Bridging heterogeneous mutation data to enhance disease gene discovery.

Bridging heterogeneous mutation data fills in the gap between various data categories and propels discovery of disease-related genes. It is known that genome-wide association study (GWAS) infers significant mutation associations that link genotype and phenotype. However, due to the differences of size and quality between GWAS studies, not all de facto vital variations are able to pass the multiple testing. In the meantime, mutation events widely reported in literature unveil typical functional biological process, including mutation types like gain of function and loss of function. To bring together the heterogeneous mutation data, we propose a 'Gene-Disease Association prediction by Mutation Data Bridging (GDAMDB)' pipeline with a statistic generative model. The model learns the distribution parameters of mutation associations and mutation types and recovers false-negative GWAS mutations that fail to pass significant test but represent supportive evidences of functional biological process in literature. Eventually, we applied GDAMDB in Alzheimer's disease (AD) and predicted 79 AD-associated genes. Besides, 12 of them from the original GWAS, 60 of them are supported to be AD-related by other GWAS or literature report, and rest of them are newly predicted genes. Our model is capable of enhancing the GWAS-based gene association discovery by well combining text mining results. The positive result indicates that bridging the heterogeneous mutation data is contributory for the novel disease-related gene discovery.

High-quality gene/disease embedding in a multi-relational heterogeneous graph after a joint matrix/tensor decomposition.

MOTIVATION: Node embedding of biological entity network has been widely investigated for the downstream application scenarios. To embed full semantics of gene and disease, a multi-relational heterogeneous graph is considered in a scenario where uni-relation between gene/disease and other heterogeneous entities are abundant while multi-relation between gene and disease is relatively sparse. After introducing this novel graph format, it is illuminative to design a specific data integration algorithm to fully capture the graph information and bring embeddings with high quality. RESULTS: First, a typical multi-relational triple dataset was introduced, which carried significant association between gene and disease. Second, we curated all human genes and diseases in seven mainstream datasets and constructed a large-scale gene-disease network, which compromising 163,024 nodes and 25,265,607 edges, and relates to 27,165 genes, 2,665 diseases, 15,067 chemicals, 108,023 mutations, 2,363 pathways, and 7.732 phenotypes. Third, we proposed a Joint Decomposition of Heterogeneous Matrix and Tensor (JDHMT) model, which integrated all heterogeneous data resources and obtained embedding for each gene or disease. Forth, a visualized intrinsic evaluation was performed, which investigated the embeddings in terms of interpretable data clustering. Furthermore, an extrinsic evaluation was performed in the form of linking prediction. Both intrinsic and extrinsic evaluation results showed that JDHMT model outperformed other eleven state-of-the-art (SOTA) methods which are under relation-learning, proximity-preserving or message-passing paradigms. Finally, the constructed gene-disease network, embedding results and codes were made available. DATA AND CODES AVAILABILITY: The constructed massive gene-disease network is available at: https://hzaubionlp.com/heterogeneous-biological-network/. The codes are available at: https://github.com/bionlp-hzau/JDHMT.

PubCaseFinder: A Case-Report-Based, Phenotype-Driven Differential-Diagnosis System for Rare Diseases.

Recently, to speed up the differential-diagnosis process based on symptoms and signs observed from an affected individual in the diagnosis of rare diseases, researchers have developed and implemented phenotype-driven differential-diagnosis systems. The performance of those systems relies on the quantity and quality of underlying databases of disease-phenotype associations (DPAs). Although such databases are often developed by manual curation, they inherently suffer from limited coverage. To address this problem, we propose a text-mining approach to increase the coverage of DPA databases and consequently improve the performance of differential-diagnosis systems. Our analysis showed that a text-mining approach using one million case reports obtained from PubMed could increase the coverage of manually curated DPAs in Orphanet by 125.6%. We also present PubCaseFinder (see Web Resources), a new phenotype-driven differential-diagnosis system in a freely available web application. By utilizing automatically extracted DPAs from case reports in addition to manually curated DPAs, PubCaseFinder improves the performance of automated differential diagnosis. Moreover, PubCaseFinder helps clinicians search for relevant case reports by using phenotype-based comparisons and confirm the results with detailed contextual information.

Open Agile text mining for bioinformatics: the PubAnnotation ecosystem.

