Analysis of risk factors for delayed bleeding after semi-closed hemorrhoidectomy.

PURPOSE: The aim of this study was to determine the incidence of delayed post-hemorrhoidectomy bleeding (DPHB) after hemorrhoidectomy using a semi-closed procedure. We also investigated risk factors associated with DPHB. METHODS: This retrospective study enrolled a total of 1645 consecutive patients with symptomatic grade II to IV hemorrhoids who underwent a semi-closed procedure at the Seoul Songdo Hospital between September 2018 and May 2019. All patients underwent a semi-closed procedure with submucosal feeding vessel ligation, a method commonly performed at our institution. RESULTS: A total of 1645 patients (mean age: 48.67 (±14.38) years, 823 (50.0%) male/822 (50.0%) female) underwent semi-closed hemorrhoidectomy. Critically, 24 (1.5%) patients experienced DPHB. Of these patients, 13 (0.8%) experienced stump bleeding, whereas 11 (0.7%) experienced marginal bleeding. The mean bleeding period was 8.21±4.45 days. Multivariate analysis showed that male sex, drinking history, more than four hemorrhoid piles, and laxative agents were independent risk factors for DPHB. The risk of stump bleeding was significantly associated with male sex (OR=5.55, 95% CI 1.23-25.14, p=0.026), more than four hemorrhoid piles (OR=5.90, 95% CI 1.62-21.53, p=0.007), and laxative usage (OR=3.92, 95% CI 1.31-11.74, p=0.015). Conversely, the risk of marginal bleeding were significantly associated with drinking history (OR=10.48, 95% CI 1.34-82.03, p=0.025) and more than four hemorrhoid piles (OR=4.71, 95% CI 1.24-17.81, p=0.023). CONCLUSION: Male sex, drinking history, more than four hemorrhoid piles, and laxative usage were independent risk factors for DPHB in patients undergoing semi-closed hemorrhoidectomy. The risk factors for stump bleeding included male sex, more than four hemorrhoid piles, and laxative usage. In contrast, the risk factors for marginal bleeding were drinking history and more than four hemorrhoid piles.

Local Alignment of DNA Sequence Based on Deep Reinforcement Learning.

Goal: Over the decades, there have been improvements in the sequence alignment algorithm, with significant advances in various aspects such as complexity and accuracy. However, human-defined algorithms have an explicit limitation in view of developmental completeness. This paper introduces a novel local alignment method to obtain optimal sequence alignment based on reinforcement learning. Methods: There is a DQNalign algorithm that learns and performs sequence alignment through deep reinforcement learning. This paper proposes a DQN x-drop algorithm that performs local alignment without human intervention by combining the x-drop algorithm with this DQNalign algorithm. The proposed algorithm performs local alignment by repeatedly observing the subsequences and selecting the next alignment direction until the x-drop algorithm terminates the DQNalign algorithm. This proposed algorithm has an advantage in view of linear computational complexity compared to conventional local alignment algorithms. Results: This paper compares alignment performance (coverage and identity) and complexity for a fair comparison between the proposed DQN x-drop algorithm and the conventional greedy x-drop algorithm. Firstly, we prove the proposed algorithm's superiority by comparing the two algorithms' computational complexity through numerical analysis. After that, we tested the alignment performance actual HEV and E.coli sequence datasets. The proposed method shows the comparable identity and coverage performance to the conventional alignment method while having linear complexity for the [Formula: see text] parameter. Conclusions: Through this study, it was possible to confirm the possibility of a new local alignment algorithm that minimizes computational complexity without human intervention.

Pairwise Heuristic Sequence Alignment Algorithm Based on Deep Reinforcement Learning.

Goal: Various methods have been developed to analyze the association between organisms and their genomic sequences. Among them, sequence alignment is the most frequently used method for comparative analysis of biological genomes. We intend to propose a novel pairwise sequence alignment method using deep reinforcement learning to break out the old pairwise alignment algorithms. Methods: We defined the environment and agent to enable reinforcement learning in the sequence alignment system. This novel method, named DQNalign, can immediately determine the next direction by observing the subsequences within the moving window. Results: DQNalign shows superiority in the dissimilar sequence pairs that have low identity values. And theoretically, we confirm that DQNalign has a low dimension for the sequence length in view of the complexity. Conclusions: This research shows the application method of deep reinforcement learning to the sequence alignment system and how deep reinforcement learning can improve the conventional sequence alignment method.

