Clostridium perfringens carrying the enterotoxin gene is an important cause of both foodborne and non-foodborne diarrheal disease. Rapid identification of isolates carrying the enterotoxin gene is invaluable for outbreak investigation whilst information on the genomic location of the enterotoxin (cpe) gene can improve our understanding of disease transmission. This paper describes the validation of a real-time polymerase chain reaction (PCR) assay for the identification of C. perfringens and assessment of the potential to cause diarrhea, together with an investigation into the genomic location of the cpe genes in isolates from confirmed incidents of C. perfringens diarrhea. The real-time assay was shown to be specific for the identification of 253 C. perfringens cultures and gave results concordant with those from motility nitrate and lactose gelatine media, the Nagler reaction, and a conventional block-based PCR assay. The cpe gene was detected in 223 of 253 C. perfringens cultures isolated in association with human gastrointestinal disease. A subset of cpe-positive C. perfringens isolates associated with separate incidents of diarrheal disease were investigated further for plasmid or chromosomal location of the cpe gene using a multiplex PCR assay. The cpe gene was plasmid encoded in two isolates from cases of sporadic diarrhea and six isolates from cases of food poisoning. The cpe gene from the remaining 11 isolates from different food poisoning outbreaks was found to be chromosomally encoded. One of the C. perfringens strains with a plasmid encoded cpe gene formed spores of high heat resistance and five formed spores that were sensitive to heating. Eight of the isolates with a chromosomal cpe gene formed heat-resistant spores, and two formed spores with an intermediate heat resistance.

Purpose The purpose of this paper is to present a framework that captures the strategic value of artificial intelligence (AI) systems. Although AI has become a crucial component of digital transformation efforts tied to organizational strategy, many firms struggle to derive strategic value from emerging AI systems. Design/methodology/approach The analytical framework in this paper is based on a learning-centered approach. Specifically, by building on the knowledge-based perspective, this paper elaborates on how AI can contribute to organizational learning to create a competitive advantage in knowledge-intensive contexts. Findings This paper argues that the power of AI as a strategic resource lies in its self-learning capacities. Such learning capacities are only realized in partnership with humans through mutual learning. This paper formulates the concept of artificial capital and the ways artificial and human capital can together drive routinization and strategic learning processes that connect internal and external environments of the organization. Originality/value This is a timely contribution as many organizations are considering adopting AI technologies for strategic purposes. This paper translates the proposed framework into several practical implications for managing and developing AI to meet strategic business goals.

The obstetric emergency shoulder dystocia is a relatively rare event which is generally unpredictable but is associated with a high level of fetal mortality and morbidity and has the potential to lead to litigation. To minimize these problems, it is essential that health professionals are adequately trained in the management of shoulder dystocia. This article presents the results of a survey of training activities and an analysis of practice guidelines in relation to shoulder dystocia. The findings suggest a need for a more formalized, structured and monitored approach to training.

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