Clinical metabolomics characteristics of diabetic kidney disease: A meta-analysis of 1875 cases with diabetic kidney disease and 4503 controls.

AIMS: Diabetic Kidney Disease (DKD), one of the major complications of diabetes, is also a major cause of end-stage renal disease. Metabolomics can provide a unique metabolic profile of the disease and thus predict or diagnose the development of the disease. Therefore, this study summarises a more comprehensive set of clinical biomarkers related to DKD to identify functional metabolites significantly associated with the development of DKD and reveal their driving mechanisms for DKD. MATERIALS AND METHODS: We searched PubMed, Embase, the Cochrane Library and Web of Science databases through October 2022. A meta-analysis was conducted on untargeted or targeted metabolomics research data based on the strategy of standardized mean differences and the process of ratio of means as the effect size, respectively. We compared the changes in metabolite levels between the DKD patients and the controls and explored the source of heterogeneity through subgroup analyses, sensitivity analysis and meta-regression analysis. RESULTS: The 34 clinical-based metabolomics studies clarified the differential metabolites between DKD and controls, containing 4503 control subjects and 1875 patients with DKD. The results showed that a total of 60 common differential metabolites were found in both meta-analyses, of which 5 metabolites (p < 0.05) were identified as essential metabolites. Compared with the control group, metabolites glycine, aconitic acid, glycolic acid and uracil decreased significantly in DKD patients; cysteine was significantly higher. This indicates that amino acid metabolism, lipid metabolism and pyrimidine metabolism in DKD patients are disordered. CONCLUSIONS: We have identified 5 metabolites and metabolic pathways related to DKD which can serve as biomarkers or targets for disease prevention and drug therapy.

Leveraging a pharmacogenomics knowledgebase to formulate a drug response phenotype terminology for genomic medicine.

MOTIVATION: Despite the increasing evidence of utility of genomic medicine in clinical practice, systematically integrating genomic medicine information and knowledge into clinical systems with a high-level of consistency, scalability and computability remains challenging. A comprehensive terminology is required for relevant concepts and the associated knowledge model for representing relationships. In this study, we leveraged PharmGKB, a comprehensive pharmacogenomics (PGx) knowledgebase, to formulate a terminology for drug response phenotypes that can represent relationships between genetic variants and treatments. We evaluated coverage of the terminology through manual review of a randomly selected subset of 200 sentences extracted from genetic reports that contained concepts for 'Genes and Gene Products' and 'Treatments'. RESULTS: Results showed that our proposed drug response phenotype terminology could cover 96% of the drug response phenotypes in genetic reports. Among 18 653 sentences that contained both 'Genes and Gene Products' and 'Treatments', 3011 sentences were able to be mapped to a drug response phenotype in our proposed terminology, among which the most discussed drug response phenotypes were response (994), sensitivity (829) and survival (332). In addition, we were able to re-analyze genetic report context incorporating the proposed terminology and enrich our previously proposed PGx knowledge model to reveal relationships between genetic variants and treatments. In conclusion, we proposed a drug response phenotype terminology that enhanced structured knowledge representation of genomic medicine. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Examining an integrated sociocultural and objectification model of thinness- and muscularity-oriented disordered eating in Chinese older men and women.

OBJECTIVE: We tested an integrated model of three prominent theories of disordered eating (tripartite influence theory, objectification theory, and social comparison theory) in a sample of older Chinese men and women. METHOD: Chinese older men (n = 270) and women (n = 160) completed questionnaires assessing the tripartite influence, objectification, and social comparison theories and thinness- and muscularity-oriented disordered eating. Two structural equation models were tested in Chinese older men and women. RESULTS: The integrated model showed good model fit and described meaningful variance in thinness- and muscularity-oriented disordered eating in Chinese older men and women. Higher appearance pressures were uniquely related to higher muscularity-oriented disordered eating in men. Across both gender groups, higher thinness internalization was uniquely related to higher thinness- and muscularity-oriented disordered eating, and in women only, higher muscularity internalization was uniquely related to lower thinness-oriented disordered eating. In men, higher upward and downward body image comparisons were uniquely related to higher and lower, respectively, muscularity-oriented disordered eating. In women, higher upward body image comparisons were only uniquely related to higher muscularity-oriented disordered eating while higher downward body image comparisons were uniquely related to both outcomes. Higher body shame was uniquely related to higher thinness-oriented disordered eating across both groups and in men alone, higher body shame was also uniquely related to higher muscularity-oriented disordered eating. DISCUSSION: Findings, which tested the integration of tripartite influence, objectification, and social comparison theories, inform the prevention and treatment of disordered eating in Chinese older populations. PUBLIC SIGNIFICANCE: The present study is the first to describe theories of disordered eating (tripartite influence, objectification, and social comparison) in Chinese older adults. Findings suggested good model fit and the integrated models described meaningful variance in thinness- and muscularity-oriented disordered eating in Chinese older women and men. Findings extend existing theories of disordered eating and, pending further study, may inform theory-driven prevention and treatment approaches in Chinese older adults.