Feature selection algorithm based on dual correlation filters for cancer-associated somatic variants.

BACKGROUND: Since the development of sequencing technology, an enormous amount of genetic information has been generated, and human cancer analysis using this information is drawing attention. As the effects of variants on human cancer become known, it is important to find cancer-associated variants among countless variants. RESULTS: We propose a new filter-based feature selection method applicable for extracting cancer-associated somatic variants considering correlations of data. Both variants associated with the activation and deactivation of cancer's characteristics are analyzed using dual correlation filters. The multiobjective optimization is utilized to consider two types of variants simultaneously without redundancy. To overcome high computational complexity problem, we calculate the correlation-based weight to select significant variants instead of directly searching for the optimal subset of variants. The proposed algorithm is applied to the identification of melanoma metastasis or breast cancer stage, and the classification results of the proposed method are compared with those of conventional single correlation filter-based method. CONCLUSIONS: We verified that the proposed dual correlation filter-based method can extract cancer-associated variants related to the characteristics of human cancer.

Comparison of 3-Dimensional Pelvic Floor Ultrasonography and Defecography for Assessment of Posterior Pelvic Floor Disorders.

PURPOSE: The aim of this study was to determine the accuracy of 3-dimensional (3D) pelvic floor ultrasonography and compare it with defecography in assessment of posterior pelvic disorders. METHODS: Eligible patients were consecutive women undergoing 3D pelvic floor ultrasonography at one hospital between August 2017 and February 2019. All 3D pelvic floor ultrasonography was performed by one examiner. A total of 167 patients with suspected posterior pelvic disorder was retrospectively enrolled in the study. The patients were divided into 3 groups according to the main symptoms. RESULTS: There were 82 rectoceles on defecography (55 barium trapping) and 84 on 3D pelvic floor ultrasonography. Each modality identified 6 enteroceles. There were 43 patients with pelvic floor dyssynergia on defecography and 41 on ultrasonography. There were 84 patients with intussusception on defecography and 41 on 3D pelvic floor ultrasonography. Agreement of the 2 diagnostic tests was confirmed using Cohen's kappa value. Rectocele (kappa, 0.784) and enterocele (kappa, 0.654) both indicated good agreement between defecography and 3D pelvic floor ultrasonography. In addition, pelvic floor dyssynergia (kappa, 0.406) showed moderate agreement, while internal intussusception (kappa, 0.296) had fair agreement. CONCLUSION: This study showed good agreement for detection of posterior pelvic disorders between defecography and 3D pelvic floor ultrasonography.

Specificity Analysis of Genome Based on Statistically Identical K-Words With Same Base Combination.

Goal: Individual characteristics are determined through a genome consisting of a complex base combination. This base combination is reflected in the k-word profile, which represents the number of consecutive k bases. Therefore, it is important to analyze the genome-specific statistical specificity in the k-word profile to understand the characteristics of the genome. In this paper, we propose a new k-word-based method to analyze genome-specific properties. Methods: We define k-words consisting of the same number of bases as statistically identical k-words. The statistically identical k-words are estimated to appear at a similar frequency by statistical prediction. However, this may not be true in the genome because it is not a random list of bases. The ratio between frequencies of two statistically identical k-words can then be used to investigate the statistical specificity of the genome reflected in the k-word profile. In order to find important ratios representing genomic characteristics, a reference value is calculated that results in a minimum error when classifying data by ratio alone. Finally, we propose a genetic algorithm-based search algorithm to select a minimum set of ratios useful for classification. Results: The proposed method was applied to the full-length sequence of microorganisms for pathogenicity classification. The classification accuracy of the proposed algorithm was similar to that of conventional methods while using only a few features. Conclusions: We proposed a new method to investigate the genome-specific statistical specificity in the k-word profile which can be applied to find important properties of the genome and classify genome sequences.

