Citations

BackgroundIt is widely assumed that the amount of alcohol in the blood reflects the amount of alcohol consumed. However, several factors in addition to amount of alcohol consumed can influence blood alcohol concentration (BAC). This study examines the effect of alcohol dose, concentration, and volume onBACin rats with a high‐alcohol‐drinking (HAD) phenotype.MethodsStudy 1 examined the relationship between the amount of alcohol consumed andBAC. Alcohol‐naïve, male,HADrats (N = 7) were given access to alcohol for 2 h/d for 9 consecutive days with food and water ad libitum. Alcohol intake andBACwere measured at 30, 60, and 90 minutes after onset of access. Study 2 examined the effects of altering alcohol dose, concentration, and volume onBAC(as measured by area under the curve). Alcohol‐naïve, male,HADrats (N = 39) were infused, via an intragastric cannulus, with 1.16, 2.44, or 3.38 g alcohol/kg body weight (BW), produced by varying alcohol volume while holding concentration constant or by holding volume constant while varying concentration. Other rats were infused with 10, 15, or 20% v/v alcohol solutions while holding dose constant.ResultsBACwas more strongly correlated with the ratio of alcohol intake (g/kgBW) to total fluid intake (mls) (R = 0.85 to 0.97,p < 0.05 top < 0.001) than it was with the amount of alcohol consumed (g/kgBW) (R = 0.70 to 0.81,p < 0.05). No effect of alcohol dose was seen during the first hour following the onset of an alcohol infusion regardless of whether dose was achieved by altering alcohol volume or concentration. After 1 hour, higher alcohol doses were predictive of greaterBACs.ConclusionsThe fact that a 3‐fold difference in alcohol dose did not result in significant differences inBACs during the first 30 minutes after ingestion of alcohol has potentially important implications for interpretation of studies that measure alcohol‐sensitive end points during this time.

Abstract Background Trends in the distribution of ribotypes for C. difficile associated diarrheal isolates obtained over time in the United States are lacking. As part of surveillance program for C. difficile susceptibility, we analyzed stool isolates for ribotype distribution from a phase 2 trial of surotomycin (2010–2011) (North America sites) as well as a national surveillance study from 2011–2014. Isolates for the surveillance study were referred from 6 geographically distinct medical centers. Methods C. difficile isolates or C. difficile toxin + stools from patients with C. difficile associated diarrhea (CDAD) were submitted for testing to Tufts Medical Center. Following isolation and confirmation as C. difficile, a random sample of isolates were ribotyped by PCR capillary gel electrophoresis. Results 673 isolates over the 5 years of the analysis have been ribotyped to date. There were 49 unique ribotype designations, and 16 ribotypes had more than 10 isolates. The ribotype distribution by year is shown in the table. Conclusion There has been a change in the frequency of ribotypes over time in the US. Of the most common ribotypes seen, 027 has decreased by over 50% while there has been an increase of 014-020, 002, and 106. 014-020 is now the most common ribotype seen in the US. These data suggest that there is a changing epidemiology of C. difficile in the US and continuous monitoring of the ribotype distributions and clinical implications is warranted. Disclosures D. R. Snydman, Merck: Consultant and Grant Investigator, Consulting fee and Research grant; Shire: Consultant, Consulting fee; Summit PLC: Consultant and Grant Investigator, Consulting fee and Research grant; BioK+: Consultant, Consulting fee; Actelion: Grant Investigator, Research grant; S. G. Jenkins, Cormedix: Consultant, Consulting fee; Bayer: Consultant, Consulting fee; Merck: Grant Investigator and Scientific Advisor, Research grant; E. J. C. Goldstein, Merck: Grant Investigator, Scientific Advisor and Speaker’s Bureau, Consulting fee and Grant recipient; Cubist: Grant Investigator, Scientific Advisor and Speaker’s Bureau, Consulting fee; Actelion: Grant Investigator, Grant recipient; Summit PLC: Grant Investigator and Scientific Advisor, Grant recipient; R. Patel, Curetis: Consultant, Grant Investigator and Speaker’s Bureau, Consulting fee and Research grant; Pocared: Grant Investigator, Research grant; nanoMR: Grant Investigator, Research grant; BioFire: Grant Investigator, Research grant; Check-Points: Grant Investigator, Research grant; 3M: Grant Investigator, Research grant; Cubist: Grant Investigator, Research grant; Merck: Grant Investigator, Research grant; S. Johnson, Bio-K+: Consultant, Consulting fee; D. N. Gerding, Merck, Shire, Cubist, Rebiotix, sanofi pasteur, Summit, DaVoltera, Actelion: Consultant, Consulting fee; CDC, US Dept of Veterans Affairs Research Service: Grant Investigator, Research grant