MOTIVATION: Most currently available text mining tools share two characteristics that make them less than optimal for use by biomedical researchers: they require extensive specialist skills in natural language processing and they were built on the assumption that they should optimize global performance metrics on representative datasets. This is a problem because most end-users are not natural language processing specialists and because biomedical researchers often care less about global metrics like F-measure or representative datasets than they do about more granular metrics such as precision and recall on their own specialized datasets. Thus, there are fundamental mismatches between the assumptions of much text mining work and the preferences of potential end-users. RESULTS: This article introduces the concept of Agile text mining, and presents the PubAnnotation ecosystem as an example implementation. The system approaches the problems from two perspectives: it allows the reformulation of text mining by biomedical researchers from the task of assembling a complete system to the task of retrieving warehoused annotations, and it makes it possible to do very targeted customization of the pre-existing system to address specific end-user requirements. Two use cases are presented: assisted curation of the GlycoEpitope database, and assessing coverage in the literature of pre-eclampsia-associated genes. AVAILABILITY AND IMPLEMENTATION: The three tools that make up the ecosystem, PubAnnotation, PubDictionaries and TextAE are publicly available as web services, and also as open source projects. The dictionaries and the annotation datasets associated with the use cases are all publicly available through PubDictionaries and PubAnnotation, respectively.

A Model to Predict 1-Month Risk of Transplant or Death in Hepatitis A-Related Acute Liver Failure.

Acute liver failure (ALF) caused by hepatitis A is a rare but fatal disease. Here, we developed a model to predict outcome in patients with ALF caused by hepatitis A. The derivation set consisted of 294 patients diagnosed with hepatitis A-related ALF (ALFA) from Korea, and a validation set of 56 patients from Japan, India, and United Kingdom. Using a multivariate proportional hazard model, a risk-prediction model (ALFA score) consisting of age, international normalized ratio, bilirubin, ammonia, creatinine, and hemoglobin levels acquired on the day of ALF diagnosis was developed. The ALFA score showed the highest discrimination in the prediction of liver transplant or death at 1 month (c-statistic, 0.87; 95% confidence interval [CI], 0.84-0.92) versus King's College criteria (KCC; c-statistic, 0.56; 95% CI, 0.53-0.59), U.S. Acute Liver Failure Study Group index specific for hepatitis A virus (HAV-ALFSG; c-statistic, 0.70; 95% CI, 0.65-0.76), the new ALFSG index (c-statistic, 0.79; 95% CI, 0.74-0.84), Model for End-Stage Liver Disease (MELD; c-statistic, 0.79; 95% CI, 0.74-0.84), and MELD including sodium (MELD-Na; c-statistic, 0.78; 95% CI, 0.73-0.84) in the derivation set (all P < 0.01). In the validation set, the performance of the ALFA score (c-statistic, 0.84; 95% CI, 0.74-0.94) was significantly better than that of KCC (c-statistic, 0.65; 95% CI, 0.52-0.79), MELD (c-statistic, 0.74; 95% CI, 0.61-0.87), and MELD-Na (c-statistic, 0.72; 95% CI, 0.58-0.85) (all P < 0.05), and better, but not statistically significant, than that of the HAV-ALFSG (c-statistic, 0.76; 95% CI, 0.61-0.90; P = 0.28) and new ALFSG indices (c-statistic, 0.79; 95% CI, 0.65-0.93; P = 0.41). The model was well-calibrated in both sets. Conclusion: Our disease-specific score provides refined prediction of outcome in patients with ALF caused by hepatitis A.

Serum cystatin C level: An excellent predictor of mortality in patients with cirrhotic ascites.

