Watchareewan Thongsaard

A herbal medicine used in the treatment of addiction mimics the action of amphetamine on in vitro rat striatal dopamine release

Thunbergia laurifolia Linn. (TH) and Simplocos racemosa Roxb. (SY) are herbal medicines used in the treatment of drug addiction without scientific support for their mechanism of action. The present study investigated the effects of these medicinal plants on dopaminergic neurotransmission in comparison with amphetamine. The effect of crude water extracts (0.1 g/ml) of TH and SY on K(+) (20 mM)-stimulated dopamine release from rat striatal slices were compared with amphetamine (10(-4) M) using high-performance liquid chromatography with electrochemical detection to measure endogenous dopamine. Amphetamine and TH, but not SY, significantly increased K(+)-stimulated dopamine release (P < 0.001) from rat striatal slices when compared with K(+)-stimulated alone. TH potentiated the effect of amphetamine on K(+)-stimulated dopamine release (P < 0.001) when compared with amphetamine alone. The results indicate that TH may stimulate dopamine release in the same manner as amphetamine. It remains to be determined whether the effect of these extracts on dopamine function is important in their therapeutic use in the treatment of drug addiction.

Local foreground extraction technique for rat walking behavior classification

An important experiment to test effect of new drug in medical science, and brain science is to study rat behaviors. Open-field test such as holeboard model is popular experiment model to analyze rat behavior. Number of each rat behavior in a period of time, example, walking, rearing, and head dip is counted and recorded. This data is compared between before and after using the drug with rat. In present, these rat behaviors are observed and counted by experimenters, that, obviously, it is included human errors easily. In this paper, we proposed a method for improving rat walking behavior classification accuracy by local foreground extraction technique. Webcam is used to record rat behavior and it is mounted over the model 1.5 meters. The proposed method consists of four steps. First, background is constructed for background subtraction by K-Mean clustering technique. Second, foreground as rat is extracted by background subtraction technique. Third step is local extraction. It is applied to complete rat body data from the second step. Finally, rat body length measured by ellipse fitting technique is used for walking behavior classification. To evaluate performance of the proposed method, classification accuracy is measured. Accuracy rate of the proposed method is 83.8%. Results show that accuracy rate of the proposed method is higher than existing method. An advantage of the proposed method is that it improves the accuracy from the existing research.