFR3) mutation status and gene expression as a prognostic marker in primary pT1 bladder cancer (BC). A total of 120 patients with primary pT1 BC were enrolled. FGFR3 mutation status was determined by direct sequencing and FGFR3 mRNA expression level was determined by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis. The results were compared with the clinicopathological parameters, and the prognostic value of FGFR3 was evaluated by Kaplan-Meier analysis and a multivariate Cox regression test. FGFR3 mutations were identified in 48/120 (40.0%) patients with pT1 BC. FGFR3 mRNA expression level was significantly higher in those with BC harboring FGFR3 mutations (P<0.001). Low FGFR3 expression level was associated with high-grade tumors and cancer progression (P=0.006 and P=0.001), whereas FGFR3 mutation status was not associated with cancer progression. Kaplan-Meier analysis revealed a similar result (log-rank, P<0.001). Multivariate analysis identified low FGFR3 expression level (odds ratio, 3.300; 95% confidence interval, 1.310-8.313; P=0.011) as an independent predictor of cancer progression. Stratification by exon site of FGFR3 mutations yielded significant differences in mRNA expression level. None of the patients with BC harboring FGFR3 mutations in exon 9 demonstrated disease progression. The mRNA expression level of the FGFR3 gene may be used to precisely identify subsets of patients with pT1 BC that have a relatively better prognosis. The prognostic influences of FGFR3 mutations may be modulated by the exon site of FGFR3 mutations.