BACKGROUND AND AIM: Although serum cystatin C level is considered a more accurate marker of renal function in patients with liver cirrhosis, its prognostic efficacy remains uncertain. This study aimed to evaluate the prognostic efficacy of serum cystatin C level in patients with cirrhotic ascites. METHODS: Patients with cirrhotic ascites from 15 hospitals were prospectively enrolled between September 2009 and March 2013. Cox regression analyses were performed to identify independent predictive factors of mortality and development of type 1 hepatorenal syndrome (HRS-1). RESULTS: In total, 350 patients were enrolled in this study. The mean age was 55.4 ± 10.8 years, and 267 patients (76.3%) were men. The leading cause of liver cirrhosis was alcoholic liver disease (64.3%), followed by chronic viral hepatitis (29.7%). Serum creatinine and cystatin C levels were 0.9 ± 0.4 mg/dL and 1.1 ± 0.5 mg/L, respectively. Multivariate analyses revealed that international normalized ratio and serum bilirubin, sodium, and cystatin C levels were independent predictors of mortality and international normalized ratio and serum sodium and cystatin C levels were independent predictors of the development of HRS-1. Serum creatinine level was not significantly associated with mortality and development of HRS-1 on multivariate analysis. CONCLUSION: Serum cystatin C level was an independent predictor of mortality and development of HRS-1 in patients with cirrhotic ascites, while serum creatinine level was not. Predictive models based on serum cystatin C level instead of serum creatinine level would be more helpful in the assessment of the condition and prognosis of patients with cirrhotic ascites.

Total Colonic Decompression After Colonoscopy Decreases Postcolonoscopy Abdominal Pain: A Randomized Double-Blind Controlled Trial.

GOALS: The purpose of this study was to determine whether total colonic decompression after colonoscopy decreased postcolonoscopy abdominal pain. BACKGROUND: Abdominal pain that occurs after a colonoscopy may cause significant discomfort in some patients, and residual bowel gas is thought to be a key contributor to this abdominal pain. STUDY: Asymptomatic 300 patients who underwent colonoscopy under sedation were randomized to either the decompression group or the control group. Initial colonoscopic procedure was performed uniformly in both the groups. After the colonoscopy examination was completed, the colonoscope was reinserted into the cecum, and the intraluminal air was aspirated during withdrawal in the decompression group. Abdominal pain was assessed before discharge and 24 to 48 hours after colonoscopy using a 10-point visual analogue scale (VAS). RESULTS: The 2 groups were similar with regard to clinical, demographic, and procedural factors. Among 288 patients, the incidence of abdominal pain (VAS≥1) after colonoscopy was 38 (26.6%) of 143 patients in the decompression group and 95 (65.5%) of 145 patients in the control group (VAS 0.68±1.35 vs. 2.14±2.15, P<0.001). There was an 86.1% reduction rate of abdominal pain by colonic decompression, based on multivariate analysis (odds ratio 0.139 [95% confidence interval, 0.077-0.250], P<0.001). Furthermore, independent factors for abdominal pain included female gender and total duration of procedure >800 seconds. There were no reinsertion-related complications in the decompression group. CONCLUSION: Total colonic decompression after colonoscopy has a beneficial effect and can reduce postcolonoscopy abdominal pain without additional complications.

Extending the evaluation of Genia Event task toward knowledge base construction and comparison to Gene Regulation Ontology task.

BACKGROUND: The third edition of the BioNLP Shared Task was held with the grand theme "knowledge base construction (KB)". The Genia Event (GE) task was re-designed and implemented in light of this theme. For its final report, the participating systems were evaluated from a perspective of annotation. To further explore the grand theme, we extended the evaluation from a perspective of KB construction. Also, the Gene Regulation Ontology (GRO) task was newly introduced in the third edition. The final evaluation of the participating systems resulted in relatively low performance. The reason was attributed to the large size and complex semantic representation of the ontology. To investigate potential benefits of resource exchange between the presumably similar tasks, we measured the overlap between the datasets of the two tasks, and tested whether the dataset for one task can be used to enhance performance on the other. RESULTS: We report an extended evaluation on all the participating systems in the GE task, incoporating a KB perspective. For the evaluation, the final submission of each participant was converted to RDF statements, and evaluated using 8 queries that were formulated in SPARQL. The results suggest that the evaluation may be concluded differently between the two different perspectives, annotation vs. KB. We also provide a comparison of the GE and GRO tasks by converting their datasets into each other's format. More than 90% of the GE data could be converted into the GRO task format, while only half of the GRO data could be mapped to the GE task format. The imbalance in conversion indicates that the GRO is a comprehensive extension of the GE task ontology. We further used the converted GRO data as additional training data for the GE task, which helped improve GE task participant system performance. However, the converted GE data did not help GRO task participants, due to overfitting and the ontology gap.

