Defining Molecular Details of the Chemistry of Biofilm Formation by Raman Microspectroscopy.

Two protocols that allow for the comparison of Raman spectra of planktonic cells and biofilm formed from these cells in their growth phase have been developed. Planktonic cells are washed and flash-frozen in <1 min to reduce the time for metabolic changes during processing, prior to freeze-drying. Biofilm is formed by standing cells in 50 µL indentations in aluminum foil in an atmosphere of saturated water vapor for 24-48 h. The results for Escherichia coli type K12 cells, which do not readily form biofilm, are compared to those for Staphylococcus epidermidis cells, which prolifically synthesize biofilm. For E. coli, the Raman spectra of the planktonic and biofilm samples are similar with the exception that the spectral signature of RNA, present in planktonic cells, could not be detected in biofilm. For S. epidermidis, major changes occur upon biofilm formation. In addition to the absence of the RNA features, new bands occur near 950 cm-1 and between 1350 and 1420 cm-1 that are associated with an increase in carbohydrate content. Unlike the case in E. coli biofilm, the intensity of G base ring modes is reduced in but A and T base ring signatures become more prominent. For S. epidermis in the biofilm's amide III region, there is evidence of an increase in the level of ß-sheet structure accompanied by a decrease in α-helical content. The presence of biofilm is confirmed by microscope-aided photography and, separately, by staining with methyl violet.

Use of the Electronic Health Record to Optimize Antimicrobial Prescribing.

PURPOSE: This review summarizes how interventions in the electronic health record (EHR) can optimize antimicrobial stewardship across the continuum of antimicrobial decision making, from diagnosis of infection to discontinuation of therapy. In addition, opportunities to optimize provider communication and patient education are identified. METHODS: A narrative review was conducted to identify how interventions in the EHR can influence antimicrobial prescribing behavior. Examples from pediatrics were specifically identified. Interventions were then categorized into high-impact/low-effort, high-impact/high-effort, and low-impact/low-effort groupings based on historical experience. FINDINGS: EHR-based interventions can be used for stratifying patients at risk for infection and are useful in identifying patients with new-onset infections. Additional tools include automatically updated antibiograms tailored to specific patient populations, timely authorization of restricted antimicrobials, and more accurate allergy labeling. Medical errors can be reduced and communication between providers can be improved by standardized data fields. Clinical decision support tools can guide appropriate selection of therapy, and visual prompts can reduce unnecessarily prolonged therapy. Benchmarking of antimicrobial use, tailored patient education, and improved communication during transitions of care are enhanced through EHR-based interventions. IMPLICATIONS: Prescribing behavior can be modified through a range of interventions in the EHR, including tailored education, alerts, prompts, and restrictions on provider behavior. Further studies are needed to compare the effectiveness of various strategies.

Measuring Drug-Induced Changes in Metabolite Populations of Live Bacteria: Real Time Analysis by Raman Spectroscopy.

Raman difference spectroscopy is shown to provide a wealth of molecular detail on changes within bacterial cells caused by infusion of antibiotics or hydrogen peroxide. Escherichia coli strains paired with chloramphenicol, dihydrofolate reductase propargyl-based inhibitors, meropenem, or hydrogen peroxide provide details of the depletion of protein and nucleic acid populations in real time. Additionally, other reproducible Raman features appear and are attributed to changes in cell metabolite populations. An initial candidate for one of the metabolites involves population increases of citrate, an intermediate within the tricarboxyclic acid cycle. This is supported by the observation that a strain of E. coli without the ability to synthesize citrate, gltA, lacks an intense feature in the Raman difference spectrum that has been ascribed to citrate. The methodology for obtaining the Raman data involves infusing the drug into live cells, then washing, freezing, and finally lyophilizing the cells. The freeze-dried cells are then examined under a Raman microscope. The difference spectra [cells treated with drug] - [cells without treatment] are time-dependent and can yield population kinetics for intracellular species in vivo. There is a strong resemblance between the Raman difference spectra of E. coli cells treated with meropenem and those treated with hydrogen peroxide.