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PURPOSE This study evaluates the suitability of the Exerheart exergame as an alternative tool to moderate-intensity aerobic exercise for healthy adults. METHODS A total of 49 healthy adults were enrolled in the study. For the first visit (visit 1), all participants underwent a cardiopulmonary exercise test (CPX) on a treadmill to assess maximum oxygen consumption (VO2 max). For the second visit (visit 2), the oxygen consumption (VO2 peak and VO2 mean) was measured during the performance of Exerheart, three times for 10 minutes each to determine its exercise intensity. RESULTS VO2 max during the CPX was 30±8.1 mL/kg/min, while VO2 peak during Exerheart was 17.4±3.4 mL/kg/min and VO2 mean was 16.3±3.3. The Exerheart intensity generated a VO2 peak of 60.4±16.3% and VO2 mean of 56.0±15.7 of the VO2 max as achieved during the CPX. No significant differences were observed by age group for VO2 peak/VO2 max or VO2 mean/VO2 max. CONCLUSIONS Exerheart exergame corresponds to moderate-intensity aerobic exercise range according to ACSM guidelines. Therefore, Exerheart exergame may be used as an effective alternative to aerobic exercise intervention for healthy adults.

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).

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 neighbors (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.