Abstract Background Detection and identification of microorganism(s) involved in periprosthetic joint infection (PJI) can inform surgical management and directed antibiotic therapy. Metagenomic shotgun sequencing is a powerful tool with the potential to change how many PJIs are diagnosed as it allows direct detection and identification of pathogens in clinical specimens. In the largest series to date, we utilized a metagenomics-based approach applied to SF to define potential microbial etiologies of failed total knee arthroplasties (TKAs). Methods Synovial fluid was collected from 112 failed TKAs [74 PJI and 38 aseptic implant failure (AF)] via preoperative arthrocentesis. Cell count and differential, standardized culture and DNA-based metagenomic shotgun sequencing were performed. Human DNA was depleted using the MolYsis basic kit prior to DNA extraction, whole genome amplification, and sequencing. Taxonomic assignment of reads and pathogen identification was achieved using a pipeline incorporating k-mer- and marker gene-based classification software. A scheme for analysis and filtration of false-positives was created and applied, incorporating cut-offs for the number of reads, quality scores, and coverage across a reference genome. Patients were classified as having PJI using the IDSA criteria and expert review. Analyses were recorded as percent agreement, with 95% confidence intervals (CI), of metagenomics to SF culture. Results Metagenomic analysis identified the known pathogen in 54 (90%) (CI, 79.5%–96.2%) of the 60 culture-positive PJIs analyzed and one (2%) (CI, 0.0%–8.9%) potential polymicrobial infection not detected by culture. For the 14 culture-negative PJIs tested, metagenomics showed 79% (CI, 49.2%–95.3%) agreement for negative findings; potential pathogens were identified in three (21%) (CI, 4.7%–50.8%) culture-negative PJI cases, with one being polymicrobial. Of the 37 culture-negative AF cases, metagenomics showed 97% (CI, 85.8%–99.9%) agreement with negative culture and identified one (3%) (CI, 0.0%–14.2%) potential pathogen. For the one culture-positive AF case, metagenomic results were negative, suggesting possible culture contamination. Conclusion Metagenomic shotgun sequencing performed on SF can be used to diagnose PJI and may be particularly useful for culture-negative PJI. Disclosures R. Patel, ASM: Board Member, None; CD Diagnostics, BioFire, Curetis, Merck, Hutchison Biofilm Medical Solutions, Accelerate Diagnostics, Allergan, and The Medicines Company: Grant Investigator, Grant recipient; Curetis: Consultant, Monies paid to my employer; A patent on Bordetella pertussis/parapertussis PCR issued, a patent on a device/method for sonication with royalties paid by Samsung to Mayo Clinic, and a patent on an anti-biofilm substance issued: Patents, Patents, any money is paid to my employer; Actelion: DSMB, Money paid to my employer; ASM and IDSA: Editor’s stipends, Editor’s stipends; NBME, Up-to-Date and the Infectious Diseases Board Review Course: NBME, Up-to-Date and the Infectious Diseases Board Review Course, Honoraria; Roche, ASM, and IDSA: Travel reimbursement, Travel reimbursement