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In order to well identify the 93 wild Cortex Daphnes samples from different species and habitats in western China and develop a standard operating procedure (SOP) for the authentication and quality of them in the future, a comprehensive and efficient identification system based on DNA barcoding and HPLC fingerprint technologies has been developed. The result showed that only 17 samples (18%) were Daphne giraldii Nitsche (DG), which is recorded in Chinese Pharmacopeia, while the others (82%) might have safety hazards. Additionally, the result of HPLC fingerprint analysis indicated that samples in the same species origins and wild distributions could be clustered together, which was consistent with DNA barcoding analysis. The study can provide a significant system for the authentication and quality of commercial Cortex Daphnes herbs. Undoubtedly, this study undoubtedly confirmed that the chemical compositions of Cortex Daphnes herbs were affected by both species origins and ecological environments, which is required more in-depth research.

To provide a basis for controlling the quality of rhubarb under artificial cultivation, the present work was designed to evaluate the contents of 14 active pharmaceutical ingredients (API) of rhubarb in major rhubarb production areas in China and analyze the correlations between the contents of API and such factors as species, geographic distribution and soil. The levels of fourteen API in rhubarb were measured using HPLC. The geographic distributions were collected using GPS and the nutrients in the soil were measured using the methods in the literature. The results showed that the levels of major API vary significantly among plants of different locations according to variance analysis. The species factor has few obvious effect on the overall properties of the rhubarb by the cluster analysis because of the two source species occurring in all divided three groups. However, Rheum tanguticum Maxim.ex Balf. is less effective at synthesizing and accumulating 9 API out of 14 than Rheum palmatum L. The correlation analysis and regression analysis also indicated that a lower latitude should be considered in the accumulation of API and a lower longitude should be considered to produce more compound anthraquinones. Lower levels of total P, rapidly available P and available molybdenum (Mo) and higher available K and available Zn in the soil were significantly correlated to accumulation of API in rhubarb. These results provide a basis for the clinical application and controlling the levels of the major API of rhubarb during artificial cultivation.

Drug-drug interaction (DDI) detection is particularly important for patient safety. However, the amount of biomedical literature regarding drug interactions is increasing rapidly. Therefore, there is a need to develop an effective approach for the automatic extraction of DDI information from the biomedical literature. In this paper, we present a Stacked Generalization-based approach for automatic DDI extraction. The approach combines the feature-based, graph and tree kernels and, therefore, reduces the risk of missing important features. In addition, it introduces some domain knowledge based features (the keyword, semantic type, and DrugBank features) into the feature-based kernel, which contribute to the performance improvement. More specifically, the approach applies Stacked generalization to automatically learn the weights from the training data and assign them to three individual kernels to achieve a much better performance than each individual kernel. The experimental results show that our approach can achieve a better performance of 69.24% in F-score compared with other systems in the DDI Extraction 2011 challenge task.

Sleep alleviates Alzheimer's disease (AD)-related neuropathological processes, whereas sleep disturbance in AD patients is associated with elevated peripheral inflammatory cytokine levels. In the present study, we assessed interleukin (IL)-1β and APOEε4 polymorphisms for association with susceptibility of sleep disturbances in AD patients. A total of 123 pretreated AD patients and 120 age-, gender- and education level-matched healthy controls were recruited for two consecutive full-night polysomnography and measurement of Epworth Sleepiness Scale (ESS) scores for sleep-wake disturbance. Their genomic DNA was analyzed for IL-1β and APOEε4 SNPs using ligase detection reaction (LDR) technology. Blood levels of IL-1β, IL-6, and tumor necrosis factor alpha (TNF-α) were measured using ELISA after lipopolysaccharide (LPS) stimulation. The odds ratio and 95% confidence interval for genotype-specific risk were calculated using an unconditional logistic regression model and adjusted by age, gender, educational levels, body mass index (BMI), and activities of daily living (ADL). Compared to the non-APOEε4/ε4 genotype, APOEε4/ε4 significantly increased the risk of AD (APOEε4/ε4 vs. non-APOEε4/ε4, adjusted OR = 4.33, 95% CI = 1.33-14.10, p = 0.015). Compared to the IL-1β CC genotype (-31), the TT genotype significantly increased the risk of AD (TT vs. CC, adjusted OR = 1.72, 95% CI = 1.13-2.61, p = 0.010). AD patients carrying the APOEε4 allele and the IL-1β TT genotype showed less time in bed, longer sleep latency and REM latency, more awakenings, and a lower SWS percentage than those carrying CC/CT combined genotypes. In addition, blood IL-1β levels were significantly greater in AD patients carrying both the APOEε4 allele and the IL-1β-31TT genotype than in those carrying the APOEε4 allele and the -31 TC or CC genotype. In conclusion, this study provides the first evidence indicating that the IL-1β-31TT genotype and homozygous APOEε4 combined are associated with increased risk of developing AD with sleep disturbance.

Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization.

When one drug influences the level or activity of another drug this is known as a drug-drug interaction (DDI). Knowledge of such interactions is crucial for patient safety. However, the volume and content of published biomedical literature on drug interactions is expanding rapidly, making it increasingly difficult for DDIs database curators to detect and collate DDIs information manually. In this paper, we propose a single kernel-based approach to extract DDIs from biomedical literature. This novel kernel-based approach can effectively make full use of syntactic structural information of the dependency graph. In particular, our approach can efficiently represent both single subgraph topological information and the relation of two subgraphs in the dependency graph. Experimental evaluations showed that our single kernel-based approach can achieve state-of-the-art performance on the publicly available DDI corpus without exploiting multiple kernels or additional domain resources.

The recognition and normalization of gene mentions in biomedical literature are crucial steps in biomedical text mining. We present a system for extracting gene names from biomedical literature and normalizing them to gene identifiers in databases. The system consists of four major components: gene name recognition, entity mapping, disambiguation and filtering. The first component is a gene name recognizer based on dictionary matching and semi-supervised learning, which utilizes the co-occurrence information of a large amount of unlabeled MEDLINE abstracts to enhance feature representation of gene named entities. In the stage of entity mapping, we combine the strategies of exact match and approximate match to establish linkage between gene names in the context and the EntrezGene database. For the gene names that map to more than one database identifiers, we develop a disambiguation method based on semantic similarity derived from the Gene Ontology and MEDLINE abstracts. To remove the noise produced in the previous steps, we design a filtering method based on the confidence scores in the dictionary used for NER. The system is able to adjust the trade-off between precision and recall based on the result of filtering. It achieves an F-measure of 83% (precision: 82.5% recall: 83.5%) on BioCreative II Gene Normalization (GN) dataset, which is comparable to the current state-of-the-art.