Publications

Understanding antibiotic use may help predict emergence of antimicrobial resistance. We evaluated antibiotic prescription trends in rural Burkina Faso, where little is known about antibiotic consumption. Antibiotic prescription data for 20 communities were extracted through record review in six primary health-care facilities serving the communities. The number of antibiotic prescriptions per child-year was calculated using population-based census data from the communities. A total of 1,444 antibiotic prescriptions were made from March to June 2017 among 3,401 children in the communities. The frequency of antibiotic prescription was 1.70 prescriptions per child-year (95% CI: 1.61-1.79). Penicillins were the most common (1.04 prescriptions per child-year, 95% CI: 1.01-1.06), followed by sulfonamides (0.69 prescriptions per child-year, 95% CI: 0.67-0.71) and macrolides (0.38 prescriptions per child-year, 95% CI: 0.37-0.40). Continued monitoring of antibiotic consumption in diverse settings will be important to understand the potential for emergence of antibiotic resistance.

BACKGROUND

Fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) disagree on the hemodynamic significance of a coronary lesion in ≈20% of cases. It is unknown whether the physiological pattern of disease is an influencing factor for this. This study assessed whether the physiological pattern of coronary artery disease influences discordance between FFR and iFR measurement.

METHODS AND RESULTS

Three-hundred and sixty intermediate coronary lesions (345 patients; mean age, 64.4±10.3 years; 76% men) with combined FFR, iFR, and iFR pressure-wire pullback were included for analysis from an international multicenter registry. Cut points for hemodynamic significance were FFR ≤0.80 and iFR ≤0.89, respectively. Lesions were classified into FFR+/iFR+ (n=154; 42.7%), FFR-/iFR+ (n=38; 10.6%), FFR+/iFR- (n=41; 11.4%), and FFR-/iFR- (n=127; 35.3%) groups. The physiological pattern of disease was classified according to the iFR pullback recordings as predominantly physiologically focal (n=171; 47.5%) or predominantly physiologically diffuse (n=189; 52.5%). Median FFR and iFR were 0.80 (interquartile range, 0.75-0.85) and 0.89 (interquartile range, 0.86-0.92), respectively. FFR disagreed with iFR in 22% (79 of 360). The physiological pattern of disease was the only influencing factor relating to FFR/iFR discordance: predominantly physiologically focal was significantly associated with FFR+/iFR- (58.5% [24 of 41]), and predominantly physiologically diffuse was significantly associated with FFR-/iFR+ (81.6% [31 of 38]; P<0.001 for pattern of disease between FFR+/iFR- and FFR-/iFR+ groups).

CONCLUSIONS

The physiological pattern of coronary artery disease was an important influencing factor for FFR/iFR discordance.

We developed an integrated, modular approach to predicting chemical toxicity relying on in vitro assay data, linkage of molecular targets to disease categories, and software for ranking chemical activity and examining structural features (chemotypes). We evaluate our approach in a proof-of-concept exercise to identify and prioritize chemicals of potential carcinogenicity concern. We identified 137 cancer pathway-related assays from a subset of U.S. EPA's ToxCast platforms. We mapped these assays to key characteristics of carcinogens and found they collectively assess 5 of 10 characteristics. We ranked all 1061 chemicals screened in Phases I and II of ToxCast by their activity in the selected cancer pathway-related assays using Toxicological Prioritization Index software. More chemicals used as biologically active agents (eg, pharmaceuticals) ranked in the upper 50% versus lower 50%. Twenty-three chemotypes are enriched in the top 5% (n = 54) of chemicals; these features may be important for their activity in cancer pathway-related assays. The biological coverage of the ToxCast assays related to cancer pathways is limited and short-term assays may not capture the biology of some key characteristics. Metabolism is also minimal in the assays. The ability of our approach to identify chemicals with cancer hazard is limited with the current input data, but we expect that our approach can be applied with future iterations of ToxCast and other data for improved chemical prioritization and characterization. The novel approach and proof-of-concept exercise described here for ranking chemicals for potential carcinogenicity concern is modular, adaptable, and amenable to evolving data streams.