Network analysis for estimating standardization trends in genomics using MEDLINE.

BACKGROUND: Biotechnology in genomics, such as sequencing devices and gene quantification software, has proliferated and been applied to clinical settings. However, the lack of standards applicable to it poses practical problems in interoperability and reusability of the technology across various application domains. This study aims to visualize and identify the standard trends in clinical genomics and to suggest areas on which standardization efforts must focus. METHODS: Of 16,538 articles retrieved from PubMed, published from 1975 to 2020, using search keywords "genomics and standard" and "clinical genomic sequence and standard", terms were extracted from the abstracts and titles of 15,855 articles. Our analysis includes (1) network analysis of full phases (2) period analysis with five phases; (3) statistical analysis; (4) content analysis. RESULTS: Our research trend showed an increasing trend from 2003, years marked by the completion of the human genome project (2003). The content analysis showed that keywords related to such concepts as gene types for analysis, and analysis techniques were increased in phase 3 when US-FDA first approved the next-generation sequencer. During 2017-2019, oncology-relevant terms were clustered and contributed to the increasing trend in phase 4 of the content analysis. In the statistical analysis, all the categories showed high regression values (R2 > 0.586) throughout the whole analysis period and phase-based statistical analysis showed significance only in the Genetics terminology category (P = .039*) at phase 4. CONCLUSIONS: Through comprehensive trend analysis from our study, we provided the trend shifts and high-demand items in standardization for clinical genetics.

Beyond the tubule: pathological variants of LRP2, encoding the megalin receptor, result in glomerular loss and early progressive chronic kidney disease.

Pathogenic variants in the LRP2 gene, encoding the multiligand receptor megalin, cause a rare autosomal recessive syndrome: Donnai-Barrow/Facio-Oculo-Acoustico-Renal (DB/FOAR) syndrome. Because of the rarity of the syndrome, the long-term consequences of the tubulopathy on human renal health have been difficult to ascertain, and the human clinical condition has hitherto been characterized as a benign tubular condition with asymptomatic low-molecular-weight proteinuria. We investigated renal function and morphology in a murine model of DB/FOAR syndrome and in patients with DB/FOAR. We analyzed glomerular filtration rate in mice by FITC-inulin clearance and clinically characterized six families, including nine patients with DB/FOAR and nine family members. Urine samples from patients were analyzed by Western blot analysis and biopsy materials were analyzed by histology. In the mouse model, we used histological methods to assess nephrogenesis and postnatal renal structure and contrast-enhanced magnetic resonance imaging to assess glomerular number. In megalin-deficient mice, we found a lower glomerular filtration rate and an increase in the abundance of injury markers, such as kidney injury molecule-1 and N-acetyl-ß-d-glucosaminidase. Renal injury was validated in patients, who presented with increased urinary kidney injury molecule-1, classical markers of chronic kidney disease, and glomerular proteinuria early in life. Megalin-deficient mice had normal nephrogenesis, but they had 19% fewer nephrons in early adulthood and an increased fraction of nephrons with disconnected glomerulotubular junction. In conclusion, megalin dysfunction, as present in DB/FOAR syndrome, confers an increased risk of progression into chronic kidney disease.

Dr. Answer AI for prostate cancer: Clinical outcome prediction model and service.

OBJECTIVES: The importance of clinical outcome prediction models using artificial intelligence (AI) is being emphasized owing to the increasing necessity of developing a clinical decision support system (CDSS) employing AI. Therefore, in this study, we proposed a "Dr. Answer" AI software based on the clinical outcome prediction model for prostate cancer treated with radical prostatectomy. METHODS: The Dr. Answer AI was developed based on a clinical outcome prediction model, with a user-friendly interface. We used 7,128 clinical data of prostate cancer treated with radical prostatectomy from three hospitals. An outcome prediction model was developed to calculate the probability of occurrence of 1) tumor, node, and metastasis (TNM) staging, 2) extracapsular extension, 3) seminal vesicle invasion, and 4) lymph node metastasis. Random forest and k-nearest neighbors algorithms were used, and the proposed system was compared with previous algorithms. RESULTS: Random forest exhibited good performance for TNM staging (recall value: 76.98%), while k-nearest neighbors exhibited good performance for extracapsular extension, seminal vesicle invasion, and lymph node metastasis (80.24%, 98.67%, and 95.45%, respectively). The Dr. Answer AI software consisted of three primary service structures: 1) patient information, 2) clinical outcome prediction, and outcomes according to the National Comprehensive Cancer Network guideline. CONCLUSION: The proposed clinical outcome prediction model could function as an effective CDSS, supporting the decisions of the physicians, while enabling the patients to understand their treatment outcomes. The Dr. Answer AI software for prostate cancer helps the doctors to explain the treatment outcomes to the patients, allowing the patients to be more confident about their treatment plans.

