Laboratory animals are often used in drug delivery and research. However, basic information about their gastrointestinal pH, fluid volume, and lymphoid tissue is not completely known. We have investigated these in, post-mortem, healthy guinea pigs, rabbits, and the pigs to assess their suitability for pre-clinical studies by comparing the results with reported human literature. The mean gastric pH (fed ad libitum) was 3.1 and 4.4 in guinea pig and pig respectively. In contrast, a very low pH (1.5) was recorded in the fed rabbits. The small intestinal pH was found in the range of 6.4 to 7.4 in the guinea pigs and rabbits, whereas lower pH (6.1-6.7) was recorded in the pig, which may have consequences for ionisable or pH responsive systems when tested in pig. A relatively lower pH than in the small intestine was found in the caecum (6.0-6.4) and colon (6.1-6.6) of the guinea pig, rabbit and the pig. The water content in the gastrointestinal tract of guinea pig, rabbit and pig was 51 g, 153 g and 1,546 g respectively. When normalized to the body weight, the guinea pig, rabbit and pig had larger amounts of water compared to man (guinea pig > rabbit > pig > man); in contrast, a reverse order was found when normalized to per unit length of the gut (guinea pig < rabbit < pig < man). The lymphoid tissue distribution (lymphoid follicles, Peyer's patches and long strips) along the length of the gut in these animals is presented; in particular, an abundance of lymphoid tissue was found in pig's stomach, small intestine and caecum, and rabbit's appendix. Their ample presence indicated the potential utility of these animal species in oral and colonic vaccination. These differences in the gastrointestinal parameters of the guinea pig, rabbit and pig in comparison to man reiterates the crucial importance of correctly selecting animal models for pre-clinical studies.

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Opinion mining is a very challenging problem, since user generated content is described in various complex ways using natural language. In opinion mining, most of the researchers have worked on general domains such as electronic products, movies, and restaurants reviews but not much on health and medical domains. Patients using drugs are often looking for stories from patients like them on the internet which they cannot always find among their friends and family. Few studies investigating the impact of social media on patients have shown that for some health problems, online community support results in a positive effect. The opinion mining method employed in this work focuses on predicting the drug satisfaction level among the other patients who already experienced the effect of a drug. This work aims to apply neural network based methods for opinion mining from social web in health care domain. We have extracted the reviews of two different drugs. Experimental analysis is done to analyze the performance of classification methods on reviews of two different drugs. The results demonstrate that neural network based opinion mining approach outperforms the support vector machine method in terms of precision, recall and f-score. It is also shown that the performance of radial basis function neural network method is superior than probabilistic neural network method in terms of the performance measures used.

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