An Integrated Extraction-Purification Process for Raspberry Leaf Polyphenols and Their In Vitro Activities.

To improve the utilization value of raspberry leaves, the extraction and purification conditions of phenolic compounds from raspberry leaves were optimized, and the contents of phenolic compounds and the biological activities of extracts were studied. After steam explosion pretreatment at 115 °C for 15 min, raspberry leaf extract with a total phenolic content (TPC) of 136.30~140.51 mg GAE/g was obtained via homogenization and ultrasound-assisted extraction. In addition, the adsorption relationship between raspberry leaf polyphenols and middle polar XDA-6 macroporous resin was best described by the Langmuir model, and tended to be monolayer adsorption. Its adsorption kinetics best resembled the pseudo second-order kinetic model, and it was speculated that this was influenced by multiple factors. According to the optimal integrated extraction-purification process, the TPC of the extracts increased to 738.98 mg GAE/g after one application of purification and 905.27 mg GAE/g after two applications of purification. Moreover, the latter case showed the highest antioxidant activity and α-glucosidase inhibition activity, and the content of the most typical compound, quercetin-3-glucuronide, reached 199.69 mg/g. SE has a double-edged effect, and is more conducive to the release of active substances as a pre-treatment method. This study provides a theoretical basis for the efficient use of raspberry leaves, further improving their medicinal and economic value.

Transcriptome-wide N6-methyladenine methylation in granulosa cells of women with decreased ovarian reserve.

BACKGROUND: The emerging epitranscriptome plays an essential role in female fertility. As the most prevalent internal mRNA modification, N6-methyladenine (m6A) methylation regulate mRNA fate and translational efficiency. However, whether m6A methylation was involved in the aging-related ovarian reserve decline has not been investigated. Herein, we performed m6A transcriptome-wide profiling in the ovarian granulosa cells of younger women (younger group) and older women (older group). RESULTS: m6A methylation distribution was highly conserved and enriched in the CDS and 3'UTR region. Besides, an increased number of m6A methylated genes were identified in the older group. Bioinformatics analysis indicated that m6A methylated genes were enriched in the FoxO signaling pathway, adherens junction, and regulation of actin cytoskeleton. A total of 435 genes were differently expressed in the older group, moreover, 58 of them were modified by m6A. Several specific genes, including BUB1B, PHC2, TOP2A, DDR2, KLF13, and RYR2 which were differently expressed and modified by m6A, were validated using qRT-PCR and might be involved in the decreased ovarian functions in the aging ovary. CONCLUSIONS: Hence, our finding revealed the transcriptional significance of m6A modifications and provide potential therapeutic targets to promote fertility reservation for aging women.

Parental chest computerized tomography examination before IVF/ICSI has no impact on pregnancy and neonatal outcomes: a cohort study of 2680 fresh transfer cycles.