Nephron loss detected by MRI following neonatal acute kidney injury in rabbits.

BACKGROUND: Acute kidney injury affects nearly 30% of preterm neonates in the intensive care unit. We aimed to determine whether nephrotoxin-induced AKI disrupted renal development assessed by imaging (CFE-MRI). METHODS: Neonatal New Zealand rabbits received indomethacin and gentamicin (AKI) or saline (control) for four days followed by cationic ferritin (CF) at six weeks. Ex vivo images were acquired using a gradient echo pulse sequence on 7 T MRI. Glomerular number (Nglom) and apparent glomerular volume (aVglom) were determined. CF toxicity was assessed at two and 28 days in healthy rabbits. RESULTS: Nglom was lower in the AKI group as compared to controls (74,034 vs 198,722, p < 0.01). aVglom was not different (AKI: 7.3 × 10-4 vs control: 6.2 × 10-4 mm3, p = 0.69). AKI kidneys had a band of glomeruli distributed radially in the cortex that were undetectable by MRI. Following CF injection, there was no difference in body or organ weights except for the liver, and transient changes in serum iron, platelets and white blood cell count. CONCLUSIONS: Brief nephrotoxin exposure during nephrogenesis results in fewer glomeruli and glomerular maldevelopment in a unique pattern detectable by MRI. Whole kidney evaluation by CFE-MRI may provide an important tool to understand the development of CKD following AKI.

Research trends in Korean medicine based on temporal and network analysis.

BACKGROUND: Much research on Korean medicine has been recently published in Korea. The aim of this study was to determine the research trends in Korean medicine by performing a comprehensive analysis of articles that have been published in Korea using temporal and network analysis methods. METHODS: A total of 29,876 articles from 1963 to 2018 were prepared from OASIS (Oriental Medicine Advanced Searching Integrated System), the largest portal for Korean medicine. After the keywords and years were extracted from the metadata of the articles, an annual frequency matrix was obtained for the keywords. By using the matrix, the temporal trends of the keywords were analyzed by comparing the changes in similarity between the lists of keywords by year. Moreover, to analyze the relationship among research topics, a clustered network was constructed in which a node was a keyword and an edge was a similarity between two keywords. RESULTS: The temporal trend of the keywords was classified into six chronological phases. The appearance frequency of most keywords tended to increase gradually, but only the keywords "mibyeong," "systems biology" and "korean medicine hospital" appeared in the most recent phase. The network of keywords was clustered and visualized into thirteen groups with the Gephi software. The main keywords in each group were related to effects such as "anti-inflammation" and "antioxidant," to diseases such as "allergic rhinitis" and "diabetes" and to therapies such as "herbal acupuncture" and "herbal formula." CONCLUSIONS: The analysis of the trends determined in this study provides a systematic understanding as well as future research directions in Korean medicine to researchers. In the future, an overall analysis of the research trends in Korean medicine will be done by analyzing articles published in Korea and other countries.

Exploring Research Topics and Trends in Nursing-related Communication in Intensive Care Units Using Social Network Analysis.

This study used social network analysis to identify the main research topics and trends in nursing-related communication in intensive care units. Keywords from January 1967 to June 2016 were extracted from PubMed using Medical Subject Headings terms. Social network analysis was performed using Gephi software. Research publications and newly emerging topics in nursing-related communication in intensive care units were classified into five chronological phases. After the weighting was adjusted, the top five keyword searches were "conflict," "length of stay," "nursing continuing education," "family," and "nurses." During the most recent phase, research topics included "critical care nursing," "patient handoff," and "quality improvement." The keywords of the top three groups among the 10 groups identified were related to "neonatal nursing and practice guideline," "infant or pediatric and terminal care," and "family, aged, and nurse-patient relations," respectively. This study can promote a systematic understanding of communication in intensive care units by identifying topic networks. Future studies are needed to conduct large prospective cohort studies and randomized controlled trials to verify the effects of patient-centered communication in intensive care units on patient outcomes, such as length of hospital stay and mortality.

DisArticle: a web server for SVM-based discrimination of articles on traditional medicine.

BACKGROUND: Much research has been done in Northeast Asia to show the efficacy of traditional medicine. While MEDLINE contains many biomedical articles including those on traditional medicine, it does not categorize those articles by specific research area. The aim of this study was to provide a method that searches for articles only on traditional medicine in Northeast Asia, including traditional Chinese medicine, from among the articles in MEDLINE. RESULTS: This research established an SVM-based classifier model to identify articles on traditional medicine. The TAK + HM classifier, trained with the features of title, abstract, keywords, herbal data, and MeSH, has a precision of 0.954 and a recall of 0.902. In particular, the feature of herbal data significantly increased the performance of the classifier. By using the TAK + HM classifier, a total of about 108,000 articles were discriminated as articles on traditional medicine from among all articles in MEDLINE. We also built a web server called DisArticle ( http://informatics.kiom.re.kr/disarticle ), in which users can search for the articles and obtain statistical data. CONCLUSIONS: Because much evidence-based research on traditional medicine has been published in recent years, it has become necessary to search for articles on traditional medicine exclusively in literature databases. DisArticle can help users to search for and analyze the research trends in traditional medicine.

