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Background and Objectives: Chronic lower back pain (LBP) is a common clinical disorder. The early identification of patients who will develop chronic LBP would help develop preventive measures and treatment. We aimed to develop machine learning models that can accurately predict the risk of chronic LBP. Materials and Methods: Data from the Sixth Korea National Health and Nutrition Examination Survey conducted in 2014 and 2015 (KNHANES VI-2, 3) were screened for selecting patients with chronic LBP. LBP lasting >30 days in the past 3 months was defined as chronic LBP in the survey. The following classification models with machine learning algorithms were developed and validated to predict chronic LBP: logistic regression (LR), k-nearest neighbours (KNN), naïve Bayes (NB), decision tree (DT), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), and artificial neural network (ANN). The performance of these models was compared with respect to the area under the receiver operating characteristic curve (AUROC). Results: A total of 6119 patients were analyzed in this study, of which 1394 had LBP. The feature selected data consisted of 13 variables. The LR, KNN, NB, DT, RF, GBM, SVM, and ANN models showed performances (in terms of AUROCs) of 0.656, 0.656, 0.712, 0.671, 0.699, 0.660, 0.707, and 0.716, respectively, with ten-fold cross-validation. Conclusions: In this study, the ANN model was identified as the best machine learning classification model for predicting the occurrence of chronic LBP. Therefore, machine learning could be effectively applied in the identification of populations at high risk of chronic LBP. [ABSTRACT FROM AUTHOR]

In our ongoing search for new secondary metabolites from fungi, a basidiomycete fungus Irpex consors was selected for mycochemical investigation, and three new zwitterionic alkaloids (1-3) and five known compounds (4-8) were isolated from the culture broth (16 l) of I. consors. The culture filtrate was fractionated by a series of column chromatography including Diaion HP-20, silica gel, and Sephadex LH-20, Sep-Pak C18 cartridge, medium pressure liquid chromatography (MPLC), and high pressure liquid chromatography (HPLC) to yield eight compounds (1-8). The structures of the isolated compounds were elucidated by the interpretation of nuclear magnetic resonance (NMR) spectra and high-resolution mass spectrometry (HR-MS). Their antioxidant and antibacterial activities were examined. The zwitterionic structures of three new sesquiterpene alkaloids (1-3) were determined together with five known compounds identified as stereumamide E (4), stereumamide G (5), stereumamide H (6), stereumamide D (7), and sterostrein H (8). This is the first report of the zwitterionic alkaloids in the culture broth of I. consors. Three new zwitterionic alkaloids were named as consoramides A-C (1-3). [ABSTRACT FROM AUTHOR]

During our ongoing investigation of neuraminidase inhibitors from medicinal fungi, we found that the fruiting bodies of Phellinus igniarius exhibited significant inhibitory activity against neuraminidase from recombinant H3N2 influenza viruses. Two active compounds were isolated from the methanolic extract of P. igniarius through solvent partitioning and Sephadex LH-20 column chromatography. The active compounds were identified as phelligridins E and G on proton nuclear magnetic resonance (1H NMR) and electrospray ionization mass measurements. These compounds inhibited neuraminidases from recombinant rvH1N1, H3N2, and H5N1 influenza viruses, with IC50 values in the range of 0.7~8.1 μM. [ABSTRACT FROM AUTHOR]

In a previous study, we identified a Streptomyces sp., A3265, as exhibiting potent antifungal activity against various plant pathogenic fungi, including Botrytis cinerea, Colletotrichum gloeosporioides, and Rhizoctonia solani. This strain also exhibited a biocontrolling effect against ginseng root rot and damping-off disease, common diseases of ginseng and other crops. In this study, we isolated two antifungal substances responsible for this biocontrolling effect via Diaion HP-20 and Sephadex LH-20 column chromatography, medium pressure liquid chromatography, and high-performance liquid chromatography. These compounds were identified as guanidylfungin A and methyl guanidylfungin A by spectroscopic methods. These compounds exhibited potent antimicrobial activity against various plant pathogenic fungi as well as against bacteria. [ABSTRACT FROM AUTHOR]

We investigated a total of 335 samples of Korean native mushroom extracts as part of our lipoxygenase (LOX) inhibitor screening program. Among the mushroom-methanolic extracts we investigated, 35 exhibited an inhibitory activity greater than 30% against LOX at a concentration of 100μg/mL. Especially, Collybia maculata, Tylopilus neofelleus, Strobilomyces confusus, Phellinus gilvus, P. linteus, P. baumii, and Inonotus mikadoi exhibited relatively potent LOX inhibitory activities of 73.3%, 51.6%, 52.4%, 66.7%, 59.5%, 100.0%, and 85.2%, respectively. Bioassay-guided fractionation led to the isolation of inoscavin A from the methanolic extract of P baumii, which showed the most potent activity and was identified by spectroscopic methods. Specifically, inoscavin A exhibited potent LOX inhibitory activity with an IC50 value of 6.8 μM. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to explore socio-demographic, clinical and psychological factors associated with medication adherence among patients with chronic heart failure (CHF). Methods: In a cross-sectional survey, 133 patients with CHF who had visited a clinic located in Seoul participated. Medication adherence (MA) was measured using a single item visual analogue rating scale (VAS). Barriers and attitudes toward adherence, and depression were measured using a structured questionnaire. Medical records were reviewed to identify their clinical characteristics. Multivariate logistic regression was performed identify factors affecting medication adherence. Results: About one third of participants (31.1%) reported their MA as 100%. Overall attitudes on MA were highly positive and 49 (36.8%) of participants were depressive. MA was associated with significantly barriers (p<.001), attitudes (p<.001), age (p<.001), and duration of disease (p<.001). The high adherent group had significantly less barriers than other groups (OR=.389, p=0.02). Conclusion: This study indicates that barriers and attitudes toward MA were the most important factors affecting their adherence to HF medication. To improve MA for patient with CHF, the nursing strategies which can lead poor adherent patients to their optimal level should be developed. [ABSTRACT FROM AUTHOR]

This paper describes initial experiences with semi-automated performance tuning of a sparse linear solver in LS-DYNA, a large, widely used engineering application. Through a collection of tools supporting empirical optimization, we alleviate the burden of performance tuning for mapping today’s sophisticated engineering software to increasingly complex hardware platforms. We describe a tool that automatically isolates code segments to create benchmark subsets for the purposes of performance tuning. We present a collection of automatically generated empirical results that demonstrate the sensitivity of the application’s performance to optimization parameters. Through this case study, we demonstrate the importance of developing automatic performance tuning support for performance-sensitive applications. [ABSTRACT FROM AUTHOR]