Biomedical subjects

Jihua Huang

Publications and source records attributed to Jihua Huang.

4 recordsLinked to original sources

PubMed2005

[Study on the antipyretic, analgesic effects of qingkailing freeze-dried powder for injection and its antipyretic mechanism].

OBJECTIVE: To investigate the antipyretic, analgesic effects of Qingkailing Freeze-Dried Powder for Injection (QI) and its mechanism of the antipyretic effect. METHODS: The rabbit model of 2, 4-dinitrophenol-induced fever and the rat model of endotoxin-induced fever were established and the body temperatures were observed. Radioimmunoassay was adopted to detect the levels of IL-1beta and cAMP in the hypothalamus of rats. The analgesic effects were tested by hot-plate and writhing reaction method. RESULTS: QI showed marked antipyretic effects on the 2,4-dinitrophenol-induced fever and the endotoxin-induced fever. At the same time, QI remarkably decreased the contents of IL-beta and cAMP in the hypothalamus. QI also remarkably reduced the frequency of acetic acid-induced writhing and delaied the time of licking rear feet. CONCLUSION: QI has antipyretic, analgesic effects. Its mechanism of the antipyretic effects on rats may be inhibiting the production of IL-1beta and accordingly decreasing cAMP in the hypothalamus.

2,4-Dinitrophenol↗

PubMed2003

Clustering gene expression pattern and extracting relationship in gene network based on artificial neural networks.

Massive datasets such as gene expression profiles are accumulating along with the development of DNA microarray technologies. In this paper, we focus on mining biological relevant information such as typical expression patterns and the interconnections of gene networks from massive datasets. At first, the algorithm of a self-organizing map (SOM) was used to cluster gene expression data. Then, for the typical patterns extracted by the SOM, a three-layer artificial neural network (ANN) model was used to extract the relationships between the expression patterns. In order to evaluate the clustering analysis based on the SOM, biological and statistical indices were introduced. To validate the efficiency of the scheme proposed for extracting the relationships between the expression patterns with the ANN, a test dataset was created and used for the test. Finally, the interconnections of a typical pattern of early G1, late G1, S, G2, and M phases in a yeast cell cycle were extracted and visualized.

Journal Article↗

PubMed2002

Classification of fermentation performance by multivariate analysis based on mean hypothesis testing.

Multivariate analysis, such as principal component analysis and artificial autoassociative neural networks, is currently extensively applied to feature capturing, physiological state recognition, fault detection and bioprocess control. However, it is not clear which process variable should be selected as an important input for multivariate analysis to analyze physiological conditions and/or bioprocess performance a priori. An efficacious method to select more informative process variables from the repository of historical data is highly desired. In this study, we focused on a premodeling step. Mean hypothesis testing (MHT) was used to select appropriate variables for multivariate analysis. Fermentation data sets were classified into two classes "good" and "bad" according to the MHT results. The results showed that selecting discriminating process variables from the historical database by MHT enhanced the overall effectiveness of multivariate analysis prior to principal component analysis and artificial autoassociative neural network model creation.

Journal Article↗

PubMed2002

Data preprocessing and output evaluation of an autoassociative neural network model for online fault detection in virginiamycin production.

In this study, an artificial autoassociative neural network (AANN) was used online to detect deviations from normal antibiotic production fermentation using conventional process variables. To improve the efficiency of extracting hidden information contained in multidimensional process variables, and to finally render the AANN adequate for fault detection, we explored the following methods: selection of process variables; preprocessing of data that involved normalizing the training data of the AANN; and evaluation of data that involved assessing the output of the AANN. A method for fault detection in virginiamycin M and S production by Streptomyces virginiae was successfully developed based on these techniques.

Journal Article↗