Diagnostic performance of pressure-bounded coronary flow reserve.

Fluid dynamics studies have proposed that coronary flow reserve can be calculated from coronary artery pressure instead of coronary blood flow. We sought to investigate the diagnostic performance of pressure-bounded coronary flow reserve (pb-CFR) compared with CFR measured by conventional thermodilution method (CFRthermo) in the clinical setting. Pressure guidewire was used to measure CFRthermo and fractional flow reserve (FFR) in left anterior descending coronary artery in 62 patients with stable coronary artery disease. Pb-CFR was calculated only with resting distal coronary artery pressure (Pd), resting aortic pressure (Pa) and FFR. Pb-CFR was moderately correlated with CFRthermo (r = 0.54, P < 0.001). Pb-CFR showed a poor agreement with CFRthermo, presenting large values of mean difference and root mean square deviation (1.5 ± 1.4). Pb-CFR < 2.0 predicted CFRthermo < 2.0 with an accuracy of 79%, sensitivity of 83%, specificity of 78%, positive predictive value of 48%, negative predictive value of 95%. The discordance presenting CFRthermo < 2.0 and pb-CFR ≥ 2.0 was associated with diffuse disease (P < 0.001). The discordance presenting CFRthermo ≥ 2 and pb-CFR < 2 was associated with a high FFR (P = 0.002). Pb-CFR showed moderate correlation and poor agreement with CFRthermo. Pb-CFR might be reliable in excluding epicardial coronary artery disease and microcirculatory disorders.

Nosocomial Outbreak of Upper Respiratory Tract Infection With ß-Lactamase-Negative Ampicillin-Resistant Nontypeable Haemophilus influenzae.

OBJECTIVETo describe the epidemiologic features of an outbreak of an acute respiratory tract infection (ARI) caused by ß-lactamase-negative ampicillin-resistant (BLNAR) nontypeable Haemophilus influenzae (NTHi) in an acute-care ward.DESIGNCross-sectional case-control study.SETTINGAn acute-care ward (ward A) in a general hospital of Kochi in western Japan.METHODSPatients who shared a room with an index patient and all staff in ward A were screened and followed from July 1 to August 31, 2015. Sputum or throat swab samples were collected from participants and tested by culture and polymerase chain reaction (PCR). The association between detected pathogens and ARI development among all participants was examined. A case-control study was conducted to identify risk factors for disease.RESULTSIn total, 78 participants, including the index patient, were enrolled. Of all participants, 27 (34.6%) developed mild respiratory symptoms during a 3-week period: 24 were diagnosed as upper respiratory tract infections, and 3 were diagnosed as lower respiratory tract infections. The presence of BLNAR NTHi was confirmed in 13 participants, and multilocus sequence typing demonstrated that these isolates belonged to sequence type 159. All isolates showed identical pulsed-field gel electrophoresis patterns. The presence of BLNAR NTHi was strongly associated with ARI development, whereas viruses were not associated with the disease. Multivariate analyses demonstrated that a history of contact with the index patient was independently associated with ARI caused by BLNAR NTHi.CONCLUSIONSBLNAR NTHi has the potential to cause upper respiratory tract infections among adults and to spread rapidly in hospital settings.Infect Control Hosp Epidemiol 2018;39:652-659.

Random sample consensus combined with partial least squares regression (RANSAC-PLS) for microbial metabolomics data mining and phenotype improvement.

In recent years, the advent of high-throughput omics technology has made possible a new class of strain engineering approaches, based on identification of possible gene targets for phenotype improvement from omic-level comparison of different strains or growth conditions. Metabolomics, with its focus on the omic level closest to the phenotype, lends itself naturally to this semi-rational methodology. When a quantitative phenotype such as growth rate under stress is considered, regression modeling using multivariate techniques such as partial least squares (PLS) is often used to identify metabolites correlated with the target phenotype. However, linear modeling techniques such as PLS require a consistent metabolite-phenotype trend across the samples, which may not be the case when outliers or multiple conflicting trends are present in the data. To address this, we proposed a data-mining strategy that utilizes random sample consensus (RANSAC) to select subsets of samples with consistent trends for construction of better regression models. By applying a combination of RANSAC and PLS (RANSAC-PLS) to a dataset from a previous study (gas chromatography/mass spectrometry metabolomics data and 1-butanol tolerance of 19 yeast mutant strains), new metabolites were indicated to be correlated with tolerance within certain subsets of the samples. The relevance of these metabolites to 1-butanol tolerance were then validated from single-deletion strains of corresponding metabolic genes. The results showed that RANSAC-PLS is a promising strategy to identify unique metabolites that provide additional hints for phenotype improvement, which could not be detected by traditional PLS modeling using the entire dataset.

A simple and portable colorimeter using a red-green-blue light-emitting diode and its application to the on-site determination of nitrite and iron in river-water.

A portable colorimeter using a red-green-blue light-emitting diode as a light source has been developed. An embedded controller sequentially turns emitters on and off, and acquires the signals detected by two photo diodes synchronized with their blinking. The controller calculates the absorbance and displays it on a liquid-crystal display. The whole system, including a 006P dry cell, is contained in a 100 x 70 x 50 mm aluminum case and its mass is 280 g. This colorimeter was successfully applied to the on-site determination of nitrite and iron in river-water.