Structuralizing biomedical abstracts with discriminative linguistic features.

OBJECTIVE: Nearly 75% of the abstracts in MEDLINE papers present in an unstructured format. This study aims to automate the reformatting of unstructured abstracts into the Introduction, Methods, Results, and Discussion (IMRAD) format. The quality of this reformatting relies on the features used in sentence classification. Therefore, we explored the most effective linguistic features in MEDLINE papers. METHODS: We constructed a feature set consisting of bag of words, linguistic features, grammatical features, and structural features. In order to evaluate the effectiveness, which is the capability of the sentence classification with the features, three datasets from PubMed Central Open Access Subset were selected and constructed: (1) structured abstract (SA) for training, (2) unstructured RCT abstract (UA-1) and (3) unstructured general abstract (UA-2). F-score and accuracy were used to measure the effectiveness on IMRAD section level and the overall classification. RESULTS: Adding linguistic features improves the classification of the abstract sentence from 1.2% to 35.8% in terms of accuracy in three abstract datasets. The highest accuracies achieved were 91.7% in SA, 86.3% in UA-1, and 77.9% in UA-2. Linguistic features (dimensions=15) had fewer dimensions than bag-of-words (dimensions= 1541). All representative linguistic features (n-gram and verb phrase, and noun phrase) for each section are identified in our system (available at http://abstract.bike.re.kr). CONCLUSION: Linguistic features can be used to effectively classify sentence with low computation burden in MEDLINE abstract.

Marginal and Internal Fit of Conventional Metal-Ceramic and Lithium Disilicate CAD/CAM Crowns.

This study aimed to evaluate and compare the marginal and internal gap widths of lithium disilicate computer-aided design / computer-assisted manufacture (LDC) crowns and conventionally produced porcelain-fused-to-metal (PFM) crowns. A convenience sample of 21 patients treated with a single restoration was selected. PFM and LDC crowns were fabricated for each selected abutment tooth, following traditional crown preparation. Silicone replicas were produced, and internal gaps and marginal gaps were measured. Internal gaps were significantly larger for the axial and occlusal surfaces of LDC crowns than for those of PFM crowns (P < .001). Marginal gaps were not significantly different (P > .05). Both LDC crowns and PFM crowns showed clinically acceptable marginal fit.

TM-MC: a database of medicinal materials and chemical compounds in Northeast Asian traditional medicine.

BACKGROUND: In traditional medicine, there has been a great deal of research on the effects exhibited by medicinal materials. To study the effects, resources that can systematically describe the chemical compounds in medicinal materials are necessary. In recent years, numerous databases on medicinal materials and constituent compounds have been constructed. However, because these databases provide differing information and the sources of such information are unclear or difficult to verify, it is difficult to decide which database to use. Moreover, there is much overlapping information. The aim of this study was to construct a database of medicinal materials and chemical compounds in Northeast Asian traditional medicine (TM-MC), for which medicinal materials are listed in the Korean, Chinese, and Japanese pharmacopoeias and information on the compound names of medicinal materials can easily be confirmed online. DESCRIPTION: To provide information on the chemical compounds of medicinal materials, chromatography articles from MEDLINE and PubMed Central were searched. After chemical compounds of medicinal materials were extracted by manually investigating the full-text of articles, a database of information on about 14,000 compounds from 536 medicinal materials was built. The database also provides links to the articles from which each medicinal material and chemical compound were extracted. CONCLUSION: TM-MC database provides information on medicinal materials and their chemical compounds from chromatography articles in MEDLINE and PubMed Central. Researchers can easily check relevant information through the links to articles.

Ontology-based reusable clinical document template production system.

Clinical documents embody professional clinical knowledge. This paper shows an effective clinical document template (CDT) production system that uses a clinical description entity (CDE) model, a CDE ontology, and a knowledge management system called STEP that manages ontology-based clinical description entities. The ontology represents CDEs and their inter-relations, and the STEP system stores and manages CDE ontology-based information regarding CDTs. The system also provides Web Services interfaces for search and reasoning over clinical entities. The system was populated with entities and relations extracted from 35 CDTs that were used in admission, discharge, and progress reports, as well as those used in nursing and operation functions. A clinical document template editor is shown that uses STEP.