Clinical significance of hepatitis B virus precore and core promoter variants in Korean patients with chronic hepatitis B.

BACKGROUND/AIM: We aimed to clarify the clinical significance of precore (preC)/core promoter (CP) variants of hepatitis B virus (HBV) in chronic hepatitis B (CHB) patients. METHODS: We assessed serum HBeAg, HBV DNA levels, alanine transferase (ALT) levels, and progression of liver fibrosis in 226 Korean CHB patients, presumed to be infected with genotype C HBV, to analyze HBV variants in the preC region (G1896A) and CP regions (A1762T, G1764A). RESULTS: CP and preC variants were more frequently found in HBeAg-negative patients than in HBeAg-positive patients (P<0.05). HBeAg-positive patients with CP variants had higher ALT levels and more advanced fibrosis scores (all P<0.01) than those without variants; those with preC variant had lower HBV DNA levels (P=0.009), with no significant difference in ALT levels and fibrosis scores. However, no significant correlation was found between HBV variants and clinicopathologic findings in HBeAg-negative patients. Furthermore, multivariate analysis revealed that (1) progression of liver fibrosis (≥F2) was associated with older age in both HBeAg-positive and HBeAg-negative patients (P<0.05) and with CP variants in the HBeAg-positive group (P=0.007), and (2) HBV DNA levels were positively correlated with ALT levels, irrespective of HBeAg (P<0.05), whereas they were negatively correlated with the presence of preC variant in the HBeAg-positive group (P=0.004). CONCLUSIONS: In HBeAg-positive CHB patients infected with genotype C HBV, preC variant was associated with enhanced host immune response with lower HBV DNA levels, whereas CP variants were associated with severe liver damage and liver fibrosis progression.

[Acute Liver Failure: Current Updates and Management].

Acute liver failure (ALF) is a rare disease condition with a dynamic clinical course and catastrophic outcomes. Several etiologies are involved in ALF. Hepatitis A and B infections and indiscriminate use of untested herbs or supplemental agents are the most common causes of ALF in Korea. Noninvasive neurological monitoring tools have been used in patients with ALF in recent times. Ongoing improvements in intensive care, including continuous renal replacement therapy, therapeutic plasma exchange, vasopressor, and extracorporeal membrane oxygenation, have reduced the mortality rate of patients with ALF. However, liver transplantation is still the most effective treatment for patients with intractable ALF. There is a need for further research in the areas of better prognostication and precise selection of patients for emergency transplantation.

Efficacy and safety of glecaprevir/pibrentasvir treatment in Koreans with chronic hepatitis C: A retrospective study.

BACKGROUND AND STUDY AIMS: The introduction of direct-acting antiviral (DAA) drugs has dramatically improved chronic hepatitis C (CHC) treatment. The pangenotype DAA therapy glecaprevir/pibrentasvir (G/P) was recently recommended for treating CHC in Korea. Unfortunately, given its recent introduction, little real-world data from a Korean population exists. We examined the effectiveness and safety of G/P treatment in Koreans with CHC. PATIENTS AND METHODS: We analyzed CHC patients at Samsung Changwon Hospital from June 2018 to December 2020. Sustained virologic response at 12 weeks posttreatment (SVR 12) was evaluated after treatment, and the associated factors were analyzed. Furthermore, the degree of liver fibrosis before and after treatment was compared to determine whether liver fibrosis improved. RESULTS: In total, 102 patients were enrolled; 35.3 % had compensated liver cirrhosis (LC), and 11.8 % had received previous treatment. Of the 102 patients, 99 (97.1 %) reached SVR 12. Of the 81 patients who completed 8 weeks of G/P treatment, 80 (98.8 %) reached SVR 12, while 19 of the 21 (90.5 %) patients in the 12- or 16-week group reached SVR 12, with no significant difference between the two groups (P = 0.107). As a secondary endpoint, liver fibrosis before and after treatment was also compared. The Fibrosis-4 index (FIB-4) (3.3 vs 2.8, P = 0.010), aspartate transaminase (AST)-platelet ratio index (APRI) (1.3 vs 1.0, P < 0.001), and liver stiffness measurements (LSM) (9.5 vs 4.6, P < 0.001) were significantly different after G/P treatment. CONCLUSIONS: Regardless of genotype, G/P treatment for Koreans with CHC is safe, highly effective, and can improve liver fibrosis.