BACKGROUND: Some concern has been expressed regarding the negative effects of low-level ionizing radiation exposure in the context of radiological evaluation prior to IVF/ICSI treatment, but the available evidence is limited and conflicting. The aim of this study is to evaluate pregnancy and neonatal outcomes of couples who did chest computed tomography (CT) prior to IVF/ICSI. METHODS: This was a retrospective cohort study of 2680 IVF/ICSI fresh embryo transfer cycles conducted from January 2019 - August 2020. Fertility outcomes were compared between couples that had or had not undergone CT examination within 3 months prior to the date of oocyte retrieval and sperm collection. Miscarriage was the primary study outcome, while secondary outcomes included the number of oocytes collected, oocyte maturation, normal fertilization, number of good quality cleavage stage embryos, blastocyst formation, implantation, clinical pregnancy, ectopic pregnancy, live birth, multiple birth, Cesarean section rates, gestational weeks, maternal obstetric complications, birth weight, newborn sex ratio, and birth defect incidence. Propensity score matching was used to control for potential confounding variables. RESULTS: Of the 2680 cycles included in this study, couples underwent CT examination in 731 cycles. After 1:1 propensity score matching, 670 cycles were included in each group. When comparing demographic and fertility-related variables between groups that had and had not undergone CT examination after propensity score matching, we detected no significant differences in miscarriage rates (16.99% vs. 15.77%, OR = 1.10, 95CI% = 0.74 to 1.68). Similarly, both groups exhibited comparable oocyte and embryonic development, implantation rates (41.99% vs. 40.42%, OR = 1.07, 95%CI = 0.87 to 1.31), clinical pregnancy rates (45.67% vs. 44.48%, OR = 1.05, 95%CI = 0.85 to 1.30), ectopic pregnancy rates (2.94% vs. 1.68%, OR = 1.78, 95%CI = 0.59 to 5.36), live birth rates (36.57% vs. 35.67%, OR = 1.04, 95%CI = 0.83 to 1.30), multiple birth rates, Cesarean section rates, gestational weeks, maternal obstetric complication rates, and neonatal outcomes. CONCLUSIONS: Chest CT examination before IVF/ICSI has no impact on pregnancy and neonatal outcomes associated with fresh embryo transfer. TRIAL REGISTRATION: Not applicable.

Retracted: The Nephroprotective Effect of TNF Receptor-Associated Factor 6 (TRAF6) Blockade on LPS-Induced Acute Renal Injury Through the Inhibition if Inflammation and Oxidative Stress.

This publication has been retracted by the Editor due to the identification of non-original figure images and manuscript content that raise concerns regarding the credibility and originality of the study and the manuscript. Reference: Xuemei Chen, Yiqing Zhao, Jiajun Xu, Jiachun Bao, Junyao Zhao, Jingfeng Chen, Guowei Chen, Jibo Han. The Nephroprotective Effect of TNF Receptor-Associated Factor 6 (TRAF6) Blockade on LPS-Induced Acute Renal Injury Through the Inhibition if Inflammation and Oxidative Stress. Med Sci Monit, 2020; 26: e919698. DOI: 10.12659/MSM.919698.

Effect of cumulin and super-GDF9 in standard and biphasic mouse IVM.

PURPOSE: In vitro maturation (IVM) is a technology that generates mature oocytes following culture of immature cumulus-oocyte complexes (COC) in vitro. IVM is characterized by minimal patient stimulation, making it attractive for certain patient groups. Recently, a biphasic IVM system, capacitation (CAPA)-IVM, has shown improved clinical outcomes relative to standard IVM; however, it remains less efficient than IVF. This study assessed whether supplementation of CAPA-IVM culture media with the novel TGFß superfamily proteins cumulin and super-GDF9 improves subsequent mouse embryo development. METHODS: Immature mouse COCs were cultured by standard IVM or biphasic IVM ± cumulin or super-GDF9. RESULTS: Both cumulin and super-GDF9 in standard IVM significantly improved day-6 blastocyst rate (53.9% control, 73.6% cumulin, 70.4% super-GDF9; p = 0.006; n = 382-406 oocytes). Cumulin or super-GDF9 in CAPA-IVM did not alter embryo yield or blastocyst cell allocation in an unstimulated model. Moreover, cumulin did not alter these outcomes in a mild PMSG stimulation model. Cumulin in CAPA-IVM significantly increased cumulus cell expression of cumulus expansion genes (Ptgs2, Ptx3, Adamts1, Gfat2) and decreased Lhr expression relative to control. However, cumulin-induced mRNA expression of cumulus cell (Ptgs2, Ptx3) and oocyte genes (Gdf9, Bmp15, Oct4, Stella) in CAPA-IVM remained significantly lower than that of in vivo matured cells. CONCLUSION: Cumulin did not provide an additional beneficial effect in biphasic IVM in terms of blastocyst yield and cell allocation; however in standard IVM, cumulin and super-GDF9 significantly improve oocyte developmental competence.

Engineering the permeability of Halomonas bluephagenesis enhanced its chassis properties.

