Doped dialkylated phenazines : a novel series of highly conducting organic solids

Doping dimethylphenazine (M2P)-TCNQ and diethylphenazine (E2P)-TCNQ with phenazine gave rise to new conducting compounds with segregated stacks : (M2P)0.5 (P)0,5 TCNQ and (E2P)0,55 (P)0,45 TCNQ. The room temperature electrical conductivities are in the range 10-100 Ω-1 cm-1 for (1) and 1-10 Ω-1cm-1 for (2). The paramagnetism of both compounds could be separated into contributions from TCNQ and Phenazine stacks. The structural electrical and magnetic similarities of (1) with the well-known N-methylphenazinium (NMP)-TCNQ led to a reexamination of its magnetic properties. It was shown that an estimate of the charge transfer can be obtained from the temperature dependence of the magnetic susceptibility

Trends of Antibacterial Resistance at the National Reference Laboratory in Cameroon: Comparison of the Situation between 2010 and 2017

INTRODUCTION: Antimicrobial resistance represents a growing public health threat. One of the World Health Organization's strategic objectives is "strengthening knowledge through surveillance and research." Sub-Saharan African countries are still far from achieving this objective. We aimed to estimate and compare the prevalence of antibacterial resistance in 2010 and 2017 in Cameroon. METHODS: We conducted a retrospective study on all clinical specimens cultured in Centre Pasteur du Cameroun (CPC) in 2010 and 2017. Data were extracted from the CPC's laboratory data information system software and then managed and analyzed using R. Bacterial resistance rates were calculated in each year and compared using chi-square or Fisher's tests, and relative changes were calculated. Outcomes included acquired resistance (AR), WHO priority resistant pathogens, some specific resistances of clinical interest, and resistance patterns (multi, extensively, and pan drug resistances) for five selected pathogens. RESULTS: A total of 10,218 isolates were analyzed. The overall AR rate was 96.0% (95% CI: 95.4-96.6). Most of WHO priority bacterial resistance rates increased from 2010 to 2017. The most marked increases expressed as relative changes concerned imipenem-resistant Acinetobacter (6.2% vs. 21.6%, +248.4%, p = 0.02), imipenem-resistant Pseudomonas aeruginosa (13.5% vs. 23.5%, +74.1%, p < 0.01), 3rd generation-resistant Enterobacteriaceae (23.8% vs. 40.4%, +65.8%, p < 10(-15)), methicillin-resistant Staphylococcus aureus (27.3% vs. 46.0%, +68.6%, p < 0.002), fluoroquinolone-resistant Salmonella (3.9% vs. 9.5%, +142.9%, p = 0.03), and fluoroquinolone-resistant Enterobacteriaceae (32.6% vs. 54.0%, +65.8%, p < 10(-15)). For selected pathogens, global multidrug resistance was high in 2010 and 2017 (74.9% vs. 78.0% +4.1%, p = 0.01), intensively drug resistance rate was 5.8% (7.0% vs. 4.7%; p = 0.07), and no pan drug resistance has been identified. CONCLUSION: Bacterial resistance to antibiotics of clinical relevance in Cameroon was high and appeared to increase between 2010 and 2017. There is a need for regular surveillance of antibacterial resistance to inform public health strategies and empirically inform prescription practices

Rapid determination of anti-tuberculosis drug resistance from whole-genome sequences

Mycobacterium tuberculosis drug resistance (DR) challenges effective tuberculosis disease control. Current molecular tests examine limited numbers of mutations, and although whole genome sequencing approaches could fully characterise DR, data complexity has restricted their clinical application. A library (1,325 mutations) predictive of DR for 15 anti-tuberculosis drugs was compiled and validated for 11 of them using genomic-phenotypic data from 792 strains. A rapid online ‘TB-Profiler’ tool was developed to report DR and strain-type profiles directly from raw sequences. Using our DR mutation library, in silico diagnostic accuracy was superior to some commercial diagnostics and alternative databases. The library will facilitate sequence-based drug-susceptibility testing

Identification of Mycobacterium using the EF-Tu encoding (tuf) gene and the tmRNA encoding (ssrA) gene

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Mathematical model of the interactions between Micrococcus spp. and Pseudomonas aeruginosa on agar surface

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Early stages of in vitro killing curve of LY146032 and vancomycin for Staphylococcus aureus.

The early stages of the time-killing curves of vancomycin and LY146032 have been studied, by use of short sampling intervals, for three strains of Staphylococcus aureus. Both vancomycin and LY146032 killed S. aureus, but the time-killing curves differed: the effect of vancomycin was slow, limited, and not related to the concentration of the drug, whereas that of LY146032 was rapid, extensive, and related to concentration. When strains ATCC 25923 and CIP 6525 were exposed to LY146032, the population decreased exponentially with time. The killing rate was constant and linked to the concentration by a Michaelis-Menten relationship. The maximum killing rate and the affinity constant of LY146032, estimated from the data transformed by the Lineweaver-Burk method, differed for the two strains. The concentration of the antibiotic at which killing theoretically begins (estimated by linear regression using the logarithm of the concentration) is of the same magnitude as the MIC of LY146032, which indicates the pure bactericidal mode of action of the drug. S. aureus ATCC 12600 was more resistant to the bactericidal effect of the two drugs, and its killing curve did not conform to the model described here

Mathematical model for comparison of time-killing curves.

The relevance of mathematical modeling to investigations of the bactericidal effects of antimicrobial agents has been emphasized in many studies of killing kinetics. We propose here a descriptive model of general use, with four parameters which account for the lag phase, the initial number of bacteria, and the limit of effectiveness and bactericidal rate of antimicrobial agents. The model has been applied to several kinetic datum sets with amoxicillin, cephalothin, nalidixic acid, pefloxacin, and ofloxacin against two Escherichia coli strains. It is a useful tool to compare killing curves by taking into account model parameter confidence limits. This can be illustrated by studying drug effects, strain effects, and concentration effects. For the antibiotics used here, concentration effects had an influence mainly on the length of the lag phase and the minimum number of living cells observed. It is therefore clear that differences in the killing curves with changes in one or more parameters could occur