• Clinics in Shoulder and Elbow
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• v.26 no.2
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• pp.182-190
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• 2023

Elbow traumas represent a relatively common condition in clinical practice. However, there is a lack of evidence regarding the most accurate tests for screening these potentially serious conditions and excluding elbow fractures. The purpose of this investigation was to analyze the literature concerning the diagnostic accuracy of clinical tests for the detection or exclusion of suspected elbow fractures. A systematic review was performed using the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines. Literature databases including PubMed, Cumulative Index to Nursing and Allied Health Literature, Diagnostic Test Accuracy, Cochrane Library, the Web of Science, and ScienceDirect were searched for diagnostic accuracy studies of subjects with suspected traumatic elbow fracture investigating clinical tests compared to imaging reference tests. The risk of bias in each study was assessed independently by two reviewers using the Quality Assessment of Diagnostic Accuracy Studies 2 checklist. Twelve studies (4,485 patients) were included. Three different types of index tests were extracted. In adults, these tests were very sensitive, with values up to 98.6% (95% confidence interval [CI], 95.0%-99.8%). The specificity was very variable, ranging from 24.0% (95% CI, 19.0%-30.0%) to 69.4% (95% CI, 57.3%-79.5%). The applicability of these tests was very high, while overall studies showed a medium risk of bias. Elbow full range of motion test, elbow extension test, and elbow extension and point tenderness test appear to be useful in the presence of a negative test to exclude fracture in a majority of cases. The specificity of all tests, however, does not allow us to draw useful conclusions because there was a great variability of results obtained.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.4
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• pp.77-97
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• 2017

A recent sharp increase of the biomedical literature causes researchers to struggle to grasp the current research trends and conduct creative studies based on the previous results. In order to alleviate their difficulties in keeping up with the latest scholarly trends, numerous attempts have been made to develop specialized analytic services that can provide direct, intuitive and formalized scholarly information by using various text mining technologies such as information extraction and event detection. This paper introduces and evaluates total 8 Convolutional Neural Network (CNN) models for extracting biomedical events from academic abstracts by applying various feature utilization approaches. Also, this paper conducts performance comparison evaluation for the proposed models. As a result of the comparison, we confirmed that the Entity-Type-Fully-Connected model, one of the introduced models in the paper, showed the most promising performance (72.09% in F-score) in the event classification task while it achieved a relatively low but comparable result (21.81%) in the entire event extraction process due to the imbalance problem of the training collections and event identify model's low performance.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.77-84
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• 2023

Given the growing volume of biomedical papers, the ability to efficiently extract keywords has become crucial for accessing and responding to important information in the literature. In this study, we conduct a comprehensive evaluation of different unsupervised learning-based models and BERT-based models for keyword extraction in the biomedical field. Our experimental findings reveal that the BioBERT model, trained on biomedical-specific data, achieves the highest performance. This study offers precise and dependable insights to guide forthcoming research in biomedical keyword extraction. By establishing a well-suited experimental framework and conducting thorough comparisons and analyses of diverse models, we have furnished essential information. Furthermore, we anticipate extending our contributions to other domains by providing comparative experiments and practical guidelines for effective keyword extraction.

• Journal of Computing Science and Engineering
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• v.6 no.2
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• pp.168-177
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• 2012

The search for relevant and actionable information is a key to achieving clinical and research goals in biomedicine. Biomedical information exists in different forms: as text and illustrations in journal articles and other documents, in images stored in databases, and as patients' cases in electronic health records. This paper presents ways to move beyond conventional text-based searching of these resources, by combining text and visual features in search queries and document representation. A combination of techniques and tools from the fields of natural language processing, information retrieval, and content-based image retrieval allows the development of building blocks for advanced information services. Such services enable searching by textual as well as visual queries, and retrieving documents enriched by relevant images, charts, and other illustrations from the journal literature, patient records and image databases.

• CELLMED
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• v.5 no.3
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• pp.16.1-16.3
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• 2015

Evidence based medicine involves using both the individual clinician's expertise and the current best available external clinical evidence from systematic research in deciding on the appropriate care for individual patients. The current approach to evidence based practice in healthcare adds a third component which is patient values. Evidence based practice is thus a triad, in which the practitioner's expertise, research evidence and the patient's values are all given consideration. The balance to be struck between them depends on the individual case. The literature indicates that complementary medicine practitioners are moving away from traditional knowledge and towards the use of evidence based practice in their clinical discussions. In the context of the daily practice of complementary medicine practitioners and their continuing development of their knowledge base of evidence based practice, this short review discusses the good and bad of a review journal article.

• The Korean Journal of Malacology
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• v.32 no.4
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• pp.329-333
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• 2016

The bivalve Malleus regula ($Forssk{\aa}l$ in Niebuhr, 1775) is reported for the first time from Korea. This is the second species of Malleidae reported from this country. Since the species is quite variable, comparisons were made with the original description and descriptions in the literature; some taxonomic comments were also made. Global warming and possible changes in the northward-flowing Tsushima Current may account for the addition of new mollusk species to the island's fauna.