Bacterial outer membrane (OM), an asymmetric lipid bilayer functioning as a self-protective barrier with reduced permeability for Gram-negative bacteria, yet wasting nutrients and energy to synthesize, has not been studied for its effect on bioproduction. Here we construct several OM-defected halophile Halomonas bluephagenesis strains to investigate the effects of OM on bioproduction. We achieve enhanced chassis properties of H. bluephagenesis based on positive cellular properties among several OM-defected strains. The OM-defected H. bluephagenesis WZY09 demonstrates better adaptation to lower salinity, increasing 28%, 30% and 12% on dry cell mass (DCM), poly(3-hydroxybutyrate) (PHB) accumulation and glucose to PHB conversion rate, respectively, including enlarged cell sizes and 21-folds reduced endotoxin. Interestingly, a poly(3-hydroxybutyrate-co-21mol%4-hydroxybutyrate) (P(3HB-co-21mol%4HB)) is produced by H. bluephagenesis WZY09 derivate WZY249, increasing 60% and 260% on polyhydroxyalkanoate (PHA) production and 4HB content, respectively. Furthermore, increased electroporation efficiency, more sensitive isopropyl ß-D-1-thio-galactopyranoside (IPTG) induction, better oxygen uptake, enhanced antibiotics sensitivity and ectoine secretion due to better membrane permeability are observed if OM defected, demonstrating significant OM defection impacts for further metabolic engineering, synthetic biology studies and industrial applications.

Orthorexia nervosa is associated with positive body image and life satisfaction in Chinese elderly: Evidence for a positive psychology perspective.

OBJECTIVE: Obsessive attention to healthy eating might paradoxically lead to physical and psychosocial impairments, a potential eating disorder termed orthorexia nervosa (ON). An ongoing debate concerns whether ON should be categorized as an eating disorder, an obsessive-compulsive disorder, or a mental disorder at all. A missing voice in this debate is ON in the elderly, which remains unknown, despite health being a more central issue in everyday life during old age. Similarly missing is ON in East Asia, which remains largely unexplored. METHOD: The current study investigated ON in 313 Chinese elderly (M = 67.90, SD = 7.94) using the Chinese version of the Düsseldorf Orthorexia Scale (C-DOS). Questionnaires were used to measure traditional eating disorder symptomatology, body image (body dissatisfaction, body appreciation, and functionality appreciation), lifestyle behaviors (fruit and vegetable consumption and time spent on physical activity), and indexes of well-being (psychological distress, food-related quality of life, and life satisfaction). RESULTS: ON symptoms were positively related to physical activity, fruit and vegetable (F&V) consumption, body appreciation, functionality appreciation, and life satisfaction, but negatively related to body dissatisfaction. Compared with those without ON, the elderly with ON scored higher on positive psychological/lifestyle measures but lower on negative psychological measures. DISCUSSION: Contrary to the dominant characterization of ON as a variant of disordered eating, in Chinese elderly ON was associated with several positive lifestyle and psychological measures. Thus, ON in the elderly might not be viewed as a form of disordered eating but can be protective and beneficial.

Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation.

BACKGROUND: Stroke is an important clinical outcome in cardiovascular research. However, the ascertainment of incident stroke is typically accomplished via time-consuming manual chart abstraction. Current phenotyping efforts using electronic health records for stroke focus on case ascertainment rather than incident disease, which requires knowledge of the temporal sequence of events. OBJECTIVE: The aim of this study was to develop a machine learning-based phenotyping algorithm for incident stroke ascertainment based on diagnosis codes, procedure codes, and clinical concepts extracted from clinical notes using natural language processing. METHODS: The algorithm was trained and validated using an existing epidemiology cohort consisting of 4914 patients with atrial fibrillation (AF) with manually curated incident stroke events. Various combinations of feature sets and machine learning classifiers were compared. Using a heuristic rule based on the composition of concepts and codes, we further detected the stroke subtype (ischemic stroke/transient ischemic attack or hemorrhagic stroke) of each identified stroke. The algorithm was further validated using a cohort (n=150) stratified sampled from a population in Olmsted County, Minnesota (N=74,314). RESULTS: Among the 4914 patients with AF, 740 had validated incident stroke events. The best-performing stroke phenotyping algorithm used clinical concepts, diagnosis codes, and procedure codes as features in a random forest classifier. Among patients with stroke codes in the general population sample, the best-performing model achieved a positive predictive value of 86% (43/50; 95% CI 0.74-0.93) and a negative predictive value of 96% (96/100). For subtype identification, we achieved an accuracy of 83% in the AF cohort and 80% in the general population sample. CONCLUSIONS: We developed and validated a machine learning-based algorithm that performed well for identifying incident stroke and for determining type of stroke. The algorithm also performed well on a sample from a general population, further demonstrating its generalizability and potential for adoption by other institutions.