Knowledge-Driven Mechanistic Enrichment of the Preeclampsia Ignorome.

Preeclampsia is a leading cause of maternal and fetal morbidity and mortality. Currently, the only definitive treatment of preeclampsia is delivery of the placenta, which is central to the pathogenesis of the disease. Transcriptional profiling of human placenta from pregnancies complicated by preeclampsia has been extensively performed to identify differentially expressed genes (DEGs). The decisions to investigate DEGs experimentally are biased by many factors, causing many DEGs to remain uninvestigated. A set of DEGs which are associated with a disease experimentally, but which have no known association to the disease in the literature are known as the ignorome. Preeclampsia has an extensive body of scientific literature, a large pool of DEG data, and only one definitive treatment. Tools facilitating knowledge-based analyses, which are capable of combining disparate data from many sources in order to suggest underlying mechanisms of action, may be a valuable resource to support discovery and improve our understanding of this disease. In this work we demonstrate how a biomedical knowledge graph (KG) can be used to identify novel preeclampsia molecular mechanisms. Existing open source biomedical resources and publicly available high-throughput transcriptional profiling data were used to identify and annotate the function of currently uninvestigated preeclampsia-associated DEGs. Experimentally investigated genes associated with preeclampsia were identified from PubMed abstracts using text-mining methodologies. The relative complement of the text-mined- and meta-analysis-derived lists were identified as the uninvestigated preeclampsia-associated DEGs (n=445), i.e., the preeclampsia ignorome. Using the KG to investigate relevant DEGs revealed 53 novel clinically relevant and biologically actionable mechanistic associations.

Improving protein coreference resolution by simple semantic classification.

BACKGROUND: Current research has shown that major difficulties in event extraction for the biomedical domain are traceable to coreference. Therefore, coreference resolution is believed to be useful for improving event extraction. To address coreference resolution in molecular biology literature, the Protein Coreference (COREF) task was arranged in the BioNLP Shared Task (BioNLP-ST, hereafter) 2011, as a supporting task. However, the shared task results indicated that transferring coreference resolution methods developed for other domains to the biological domain was not a straight-forward task, due to the domain differences in the coreference phenomena. RESULTS: We analyzed the contribution of domain-specific information, including the information that indicates the protein type, in a rule-based protein coreference resolution system. In particular, the domain-specific information is encoded into semantic classification modules for which the output is used in different components of the coreference resolution. We compared our system with the top four systems in the BioNLP-ST 2011; surprisingly, we found that the minimal configuration had outperformed the best system in the BioNLP-ST 2011. Analysis of the experimental results revealed that semantic classification, using protein information, has contributed to an increase in performance by 2.3% on the test data, and 4.0% on the development data, in F-score. CONCLUSIONS: The use of domain-specific information in semantic classification is important for effective coreference resolution. Since it is difficult to transfer domain-specific information across different domains, we need to continue seek for methods to utilize such information in coreference resolution.

The Genia Event and Protein Coreference tasks of the BioNLP Shared Task 2011.

BACKGROUND: The Genia task, when it was introduced in 2009, was the first community-wide effort to address a fine-grained, structural information extraction from biomedical literature. Arranged for the second time as one of the main tasks of BioNLP Shared Task 2011, it aimed to measure the progress of the community since 2009, and to evaluate generalization of the technology to full text papers. The Protein Coreference task was arranged as one of the supporting tasks, motivated from one of the lessons of the 2009 task that the abundance of coreference structures in natural language text hinders further improvement with the Genia task. RESULTS: The Genia task received final submissions from 15 teams. The results show that the community has made a significant progress, marking 74% of the best F-score in extracting bio-molecular events of simple structure, e.g., gene expressions, and 45% ~ 48% in extracting those of complex structure, e.g., regulations. The Protein Coreference task received 6 final submissions. The results show that the coreference resolution performance in biomedical domain is lagging behind that in newswire domain, cf. 50% vs. 66% in MUC score. Particularly, in terms of protein coreference resolution the best system achieved 34% in F-score. CONCLUSIONS: Detailed analysis performed on the results improves our insight into the problem and suggests the directions for further improvements.