• Journal of Microbiology and Biotechnology
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• v.23 no.4
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• pp.518-526
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• 2013

Gut-derived lipopolysaccharides (LPS) are critical to the development and progression of chronic low-grade inflammation and metabolic diseases. In this study, the effects of probiotics Lactobacillus and Bifidobacterium on gut-derived lipopolysaccharide and inflammatory cytokine concentrations were evaluated using a human colonic microbiota model. Lactobacillus reuteri, L. rhamnosus, L. plantarum, Bifidobacterium animalis, B. bifidum, B. longum, and B. longum subsp. infantis were identified from the literature for their anti-inflammatory potential. Each bacterial culture was administered daily to a human colonic microbiota model during 14 days. Colonic lipopolysaccharides, and Gram-positive and negative bacteria were quantified. RAW 264.7 macrophage cells were stimulated with supernatant from the human colonic microbiota model. Concentrations of TNF-${\alpha}$, IL-$1{\beta}$, and IL-4 cytokines were measured. Lipopolysaccharide concentrations were significantly reduced with the administration of B. bifidum ($-46.45{\pm}5.65%$), L. rhamnosus ($-30.40{\pm}5.08%$), B. longum ($-42.50{\pm}1.28%$), and B. longum subsp. infantis ($-68.85{\pm}5.32%$) (p < 0.05). Cell counts of Gram-negative and positive bacteria were distinctly affected by the probiotic administered. There was a probiotic strain-specific effect on immunomodulatory responses of RAW 264.7 macrophage cells. B. longum subsp. infantis demonstrated higher capacities to reduce TNF-${\alpha}$ concentrations ($-69.41{\pm}2.78%$; p < 0.05) and to increase IL-4 concentrations ($+16.50{\pm}0.59%$; p < 0.05). Colonic lipopolysaccharides were significantly correlated with TNF-${\alpha}$ and IL-$1{\beta}$ concentrations (p < 0.05). These findings suggest that specific probiotic bacteria, such as B. longum subsp. infantis, might decrease colonic lipopolysaccharide concentrations, which might reduce the proinflammatory tone. This study has noteworthy applications in the field of biotherapeutics for the prevention and/or treatment of inflammatory and metabolic diseases.

• Journal of Information Processing Systems
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• v.7 no.3
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• pp.459-472
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• 2011

The vast number of biomedical literature is an important source of biomedical interaction information discovery. However, it is complicated to obtain interaction information from them because most of them are not easily readable by machine. In this paper, we present a method for extracting biomedical interaction information assuming that the biomedical Named Entities (NEs) are already identified. The proposed method labels all possible pairs of given biomedical NEs as INTERACTION or NO-INTERACTION by using a Maximum Entropy (ME) classifier. The features used for the classifier are obtained by applying various NLP techniques such as POS tagging, base phrase recognition, parsing and predicate-argument recognition. Especially, specific verb predicates (activate, inhibit, diminish and etc.) and their biomedical NE arguments are very useful features for identifying interactive NE pairs. Based on this, we devised a twostep method: 1) an interaction verb extraction step to find biomedically salient verbs, and 2) an argument relation identification step to generate partial predicate-argument structures between extracted interaction verbs and their NE arguments. In the experiments, we analyzed how much each applied NLP technique improves the performance. The proposed method can be completely improved by more than 2% compared to the baseline method. The use of external contextual features, which are obtained from outside of NEs, is crucial for the performance improvement. We also compare the performance of the proposed method against the co-occurrence-based and the rule-based methods. The result demonstrates that the proposed method considerably improves the performance.

• Biomedical Science Letters
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• v.8 no.4
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• pp.229-234
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• 2002

Chlorella is rich in chlorella growth factor (CGF). A review of the literature has described that CGF improves the capability of a Th1-based immunity, anticancer, antioxidant antibacterial activity, growth promotion, wound healing and so on, but has not studied the effect for the metabolism and the proliferation of human skin keratinocyte. The aim of this study was to examine the effect of metabolism and the proliferation of human skin keratinocyte in vitro. CGF was extracted with an autoclaving method which is a modified hot-water extraction method from dried chlorella and conformed by means of absorbance 0.22 at 260 nm. We have measured the extracellular acidification rate (ECAR) of the CGF by Cytosensor$^{\circledR}$ Microphysiometer and evaluated responsiveness depending upon the dosage on the HaCaT cell. The ECAR for the concentrations of 0.15, 1.5, 15, 150 $\mu\textrm{g}$/ml of CGF increased as a 103.6, 128.2, 149.0 and 423.9%, respectively compared to control (0.0 $\mu\textrm{g}$/ml, 100% ECAR). The ECAR for ErbBl tyrosine kinase inhibited by 4-anilinoquinazolines, $C_{16}$H$_{14}$BrN$_3$O$_2$.HCl on tile HaCaT cells with the amounts of 10 $\mu\textrm{g}$/ml of the CCF compared with 100 $\mu\textrm{g}$/ml of rhEGF. The conclusion of the study is that CGF might increase human epidermal keratinocyte proliferation through the interaction between the epidermal growth factor receptor and itself.

• Journal of the Korean Society of Radiology
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• v.81 no.1
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• pp.219-224
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• 2020

Inflammatory myofibroblastic tumor is a rare benign lesion that accounts for 0.04-1% of all lung tumors and usually appears as a solitary pulmonary nodule or mass. Here, we report the case of an endobronchial inflammatory myofibroblastic tumor in a 21-year-old man with a focus on the imaging findings and a review of previous literature.