Generating real-world evidence from unstructured clinical notes to examine clinical utility of genetic tests: use case in BRCAness.

BACKGROUND: Next-generation sequencing provides comprehensive information about individuals' genetic makeup and is commonplace in oncology clinical practice. However, the utility of genetic information in the clinical decision-making process has not been examined extensively from a real-world, data-driven perspective. Through mining real-world data (RWD) from clinical notes, we could extract patients' genetic information and further associate treatment decisions with genetic information. METHODS: We proposed a real-world evidence (RWE) study framework that incorporates context-based natural language processing (NLP) methods and data quality examination before final association analysis. The framework was demonstrated in a Foundation-tested women cancer cohort (N = 196). Upon retrieval of patients' genetic information using NLP system, we assessed the completeness of genetic data captured in unstructured clinical notes according to a genetic data-model. We examined the distribution of different topics regarding BRCA1/2 throughout patients' treatment process, and then analyzed the association between BRCA1/2 mutation status and the discussion/prescription of targeted therapy. RESULTS: We identified seven topics in the clinical context of genetic mentions including: Information, Evaluation, Insurance, Order, Negative, Positive, and Variants of unknown significance. Our rule-based system achieved a precision of 0.87, recall of 0.93 and F-measure of 0.91. Our machine learning system achieved a precision of 0.901, recall of 0.899 and F-measure of 0.9 for four-topic classification and a precision of 0.833, recall of 0.823 and F-measure of 0.82 for seven-topic classification. We found in result-containing sentences, the capture of BRCA1/2 mutation information was 75%, but detailed variant information (e.g. variant types) is largely missing. Using cleaned RWD, significant associations were found between BRCA1/2 positive mutation and targeted therapies. CONCLUSIONS: In conclusion, we demonstrated a framework to generate RWE using RWD from different clinical sources. Rule-based NLP system achieved the best performance for resolving contextual variability when extracting RWD from unstructured clinical notes. Data quality issues such as incompleteness and discrepancies exist thus manual data cleaning is needed before further analysis can be performed. Finally, we were able to use cleaned RWD to evaluate the real-world utility of genetic information to initiate a prescription of targeted therapy.

Engineering Halomonas bluephagenesis for L-Threonine production.

Halophilic Halomonas bluephagenesis (H. bluephagenesis), a chassis for cost-effective Next Generation Industrial Biotechnology (NGIB), was for the first time engineered to successfully produce L-threonine, one of the aspartic family amino acids (AFAAs). Five exogenous genes including thrA*BC, lysC* and rhtC encoding homoserine dehydrogenase mutant at G433R, homoserine kinase, L-threonine synthase, aspartokinase mutant at T344M, S345L and T352I, and export transporter of threonine, respectively, were grouped into two expression modules for transcriptional tuning on plasmid- and chromosome-based systems in H. bluephagenesis, respectively, after pathway tuning debugging. Combined with deletion of import transporter or/and L-threonine dehydrogenase encoded by sstT or/and thd, respectively, the resulting recombinant H. bluephagenesis TDHR3-42-p226 produced 7.5 g/L and 33 g/L L-threonine when grown under open unsterile conditions in shake flasks and in a 7 L bioreactor, respectively. Engineering H. bluephagenesis demonstrates strong potential for production of diverse metabolic chemicals.

Clinical concept extraction: A methodology review.

BACKGROUND: Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement. OBJECTIVES: In this literature review, we provide a methodology review of clinical concept extraction, aiming to catalog development processes, available methods and tools, and specific considerations when developing clinical concept extraction applications. METHODS: Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a literature search was conducted for retrieving EHR-based information extraction articles written in English and published from January 2009 through June 2019 from Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and the ACM Digital Library. RESULTS: A total of 6,686 publications were retrieved. After title and abstract screening, 228 publications were selected. The methods used for developing clinical concept extraction applications were discussed in this review.

Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records.

