Gene/Protein Disease Symptom Drug Enzyme Compound
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 4,687 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The effects of the methanolic extract of Radix Angelica Sinensis (Umbellifera) (abbreviated as RAS extract) and n-hexane fraction of RAS extract (RAS(H) fraction) on the various drugs-induced amnesia in rats were studied by using passive avoidance task. RAS extract (1 g/kg) significantly prolonged the shortened step-through latency induced by SCOP and CXM, but not PCA. Furthermore, RAS(H) fraction (1 g/kg) also significantly prolonged the shortened step-through latency induced by SCOP and CXM but not PCA. RAS extract at any dose alone did not influence the step-through latency in the training trial produced by non-shocked rats, but it plus PCA prolonged the latency compared with PCA alone. However, RAS(H) fraction (1 g/kg) prolonged the latency in the training trial produced by non-shocked rats, but it plus any induced drugs did not differ from any induced drugs alone. These results suggest that the attenuating effects of RAS extract on the various drugs-induced amnesia were related to the memory processes. n-Hexane fraction of RAS extract might be one of the active fractions of RAS extract in the treatment of amnesia.
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PMID:Radix Angelica Sinensis extracts ameliorate scopolamine- and cycloheximide-induced amnesia, but not p-chloroamphetamine-induced amnesia in rats. 1099 45

Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid sequences to obtain better classifiers. In this paper, we have proposed six descriptors to extract features from protein sequences. These descriptors are applied in the first stage of a three-stage framework PCA-DELM-LDA to extract feature vectors from the amino-acid sequences. Principal Component Analysis PCA has been implemented to reduce the number of extracted features. The extracted feature vectors have been used with original features to improve the performance of the Deep Extreme Learning Machine DELM in the second stage. Four new features have been extracted from the second stage and used in the third stage by Linear Discriminant Analysis LDA to classify the instances into 27 folds. The proposed framework is implemented on the independent and combined feature sets in SCOP datasets. The experimental results show that extracted feature vectors in the first stage could improve the performance of DELM in extracting new useful features in second stage.
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PMID:Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition. 2835 1