Discriminative application of string similarity methods to chemical and non-chemical names for biomedical abbreviation clustering.

BACKGROUND: Term clustering, by measuring the string similarities between terms, is known within the natural language processing community to be an effective method for improving the quality of texts and dictionaries. However, we have observed that chemical names are difficult to cluster using string similarity measures. In order to clearly demonstrate this difficulty, we compared the string similarities determined using the edit distance, the Monge-Elkan score, SoftTFIDF, and the bigram Dice coefficient for chemical names with those for non-chemical names. RESULTS: Our experimental results revealed the following: (1) The edit distance had the best performance in the matching of full forms, whereas Cohen et al. reported that SoftTFIDF with the Jaro-Winkler distance would yield the best measure for matching pairs of terms for their experiments. (2) For each of the string similarity measures above, the best threshold for term matching differs for chemical names and for non-chemical names; the difference is especially large for the edit distance. (3) Although the matching results obtained for chemical names using the edit distance, Monge-Elkan scores, or the bigram Dice coefficients are better than the result obtained for non-chemical names, the results were contrary when using SoftTFIDF. (4) A suitable weight for chemical names varies substantially from one for non-chemical names. In particular, a weight vector that has been optimized for non-chemical names is not suitable for chemical names. (5) The matching results using the edit distances improve further by dividing a set of full forms into two subsets, according to whether a full form is a chemical name or not. These results show that our hypothesis is acceptable, and that we can significantly improve the performance of abbreviation-full form clustering by computing chemical names and non-chemical names separately. CONCLUSIONS: In conclusion, the discriminative application of string similarity methods to chemical and non-chemical names may be a simple yet effective way to improve the performance of term clustering.

AGRA: analysis of gene ranking algorithms.

UNLABELLED: Often, the most informative genes have to be selected from different gene sets and several computer gene ranking algorithms have been developed to cope with the problem. To help researchers decide which algorithm to use, we developed the analysis of gene ranking algorithms (AGRA) system that offers a novel technique for comparing ranked lists of genes. The most important feature of AGRA is that no previous knowledge of gene ranking algorithms is needed for their comparison. Using the text mining system finding-associated concepts with text analysis. AGRA defines what we call biomedical concept space (BCS) for each gene list and offers a comparison of the gene lists in six different BCS categories. The uploaded gene lists can be compared using two different methods. In the first method, the overlap between each pair of two gene lists of BCSs is calculated. The second method offers a text field where a specific biomedical concept can be entered. AGRA searches for this concept in each gene lists' BCS, highlights the rank of the concept and offers a visual representation of concepts ranked above and below it. AVAILABILITY AND IMPLEMENTATION: Available at http://agra.fzv.uni-mb.si/, implemented in Java and running on the Glassfish server. CONTACT: simon.kocbek@uni-mb.si.

Psychometric hepatic encephalopathy score for the detection of minimal hepatic encephalopathy in Korean patients with liver cirrhosis.

BACKGROUND AND AIM: Although the Psychometric Hepatic Encephalopathy Score (PHES) for the diagnosis of minimal hepatic encephalopathy (MHE) has been validated in several countries, further validation is required for its use in different populations. The aims of this study were thus to standardize the PHES in a healthy Korean population and evaluate the prevalence of MHE among Korean patients with liver cirrhosis. METHODS: Two-hundred healthy subjects without evidence of liver disease and 160 patients with liver cirrhosis without overt HE were included. Blood sampling for routine laboratory tests and determination of venous ammonia concentration was performed on the day of PHES neuropsychological testing. RESULTS: The age and education years of the control group were 41 ± 13 years and 13 ± 3 years, respectively; 100 of the subjects (50.0%) were men. The PHES for the control group was -0.31 ± 2.06 and the normal range was thus set at > -5 points. The age and education years of the liver cirrhosis group were 55 ± 8 and 11 ± 4 years, respectively; 102 of those in this group (63.8%) were men. Of the liver cirrhosis patients, 129 (80.6%), 21 (13.1%), and 10 (6.3%) had Child-Pugh grades A, B, and C, respectively. The PHES of the liver cirrhosis group was -2.94 ± 3.39. MHE was diagnosed in 41 patients (25.6%), of which 26 (20.2%), nine (42.9%), and six (60.0%) had Child-Pugh grades A, B, and C, respectively. CONCLUSIONS: The PHES was useful for detecting patients with MHE. A significant proportion of Korean patients with liver cirrhosis suffer from MHE.