Machine learning has become ubiquitous and a key technology on mining electronic health records (EHRs) for facilitating clinical research and practice. Unsupervised machine learning, as opposed to supervised learning, has shown promise in identifying novel patterns and relations from EHRs without using human created labels. In this paper, we investigate the application of unsupervised machine learning models in discovering latent disease clusters and patient subgroups based on EHRs. We utilized Latent Dirichlet Allocation (LDA), a generative probabilistic model, and proposed a novel model named Poisson Dirichlet Model (PDM), which extends the LDA approach using a Poisson distribution to model patients' disease diagnoses and to alleviate age and sex factors by considering both observed and expected observations. In the empirical experiments, we evaluated LDA and PDM on three patient cohorts, namely Osteoporosis, Delirium/Dementia, and Chronic Obstructive Pulmonary Disease (COPD)/Bronchiectasis Cohorts, with their EHR data retrieved from the Rochester Epidemiology Project (REP) medical records linkage system, for the discovery of latent disease clusters and patient subgroups. We compared the effectiveness of LDA and PDM in identifying disease clusters through the visualization of disease representations. We tested the performance of LDA and PDM in differentiating patient subgroups through survival analysis, as well as statistical analysis of demographics and Elixhauser Comorbidity Index (ECI) scores in those subgroups. The experimental results show that the proposed PDM could effectively identify distinguished disease clusters based on the latent patterns hidden in the EHR data by alleviating the impact of age and sex, and that LDA could stratify patients into differentiable subgroups with larger p-values than PDM. However, those subgroups identified by LDA are highly associated with patients' age and sex. The subgroups discovered by PDM might imply the underlying patterns of diseases of greater interest in epidemiology research due to the alleviation of age and sex. Both unsupervised machine learning approaches could be leveraged to discover patient subgroups using EHRs but with different foci.

The Nephroprotective Effect of TNF Receptor-Associated Factor 6 (TRAF6) Blockade on LPS-Induced Acute Renal Injury Through the Inhibition if Inflammation and Oxidative Stress.

BACKGROUND Inflammation and oxidative stress play important roles in the pathogenesis of acute kidney injury (AKI). TRAF6 functions as a signal transducer in the Toll-like receptor 4 signaling pathway. Several reports have previously implicated TRAF6 signaling in kidney pathology. Here, we investigated whether TRAF6 blockade can mitigate inflammatory responses and oxidative stress in AKI. MATERIAL AND METHODS C57BL/6 mice were injected with lipopolysaccharide (LPS, 15 mg/kg) to induce AKI. Double immunofluorescence staining of kidney tissues showed that TRAF6 was localized to renal tubular epithelial cells, and then a tubular epithelial cell line (NRK-52E) was used for in vitro analysis. TRAF6 was blocked in vitro using siRNA and in vivo using AAV2/2 shRNA. RESULTS The knockdown of TRAF6 in mice by AAV2-shTRAF6 significantly reduced renal inflammation, oxidative stress, apoptosis and kidney dysfunction in AKI. In vitro, silencing the expression of TRAF6 attenuated LPS(0.5 µg/mL)-induced inflammatory responses and oxidative stress and upregulated proapoptotic factors. Furthermore, the beneficial actions of TRAF6 blockade were closely associated with its ability to increase IkappaB-alpha and Nrf2. CONCLUSIONS Our findings provide direct evidence that TRAF6 mediates LPS-induced inflammation and oxidative stress, leading to renal dysfunction. We also show that TRAF6 inhibition is a potential therapeutic option to prevent AKI.

Rare disease knowledge enrichment through a data-driven approach.

BACKGROUND: Existing resources to assist the diagnosis of rare diseases are usually curated from the literature that can be limited for clinical use. It often takes substantial effort before the suspicion of a rare disease is even raised to utilize those resources. The primary goal of this study was to apply a data-driven approach to enrich existing rare disease resources by mining phenotype-disease associations from electronic medical record (EMR). METHODS: We first applied association rule mining algorithms on EMR to extract significant phenotype-disease associations and enriched existing rare disease resources (Human Phenotype Ontology and Orphanet (HPO-Orphanet)). We generated phenotype-disease bipartite graphs for HPO-Orphanet, EMR, and enriched knowledge base HPO-Orphanet + and conducted a case study on Hodgkin lymphoma to compare performance on differential diagnosis among these three graphs. RESULTS: We used disease-disease similarity generated by the eRAM, an existing rare disease encyclopedia, as a gold standard to compare the three graphs with sensitivity and specificity as (0.17, 0.36, 0.46) and (0.52, 0.47, 0.51) for three graphs respectively. We also compared the top 15 diseases generated by the HPO-Orphanet + graph with eRAM and another clinical diagnostic tool, the Phenomizer. CONCLUSIONS: Per our evaluation results, our approach was able to enrich existing rare disease knowledge resources with phenotype-disease associations from EMR and thus support rare disease differential diagnosis.

