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Whole-genome sequencing (WGS) has traditionally been used in infection prevention to confirm or refute the presence of an outbreak after it has occurred. Due to decreasing costs of WGS, an increasing number of institutions have been utilizing WGS-based surveillance. Additionally, machine learning or statistical modeling to supplement infection prevention practice have also been used. We systematically reviewed the use of WGS surveillance and machine learning to detect and investigate outbreaks in healthcare settings.
We performed a PubMed search using separate terms for WGS surveillance and/or machine-learning technologies for infection prevention through March 15, 2021.
Of 767 studies returned using the WGS search terms, 42 articles were included for review. Only 2 studies (4.8%) were performed in real time, and 39 (92.9%) studied only 1 pathogen. Nearly all studies (n = 41, 97.6%) found genetic relatedness between some isolates collected. Across all studies, 525 outbreaks were detected among 2,837 related isolates (average, 5.4 isolates per outbreak). Also, 35 studies (83.3%) only utilized geotemporal clustering to identify outbreak transmission routes. Of 21 studies identified using the machine-learning search terms, 4 were included for review. In each study, machine learning aided outbreak investigations by complementing methods to gather epidemiologic data and automating identification of transmission pathways.
WGS surveillance is an emerging method that can enhance outbreak detection. Machine learning has the potential to identify novel routes of pathogen transmission. Broader incorporation of WGS surveillance into infection prevention practice has the potential to transform the detection and control of healthcare outbreaks.
Identifying routes of transmission among hospitalized patients during a healthcare-associated outbreak can be tedious, particularly among patients with complex hospital stays and multiple exposures. Data mining of the electronic health record (EHR) has the potential to rapidly identify common exposures among patients suspected of being part of an outbreak.
We retrospectively analyzed 9 hospital outbreaks that occurred during 2011–2016 and that had previously been characterized both according to transmission route and by molecular characterization of the bacterial isolates. We determined (1) the ability of data mining of the EHR to identify the correct route of transmission, (2) how early the correct route was identified during the timeline of the outbreak, and (3) how many cases in the outbreaks could have been prevented had the system been running in real time.
Correct routes were identified for all outbreaks at the second patient, except for one outbreak involving >1 transmission route that was detected at the eighth patient. Up to 40 or 34 infections (78% or 66% of possible preventable infections, respectively) could have been prevented if data mining had been implemented in real time, assuming the initiation of an effective intervention within 7 or 14 days of identification of the transmission route, respectively.
Data mining of the EHR was accurate for identifying routes of transmission among patients who were part of the outbreak. Prospective validation of this approach using routine whole-genome sequencing and data mining of the EHR for both outbreak detection and route attribution is ongoing.
Taking an inductive approach followed by validation techniques, we analyzed the meaning and dimensionality of the organizational justice construct in the People's Republic of China (China). By using qualitative and quantitative methods, we found that organizational justice involves a higher level of subjectivity in the Chinese context. Interpretation and organizational justice judgments depend on the specific situation and social relations involved. The findings revealed similarities with the Western conceptualization with one important exception, suggesting both cultural values and institutional environment shape justice judgments and managerial practices in China. The article concludes with a discussion of the findings and implications for management theory, research, and practice.
Guanxi networks are critical for achieving entrepreneurial success in China. Informed by the literatures on network-based entrepreneurship and guanxi, this study used a multiple-case method to examine the development of Chinese entrepreneurs' guanxi networks in the entrepreneurial process. Data induction based on within- and cross-case analyses of six entrepreneurial firms revealed three major findings. First, guanxi network dynamics in terms of network structure, governance mechanisms, and network content change systematically across the stages of the entrepreneurship process. Second, the usefulness of guanxi ties (such as family, business associates, or government officials) is contingent on the stage of the entrepreneurial process as well as on industrial-level factors. Third, in knowledge-intensive industries, cultivating and maintaining guanxi can be achieved through knowledge and information exchange rather than traditional gift-giving or favours. The overall conclusion is that guanxi is still of paramount importance for Chinese entrepreneurs in the midst of China's transformation from a centrally planned to a socialist market economy. We propose a stage model of guanxi network development in the entrepreneurial process and discuss implications for future research.
The central nervous system undertakes the homeostatic role of sensing nutrient intake and body reserves, integrating the information, and regulating energy intake and/or energy expenditure. Few tasks regulated by the brain hold greater survival value, particularly important in farmed ruminant species, where the demands of pregnancy, lactation and/or growth are not easily met by often bulky plant-based and sometimes nutrient-sparse diets. Information regarding metabolic state can be transmitted to the appetite control centres of the brain by a diverse array of signals, such as stimulation of the vagus nerve, or metabolic ‘feedback’ factors derived from the pituitary gland, adipose tissue, stomach/abomasum, intestine, pancreas and/or muscle. These signals act directly on the neurons located in the arcuate nucleus of the medio-basal hypothalamus, a key integration, and hunger (orexigenic) and satiety (anorexigenic) control centre of the brain. Interest in human obesity and associated disorders has fuelled considerable research effort in this area, resulting in increased understanding of chronic and acute factors influencing feed intake. In recent years, research has demonstrated that these results have relevance to animal production, with genetic selection for production found to affect orexigenic hormones, feeding found to reduce the concentration of acute controllers of orexigenic signals, and exogenous administration of orexigenic hormones (i.e. growth hormone or ghrelin) reportedly increasing DM intake in ruminant animals as well as single-stomached species. The current state of knowledge on factors influencing the hypothalamic orexigenic and anorexigenic control centres is reviewed, particularly as it relates to domesticated ruminant animals, and potential avenues for future research are identified.
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