Factors that determine glucose variability, defined by the coefficient of variation in continuous glucose monitoring values, in a Chinese population with type 2 diabetes.

AIMS: To elucidate the clinical determinants of the coefficient of variation (CV) of glucose by analysing the pancreatic ß-cell function of subjects with type 2 diabetes mellitus (T2DM). METHODS: A total of 716 Chinese subjects with T2DM were included. Continuous glucose monitoring (CGM) was used to assess blood glucose, and the CV was calculated. C-peptide concentration at 0, 0.5, 1, 2 and 3 hours (Cp0h, Cp0.5h, Cp1h, Cp2h and Cp3h, respectively) was measured after a standard 100-g steamed bun meal test to assess pancreatic ß-cell function. The determinants of glucose variability defined by the CV of CGM values were explored from two perspectives: the CV of qualitative variables and the CV of quantitative variables. RESULTS: Our data revealed that C-peptide concentration (Cp0h, Cp0.5h, Cp1h, Cp2h, Cp3h), area under the curve for C-peptide concentration at 0.5 and 3 hours (AUC-Cp0.5h and AUC-Cp3h) decreased with increasing CV quartile (P < 0.05). The CV was negatively correlated with homeostatic model assessment of ß-cell function index, C-peptide concentration at all timepoints, and AUC-Cp0.5h and AUC-Cp3h (P < 0.001). Quantile regression analysis showed that AUC-Cp0.5h had an overall negative effect on the CV in the 0.05 to 0.95 quartiles, and AUC-Cp3h tended to have a negative effect on the CV in the 0.2 to 0.65 quartiles. After adjusting for confounders, multinomial logistic regression showed that each 1-unit increase in AUC-Cp0.5h was associated with a 31.7% reduction in the risk of unstable glucose homeostasis (CV > 36%; P = 0.036; odds ratio 0.683; 95% confidence interval 0.478-0.976). We also identified the AUC-Cp0.5h (0.735 ng/mL) and AUC-Cp3h (13.355 ng/mL) cut-off values for predicting unstable glucose homeostasis (CV >36%) in T2DM subjects. CONCLUSION: Our study suggests that impaired pancreatic ß-cell function may be a clinical determining factor of CV of glucose in people with T2DM.

Characterization of a Novel Carbapenem-Resistant Klebsiella michiganensis Strain Coharboring the blaSIM-1, blaOXA-1, blaCTX-M-14, qnrS, and aac(6')-Ib-cr Genes.

Carbapenem-resistant Klebsiella michiganensis (CRKM) and Klebsiella oxytoca (CRKO) strains have occasionally been reported to cause severe infections. However, SIM-producing K. michiganensis strains have never been described. In this study, we phenotypically and genetically characterized 6 CRKM and CRKO strains isolated over the past 10 years at a Chinese tertiary hospital. All six strains were positive for the mCIM test, and five were positive for the MBL test. Carbapenemase-encoding genes (blaKPC, blaNDM, blaVIM, blaIMP, blaOXA-23, blaOXA-24, blaOXA-51, and blaOXA-58) and another 12 resistance genes were screened by PCR, and blaKPC, blaNDM, and blaIMP were identified in five strains. However, the CRKM strain KM41, which was resistant to IPM and MEM with minimum inhibitory concentrations (MICs) of 4 µg/ml and 16 µg/ml, respectively, had positive mCIM and MBL results but lacked the eight carbapenemase-encoding genes. Whole-genome sequencing of the KM41 strain revealed more than 20 drug resistance genes; in particular, blaSIM-1, blaOXA-1, blaCTX-M-14, qnrS, aac(6')-Ib-cr, aadA17, and aar-3 were found to be located in a single plasmid. To the best of our knowledge, this is the first description of a K. michiganensis strain coharboring blaSIM-1, blaOXA-1, blaCTX-M-14, qnrS, and aac(6')-Ib-cr in China.

Dispersion quality of single-walled carbon nanotubes reveals the recognition sequence of DNA.

The specific recognition between DNA and single-walled carbon nanotubes (SWCNTs) has enabled wide applications, especially in the chiral sorting of SWCNTs. However, the molecular recognition mechanism has not been fully understood. In our work, various DNA-SWCNT dispersions were prepared by the ultrasonic dispersion method, and characterized by UV absorption spectroscopy, fluorescence emission spectroscopy, zeta potential measurement, SDBS exchange kinetics and computer simulation. The effect of DNA sequence on the structure and properties of hybrid molecules was analyzed. Data analysis showed that DNA with specific recognition had better dispersion quality of the corresponding SWCNT, which means that higher content of monodispersed SWCNTs was obtained. The high-quality dispersion of the DNA-SWCNT pair was attributed to the stronger binding between DNA and SWCNT, resulting in a tighter conformation of DNA on the SWCNT surface and a larger zeta potential of DNA-SWCNT hybrids. Consequently, DNA-SWCNT dispersions of the recognition pair exhibited better stability against salt and stronger fluorescence emission intensity. However, the correlation between specific recognition and DNA coverage on SWCNT was not observed. This work gives more insight into the recognition mechanism between DNA and SWCNTs.

Risky riding: Naturalistic methods comparing safety behavior from conventional bicycle riders and electric bike riders.

As electric bicycles (e-bikes) have emerged as a new transportation mode, their role in transportation systems and their impact on users have become important issues for policy makers and engineers. Little safety-related research has been conducted in North America or Europe because of their relatively small numbers. This work describes the results of a naturalistic GPS-based safety study between regular bicycle (i.e., standard bicycle) and e-bike riders in the context of a unique bikesharing system that allows comparisons between instrumented bike technologies. We focus on rider safety behavior under four situations: (1) riding in the correct direction on directional roadway segments, (2) speed on on-road and shared use paths, (3) stopping behavior at stop-controlled intersections, and (4) stopping behavior at signalized intersections. We find that, with few exceptions, riders of e-bike behave very similarly to riders of bicycles. Violation rates were very high for both vehicles. Riders of regular bicycles and e-bikes both ride wrong-way on 45% and 44% of segments, respectively. We find that average on-road speeds of e-bike riders (13.3kph) were higher than regular bicyclists (10.4kph) but shared use path (greenway) speeds of e-bike riders (11.0kph) were lower than regular bicyclists (12.6kph); both significantly different at >95% confidence. At stop control intersections, both bicycle and e-bike riders violate the stop signs at the similar rate with bicycles violating stop signs at a slightly higher rate at low speed thresholds (∼80% violations at 6kph, 40% violations at 11kph). Bicycles and e-bikes violate traffic signals at similar rates (70% violation rate). These findings suggest that, among the same population of users, e-bike riders exhibit nearly identical safety behavior as regular bike riders and should be regulated in similar ways. Users of both technologies have very high violation rates of traffic control devices and interventions should occur to improve compliance.