Bee pollen is a granule-type material of agglutinated flower pollen made by worker honey bees with nectar and bee secretions. The resulting pollen is higher in nutritional value than untreated pollen, and is used as the primary source of protein for the bee hive. Bee pollen has been used in folk medicine from ancient times in many regions of the world for its medical properties to alleviate or cure conditions such as colds, flu, ulcers, and anemia [1]. Recently, biological effects such as antiox- [7], and antimutagenic [8] activities were also reported. Like other apicultural products, royal jelly, honey, and propolis, the chemical composition of bee pollen depends on the plant source, regional vegetation, season, and honeybee races at the site of collection. It is rich in carbohydrates, proteins, lipids, minerals, vitamins, and various organic compounds, including phenolic acids and flavonoids. Most flavonoids from bee pollen exist as glycosides and contain kaempferol or quercetin as aglycone parts. Other phenolic acids usually found in Abstract !The active constituents of Korean Papaver rhoeas bee pollen conferring neuraminidase inhibitory activities (H1N1, H3N2, and H5N1) were investigated. Six flavonoids and one alkaloid were isolated and characterized by nuclear magnetic resonance and mass spectrometry data. These included kaempferol-3-sophoroside (1), kaempferol-3-neohesperidoside (2), kaempferol-3-sambubioside (3), kaempferol-3-glucoside (4), quercetin-3-sophoroside (5), luteolin (6), and chelianthifoline (7). All compounds showed neuraminidase inhibitory activities with IC 50 values ranging from 10.7 to 151.1 µM. The most potent neuraminidase inhibitor was luteolin, which was the dominant content in the ethyl acetate fraction. All tested compounds displayed noncompetitive inhibition of H3N2 neuraminidase. Furthermore, compounds 1-7 all reduced the severity of virally induced cytopathic effects as determined by the Madin-Darby canine kidney cell-based assay showing antiviral activity with IC 50 values ranging from 10.7 to 33.4 µM (zanamivir: 58.3 µM). The active compounds were quantified by highperformance liquid chromatography, and the total amount of compounds 1-7 made up about 0.592 g/100 g bee pollen, contributing a rich resource of a natural antiviral product. Abbreviations
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.
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