Mites are known causes of allergic diseases. Currently, identification of mites based on morphology is difficult if only one mite is isolated from a (dust) sample, or when only one gender is found, or when the specimen is not intact especially with the loss of the legs. The purpose of this study was to use polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) of the ITS2 gene, to complement the morphological data for the identification of mites to the species level. For this, six species were cultured: Dermatophagoides pteronyssinus, D. farinae, Blomia tropicalis, Tyrophagus putrescentiae, Aleuroglyphus ovatus and Glycycometus malaysiensis. Genomic DNA of the mites was extracted, quantified, amplified and digested individually with restriction enzymes. Hinf I and Ple I differentiated the restriction patterns of D. pteronyssinus and D. farinae. Bfa I and Alu I enzymes differentiated B. tropicalis and G. malaysiensis. Ple I enzyme was useful for the differentiation between T. putrescentiae and A. ovatus. Bfa I was useful for the differentiation of G. malaysiensis from the rest of the species. In conclusion, different species of mites can be differentiated using PCR-RFLP of ITS2 region. With the established PCR-RFLP method in this study, identification of these mites to the species level is possible even if complete and intact adult specimens of both sexes are not available. As no study to date has reported PCR-RFLP method for the identification of domestic mites, the established method should be validated for the identification of other species of mites that were not included in this study.
Big data is anticipated to have large implications in clinical pharmacy, in view of its potential in enhancing precision medicine and to avoid medication error. However, it is equally debatable since such a powerful tool may also disrupt the need of pharmacist in healthcare industry. In this article, we commented the contribution of Big Data in various aspects of clinical pharmacy including advancing pharmaceutical care service, optimising drug supplies, managing clinical trials, and strengthening pharmacovigilance. The future direction of the usage of Big Data related to clinical pharmacy will be discussed. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.