Prognosis of hepatitis B-related liver cirrhosis in the era of oral nucleos(t)ide analog antiviral agents.

BACKGROUND AND AIM: We investigated long-term outcomes and prognostic factors in patients with hepatitis B virus (HBV)-related liver cirrhosis in the era of oral nucleos(t)ide analog antiviral agents. METHODS: Between January 1999 and February 2009, a total of 240 consecutive patients who had HBV-related cirrhosis without malignancy were treated with lamivudine and second line nucleos(t)ide analogs. The group of historical controls consisted of 481 consecutive patients with HBV-related cirrhosis who were managed without any antiviral treatment prior to 1999. RESULTS: In 78% of the patients who received antiviral treatment, sustained viral suppression (serum HBV DNA < 10(5) copies/mL) was achieved during a mean follow-up period of 46 months. The occurrences of death, hepatic decompensation, and hepatocellular carcinoma (HCC) were less frequent in the treated cohort than in untreated historical controls, with the 5-year cumulative incidences being 19.4% versus 43.9% (log-rank P < 0.001), 15.4% versus 45.4% (P = 0.001), and 13.8% versus 23.4% (P = 0.074), respectively. For patients who received antiviral treatment, suboptimal viral suppression (HBV DNA > 10(5) copies/mL at last follow-up) was an important independent risk factor of death (P < 0.001) and hepatic decompensation (P = 0.019), and was linked to an increased risk of HCC (P = 0.042). Although the Child-Pugh grade remained a useful prognostic factor, no significant differences were found between patients with Child-Pugh grade B and C cirrhosis at the beginning of antiviral treatment (P = 0.656). CONCLUSIONS: Oral antiviral agents have improved the prognosis of patients with HBV-related cirrhosis and affected the prognostic values of factors constituting the Child-Pugh system, necessitating a more efficient prognostic system.

U-Compare bio-event meta-service: compatible BioNLP event extraction services.

BACKGROUND: Bio-molecular event extraction from literature is recognized as an important task of bio text mining and, as such, many relevant systems have been developed and made available during the last decade. While such systems provide useful services individually, there is a need for a meta-service to enable comparison and ensemble of such services, offering optimal solutions for various purposes. RESULTS: We have integrated nine event extraction systems in the U-Compare framework, making them intercompatible and interoperable with other U-Compare components. The U-Compare event meta-service provides various meta-level features for comparison and ensemble of multiple event extraction systems. Experimental results show that the performance improvements achieved by the ensemble are significant. CONCLUSIONS: While individual event extraction systems themselves provide useful features for bio text mining, the U-Compare meta-service is expected to improve the accessibility to the individual systems, and to enable meta-level uses over multiple event extraction systems such as comparison and ensemble.

A Cross-Sectional Study of the Effects of Physical Activity and Nutrient Intakes on Blood Glucose Control Rates in Middle-Aged and Elderly Diabetes Patients: Korean National Health and Nutrition Examination Survey 2015-2017.

This study aimed to investigate factors affecting blood glucose control among middle-aged and older diabetic patients taking medications or receiving insulin therapy. In 2015-2017 data obtained from the Korean National Health and Nutrition Examination Survey (KNHANES), 1257 patients with diabetes were divided into a controlled group and an uncontrolled group based on blood glucose levels (cutoff ≥126 mg/dL). After adjusting for confounding factors, the BMI, total cholesterol level, and triglycerides level of the uncontrolled group were significantly higher than the controlled group. The total amount of moderate-intensity activity in controlled patients was significantly higher than that of the controlled group. Total energy, fat, saturated fatty acids, and cholesterol intakes were found to be significantly higher in the uncontrolled than controlled group. Intakes of calcium, phosphorus, potassium, riboflavin, niacin, and vitamin C were significantly lower in the uncontrolled than controlled group. Adequate nutrition intake and physical activity of patients undergoing diabetes therapy are required for effective blood glucose management for both diabetic drug and insulin therapies.