Genome classification improvements based on k-mer intervals in sequences.

Given the vast amount of genomic data, alignment-free sequence comparison methods are required due to their low computational complexity. k-mer based methods can improve comparison accuracy by extracting an effective feature of the genome sequences. The aim of this paper is to extract k-mer intervals of a sequence as a feature of a genome for high comparison accuracy. In the proposed method, we calculated the distance between genome sequences by comparing the distribution of k-mer intervals. Then, we identified the classification results using phylogenetic trees. We used viral, mitochondrial (MT), microbial and mammalian genome sequences to perform classification for various genome sets. We confirmed that the proposed method provides a better classification result than other k-mer based methods. Furthermore, the proposed method could efficiently be applied to long sequences such as human and mouse genomes.

Identification of Primary and Metastatic Melanoma based on Copy Number Variation.

The development of new sequencing technology has stimulated the cancer-related genome analysis. Copy number variation is one of the most important features that represents the structural variation. In this paper, we suggest the metastasis identification method of melanoma using copy number variations. The identification marker is defined in consideration of the presence and the type of copy number variations in primary and metastatic tumors. The optimization of marker is also provided and classification performances of developed markers are compared using the linear classifier.

[Pancreatic Panniculitis in Patients with Chronic Pancreatitis: Case Report and Review of Literature].

Pancreatic panniculitis is a rare complication characterized by subcutaneous fat necrosis associated with pancreatic disease. It has been postulated that pancreatic panniculitis is caused by the systemic activity of pancreatic enzymes that lead to microcirculatory disturbances. We report a 41-year-old heavy alcoholic woman with pancreatic panniculitis that coexisted with acute and chronic pancreatitis. She was diagnosed with chronic pancreatitis and alcoholic liver cirrhosis 5 years ago. She presented with multiple, tender, erythematous, subcutaneous nodules with heat sensation on both lower legs. Laboratory evaluation revealed an increase in the serum blood amylase and lipase. Histopathologic findings showed fat necrosis with inflammation around the necrotic subcutaneous fat tissue. The lesions subsided gradually with an improvement of acute pancreatitis.

Repetitive element signature-based visualization, distance computation, and classification of 1766 microbial genomes.

The genomes of living organisms are populated with pleomorphic repetitive elements (REs) of varying densities. Our hypothesis that genomic RE landscapes are species/strain/individual-specific was implemented into the Genome Signature Imaging system to visualize and compute the RE-based signatures of any genome. Following the occurrence profiling of 5-nucleotide REs/words, the information from top-50 frequency words was transformed into a genome-specific signature and visualized as Genome Signature Images (GSIs), using a CMYK scheme. An algorithm for computing distances among GSIs was formulated using the GSIs' variables (word identity, frequency, and frequency order). The utility of the GSI-distance computation system was demonstrated with control genomes. GSI-based computation of genome-relatedness among 1766 microbes (117 archaea and 1649 bacteria) identified their clustering patterns; although the majority paralleled the established classification, some did not. The Genome Signature Imaging system, with its visualization and distance computation functions, enables genome-scale evolutionary studies involving numerous genomes with varying sizes.

Genome-based microorganism classification using coalition formulation game.

Genome-based microorganism classification is the one of interesting issues in microorganism taxonomy. However, the advance in sequencing technology requires a low-complex algorithm to process a great amount of bio sequence data. In this paper, we suggest a coalition formation game for microorganism classification, which can be implemented in distributed manner. We extract word frequency feature from microorganism sequences and formulate the coalition game model that considers the distance among word frequency features. Then, we propose a coalition formation algorithm for clustering microorganisms with feature similarity. The performance of proposed algorithm is compared with that of conventional schemes by means of an experiment. According to the result, we showed that the correctness of proposed distributed algorithm is similar to that of conventional centralized schemes.

REViewer: a tool for linear visualization of repetitive elements within a sequence query.

