A prospective study was carried out on 26,176 Malaysian neonates born in the Maternity Hospital, Kuala Lumpur over a 12-month period to determine the incidence and associated risk factors of brachial plexus injuries. This condition was found in 42/26,176 neonates (1.6 per 1000 livebirths). Multiple logistic regression analysis of affected and control neonates from a nested case-control study showed that increasing birth weights and breech deliveries were the significant risk factors. Our study suggests that to reduce the occurrence of this condition, there is a need for: (i) better assessment of fetal size and maternal pelvimetry to enable earlier diagnosis of cephalo-pelvic disproportion, and (ii) review of the indications and techniques of breech delivery.
Life science ontologies play an important role in Semantic Web. Given the diversity in fish species and the associated wealth of information, it is imperative to develop an ontology capable of linking and integrating this information in an automated fashion. As such, we introduce the Fish Ontology (FO), an automated classification architecture of existing fish taxa which provides taxonomic information on unknown fish based on metadata restrictions. It is designed to support knowledge discovery, provide semantic annotation of fish and fisheries resources, data integration, and information retrieval. Automated classification for unknown specimens is a unique feature that currently does not appear to exist in other known ontologies. Examples of automated classification for major groups of fish are demonstrated, showing the inferred information by introducing several restrictions at the species or specimen level. The current version of FO has 1,830 classes, includes widely used fisheries terminology, and models major aspects of fish taxonomy, grouping, and character. With more than 30,000 known fish species globally, the FO will be an indispensable tool for fish scientists and other interested users.