Approximately 30 years ago, it was discovered that free-living bacteria isolated from cold ocean depths could produce polyunsaturated fatty acids (PUFA) such as eicosapentaenoic acid (EPA) (20:5n-3) or docosahexaenoic acid (DHA) (22:6n-3), two PUFA essential for human health. Numerous laboratories have also discovered that EPA-and/or DHA-producing bacteria, many of them members of the Shewanella genus, could be isolated from the intestinal tracts of omega-3 fatty acid-rich marine fish. If bacteria contribute omega-3 fatty acids to the host fish in general or if they assist some bacterial species in adaptation to cold, then cold freshwater fish or habitats should also harbor these producers. Thus, we undertook a study to see if these niches also contained omega-3 fatty acid producers. We were successful in isolating and characterizing unique EPA-producing strains of Shewanella from three strictly freshwater native fish species, i.e., lake whitefish (Coregonus clupeaformis), lean lake trout (Salvelinus namaycush), and walleye (Sander vitreus), and from two other freshwater nonnative fish, i.e., coho salmon (Oncorhynchus kisutch) and seeforellen brown trout (Salmo trutta). We were also able to isolate four unique free-living strains of EPA-producing Shewanella from freshwater habitats. Phylogenetic and phenotypic analyses suggest that one producer is clearly a member of the Shewanella morhuae species and another is sister to members of the marine PUFA-producing Shewanella baltica species. However, the remaining isolates have more ambiguous relationships, sharing a common ancestor with non-PUFA-producing Shewanella putrefaciens isolates rather than marine S. baltica isolates despite having a phenotype more consistent with S. baltica strains.

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Objective Wrong drug product errors occurring in community pharmacies often originate at the transcription stage. Electronic prescribing and automated product selection are strategies to reduce product selection errors. However, it is unclear how often automated product selection succeeds in outpatient pharmacy platforms. Materials and Methods The intake of over 800 e-prescriptions was observed at baseline and after intervention to assess the rate of automated product selection success. A dispensing accuracy audit was performed at baseline and postintervention to determine whether enhanced automated product selection would result in greater accuracy; data for both analyses were compared by 2x2 Chi square tests. In addition, an anonymous survey was sent to a convenience sample of 60 area community pharmacy managers. Results At baseline, 79.8% of 888 e-prescriptions achieved automated product selection. After the intervention period, 84.5% of 903 e-prescriptions achieved automated product selection (P = .008). Analysis of dispensing accuracy audits detected a slight but not statistically significant improvement in accuracy rate (99.3% versus 98.9%, P = .359). Fourteen surveys were returned, revealing that other community pharmacies experience similar automated product selection failure rates. Discussion Our results suggest that manual product selection by pharmacy personnel is required for a higher than anticipated proportion of e-prescriptions received and filled by community pharmacies, which may pose risks to both medication safety and efficiency. Conclusion The question of how to increase automated product selection rates and enhance interoperability between prescriber and community pharmacy platforms warrants further investigation.