A species-specific population of arrangements of repetitive elements (REs), called RE arrays, exists in the human and mouse genomes. We developed an RE analytical tool, named REViewer, for visualizing RE occurrences within RE arrays and other genomic regions as an interactive line map. REViewer utilizes an RE reference library which is established with two RE types: 1) REMiner-generated undefined REs and 2) RepeatMasker-derived defined REs. RE occurrences within queries are visualized as a line map using these two RE types. The REViewer's controller provides analytical options, such as zoom, customization of axis unit, and RE type selection. The functionality of REViewer was evaluated using the human chromosome Y sequence. The REViewer is determined to be an efficient tool that facilitates visualization of up to 6000 REs in RE arrays and other genomic regions. The maximum query size is linked to the RE mining tools (e.g., REMiner, RepeatMasker), not to REViewer.

Large interrelated clusters of repetitive elements (REs) and RE arrays predominantly represent reference mouse chromosome Y.

The vast majority of the mouse and human genomes consist of repetitive elements (REs), while protein-coding sequences occupy only ~3 %. It has been reported that the Y chromosomes of both species are highly populated with REs although at present, their complete sequences are not available in any public database. The recent update of the mouse genome database (Build 38.1) from the National Center for Biotechnology Information (NCBI) indicates that mouse chromosome Y is ~92 Mb in size, which is substantially larger than the ~16 Mb reported previously (Build 37.2). In this study, we examined how REs are arranged in mouse chromosome Y (Build 38.1) using REMiner-II, a RE mining program. A combination of diverse REs and RE arrays formed large clusters (up to ~28 Mb in size) and most of them were directly or inversely related. Interestingly, the RE population of human chromosome Y (NCBI Build 37.2-current) was less dense, and the RE/RE array clusters were not evident in comparison to mouse chromosome Y. The annotated gene loci were distributed in five different regions and most of them were surrounded by unique RE arrays. In particular, tandem RE arrays were embedded into the introns of two adjacent gene loci. The findings from this study indicate that the large and interrelated clusters of REs and RE arrays predominantly represent the unique organizational pattern of mouse chromosome Y. The potential interactions among the clusters, which are populated with various interrelated REs and RE arrays, may play a role in the structural configuration and function of mouse chromosome Y.

REMiner-II: a tool for rapid identification and configuration of repetitive element arrays from large mammalian chromosomes as a single query.

Genes occupy ~3% of the human and mouse genomes whereas repetitive elements (REs), whose biologic functions are largely uncharacterized, constitute greater than 50%. A heterogeneous population of RE arrays (arrangement structures) is formed by combinations of various REs in mammalian genomes. In this study, REMiner-II was refined from the original REMiner for a more efficient identification and configuration of RE arrays from large queries (e.g., human chromosomes) using an unbiased self-alignment protocol. Chromosome-wide RE array profiles for the entire sets of human and mouse chromosomes were obtained using REMiner-II on a personal computer. REMiner-II provides 10 adjustable parameters and three data output modes to accommodate different experimental settings and/or goals. Examination of the human and mouse chromosome data using the REMiner-II viewer revealed species-specific libraries of complexly organized RE arrays. In conclusion, REMiner-II is an efficient tool for chromosome-wide identification and characterization of RE arrays from mammalian genomes.

REMiner: a tool for unbiased mining and analysis of repetitive elements and their arrangement structures of large chromosomes.

Repetitive elements (REs) constitute a substantial portion of the genomes of human and other species; however, the RE profiles (type, density, and arrangement) within the individual genomes have not been fully characterized. In this study, we developed an RE analysis tool, called REMiner, for a chromosome-wide investigation into the occurrence of individual REs and arrangement of clusters of REs, and REMiner's functional features were examined using the human chromosome Y. The algorithm implemented by REMiner focused on unbiased mining of REs in large chromosomes and data interface within a viewer. The data from the chromosome demonstrated that REMiner is an efficient tool in regard to its capacity for a large query size and the availability of a high-resolution viewer, featuring instant retrieval of alignment data and control of magnification and identity ratio. The chromosome-wide survey identified a diverse population of ordered RE arrangements, which may participate in the genome biology.