香芹酚联合头孢曲松对耐药性沙门氏菌的体外抗菌作

【背景】由于抗生素的滥用,使得细菌耐药性问题严峻,寻找解决耐药性细菌感染的治疗策略迫在眉睫。临床上,中药与抗菌药物联用在抗耐药性细菌感染方面效果显著。【目的】研究香芹酚联合头孢曲松对耐药性沙门氏菌(SJ2)的协同效应及机制。【方法】通过二倍肉汤稀释法测定香芹酚和头孢曲松对SJ2的最小抑菌浓度(minimal inhibitory concentration, MIC);通过棋盘法和生长曲线测定探究香芹酚和头孢曲松联合抗SJ2活性;通过膜电位检测、胞外碱性磷酸酶(alkaline phosphatase, AKP)含量测定、菌体内抗生素蓄积分析、细菌生存活力测定及扫描电镜研究香芹酚联合头孢曲松对SJ2细胞壁、细胞膜的影响。【结果】香芹酚和头孢曲松对SJ2的MIC分别为256μg/mL和2 048μg/mL;香芹酚联合头孢曲松对SJ2具有协同作用,其分级抑制浓度指数(fractional inhibitory concentration index, FICI)为0.375;香芹酚可协同头孢曲松使SJ2细胞膜出现去极化,显著增加AKP泄漏至胞外的量(P<0.05),显著增加菌体胞内抗...     

The importance of methylthio-IMP for methylmercaptopurine ribonucleoside (Me-MPR) cytotoxicity in Molt F4 human malignant T-lymphoblasts

The importance of methyl-thioIMP (Me-tIMP) formation for methylmercaptopurine ribonucleoside (Me-MPR) cytotoxicity was studied in Molt F4 cells. Cytotoxicity of Me-MPR is caused by Me-tIMP formation with concomitant inhibition of purine de novo synthesis. Inhibition of purine de novo synthesis resulted in decreased purine nucleotide levels and enhanced 5-phosphoribosyl-1-pyrophosphate (PRPP) levels, with concurrent increased pyrimidine nucleotide levels. The Me-tIMP concentration increased proportionally with the concentration of Me-MPR. High Me-tIMP concentration also caused inhibition of PRPP synthesis. Maximal accumulation of PRPP thus occurred at low Me-MPR concentrations. As little as 0.2 μM Me-MPR resulted already after 2 h in maximal inhibition of formation of adenine and guanine nucleotides, caused by inhibition of purine de novo synthesis by Me-tIMP. Under these circumstances increased intracellular PRPP concentrations could be demonstrated, resulting in increased levels of pyrimidine nucleotides. So, in Molt F4 cells, formation of Me-tIMP form Me-MPR results in cytotoxicity by inhibition of purine de novo synthesis.     

Molecular magnetic materials from polyoxometalates

The significance of polyoxometalates in the field of molecular magnetism is discussed. We show that this kind of inorganic complexes provides remarkable examples for the study of the exchange interactions in clusters. On the other hand, we examine the possibility of using these metal oxide anions as magnetic components of molecular materials containing organic tetrathiafulvalenes as electron donor molecules.     

Enhancing estimation methods for integrating probability and nonprobability survey samples with machine-learning techniques. An application to a Survey on the impact of the COVID-19 pandemic in Spain

Web surveys have replaced Face-to-Face and computer assisted telephone interviewing (CATI) as the main mode of data collection in most countries. This trend was reinforced as a consequence of COVID-19 pandemic-related restrictions. However, this mode still faces significant limitations in obtaining probability-based samples of the general population. For this reason, most web surveys rely on nonprobability survey designs. Whereas probability-based designs continue to be the gold standard in survey sampling, nonprobability web surveys may still prove useful in some situations. For instance, when small subpopulations are the group under study and probability sampling is unlikely to meet sample size requirements, complementing a small probability sample with a larger nonprobability one may improve the efficiency of the estimates. Nonprobability samples may also be designed as a mean for compensating for known biases in probability-based web survey samples by purposely targeting respondent profiles that tend to be underrepresented in these surveys. This is the case in the Survey on the impact of the COVID-19 pandemic in Spain (ESPACOV) that motivates this paper. In this paper, we propose a methodology for combining probability and nonprobability web-based survey samples with the help of machine-learning techniques. We then assess the efficiency of the resulting estimates by comparing them with other strategies that have been used before. Our simulation study and the application of the proposed estimation method to the second wave of the ESPACOV Survey allow us to conclude that this is the best option for reducing the biases observed in our data.     

A mechanistic spatio-temporal modeling of COVID-19 data

Understanding the evolution of an epidemic is essential to implement timely and efficient preventive measures. The availability of epidemiological data at a fine spatio-temporal scale is both novel and highly useful in this regard. Indeed, having geocoded data at the case level opens the door to analyze the spread of the disease on an individual basis, allowing the detection of specific outbreaks or, in general, of some interactions between cases that are not observable if aggregated data are used. Point processes are the natural tool to perform such analyses. We analyze a spatio-temporal point pattern of Coronavirus disease 2019 (COVID-19) cases detected in Valencia (Spain) during the first 11 months (February 2020 to January 2021) of the pandemic. In particular, we propose a mechanistic spatio-temporal model for the first-order intensity function of the point process. This model includes separate estimates of the overall temporal and spatial intensities of the model and a spatio-temporal interaction term. For the latter, while similar studies have considered different forms of this term solely based on the physical distances between the events, we have also incorporated mobility data to better capture the characteristics of human populations. The results suggest that there has only been a mild level of spatio-temporal interaction between cases in the study area, which to a large extent corresponds to people living in the same residential location. Extending our proposed model to larger areas could help us gain knowledge on the propagation of COVID-19 across cities with high mobility levels.     

Growth kinetics of Rhizopus arrhizus in solid state fermentation of treated cassava

Summary Growth kinetics of Rhizopus arrhizus MUCL 28168 were determined for different treatments of cassava during solid state fermentation. The best case gave a specific growth rate () of 0.24 h^-1, a yield calculated on a basis that oxygen consumption (Y_x/o) was 2.9 g biomass. g^-1 O₂ consumed and the maintenance coefficient (m) was 0.004 g O₂ consumed. g^-1 biomass. h^-1.     

Influence of seasonal variation to the population growth and ecophysiology of Typha domingensis (Typhaceae)

Journal of Plant Research - Precipitation is an important climatic element that defines the hydrological regime, and its seasonal variation produces annual dry and wet periods in some areas. This...