CRISPR/Cas9 editing genome of extremophile Halomonas spp.

Extremophiles are suitable chassis for developing the next generation industrial biotechnology (NGIB) due to their resistance to microbial contamination. However, engineering extremophiles are not an easy task. Halomonas, an industrially interesting halophile able to grow under unsterile and continuous conditions in large-scale processes, can only be engineered using suicide plasmid-mediated two-step homologous recombination which is very laborious and time-consuming (up to half a year). A convenient approach for the engineering of halophiles that can possibly be extended to other extremophiles is therefore urgently required. To meet this requirement, a rapid, efficient and scarless method via CRISPR/Cas9 system was developed in this study for genome editing in Halomonas. The method achieved the highest efficiency of 100%. When eight different mutants were constructed via this special CRISPR/Cas9 method to study the combinatorial influences of four different genes on the glucose catabolism in H. bluephagenesis TD01, it took only three weeks to complete the deletion and insertion of up to 4.5 kb DNA. H. bluephagenesis was designed to produce a microbial copolymer P(3HB-co-3HV) consisting of 3-hydroxybutyrate (3HB) and 3-hydroxyvalerate (3HV). The CRISPR/Cas9 was employed to delete the prpC gene in H. bluephagenesis TD01. Shake flask studies showed that the 3HV fraction in the copolymers increased approximately 16-folds, demonstrating enhanced effectiveness of the ΔprpC mutant to synthesize PHBV. This genome engineering strategy significantly speeds up the studies on Halomonas engineering, opening up a wide area for developing NGIB.

Using data-driven sublanguage pattern mining to induce knowledge models: application in medical image reports knowledge representation.

BACKGROUND: The use of knowledge models facilitates information retrieval, knowledge base development, and therefore supports new knowledge discovery that ultimately enables decision support applications. Most existing works have employed machine learning techniques to construct a knowledge base. However, they often suffer from low precision in extracting entity and relationships. In this paper, we described a data-driven sublanguage pattern mining method that can be used to create a knowledge model. We combined natural language processing (NLP) and semantic network analysis in our model generation pipeline. METHODS: As a use case of our pipeline, we utilized data from an open source imaging case repository, Radiopaedia.org , to generate a knowledge model that represents the contents of medical imaging reports. We extracted entities and relationships using the Stanford part-of-speech parser and the "Subject:Relationship:Object" syntactic data schema. The identified noun phrases were tagged with the Unified Medical Language System (UMLS) semantic types. An evaluation was done on a dataset comprised of 83 image notes from four data sources. RESULTS: A semantic type network was built based on the co-occurrence of 135 UMLS semantic types in 23,410 medical image reports. By regrouping the semantic types and generalizing the semantic network, we created a knowledge model that contains 14 semantic categories. Our knowledge model was able to cover 98% of the content in the evaluation corpus and revealed 97% of the relationships. Machine annotation achieved a precision of 87%, recall of 79%, and F-score of 82%. CONCLUSION: The results indicated that our pipeline was able to produce a comprehensive content-based knowledge model that could represent context from various sources in the same domain.

Semantic-Enhanced Query Expansion System for Retrieving Medical Image Notes.

Most current image retrieval methods require constructing semantic metadata for representing image content. To manually create semantic metadata for medical images is time-consuming, yet it is a crucial component for query expansion. We proposed a new method for searching medical image notes that uses semantic metadata to improve query expansion and leverages a knowledge model developed specifically for the medical image domain to create relevant metadata. We used a syntactic parser and the Unified Medical Language System to analyze the corpus and store text information as semantic metadata in a knowledge model. Our new method has an interactive interface that allows users to provide relevance feedback and construct new queries more efficiently. Sixteen medical professionals evaluated the query expansion module, and each evaluator had prior experience searching for medical images. When using the initial query as the baseline standard, expanded queries achieved a performance boost of 22.6% in terms of the relevance score on first ten results (P-value<0.05). When using Google as another baseline, our system performed 24.6% better in terms of relevance score on the first ten results (P-value<0.05). Overall, 75% of the evaluators said the semantic-enhanced query expansion workflow is logical, easy to follow, and comfortable to use. In addition, 62% of the evaluators preferred using our system instead of Google. Evaluators who were positive about our system found the knowledge map-based visualization of candidate medical search terms helpful in refining cases from the initial search results.