Virulence associated characteristics and drug resistance of Klebsiella pneumoniae isolated from patients living in Zhongjiang county of Sichuan province / 中国基层医药

Objective:To investigate the distribution characteristics of virulence-related phenotypes/genotype, capsular serotype, drug resistance phenotypes, and sequence typing (ST) of Klebsiella pneumoniae in patients living in Zhongjiang county, improve clinical understanding, and provide evidence for the prevention and control of bacterial drug resistance and clinical rational drug use. Methods:The data of 135 strains of Klebsiella pneumoniae isolated from patients who received treatment in Zhongjiang County People's Hospital from July to December 2019 were retrospectively analyzed. Bacterial identification and drug sensitivity testing were performed using the WalkAway-40Plus automated microbiology system. Strains with a high viscosity phenotype were identified using wire drawing experiments. Hypervirulence-associated capsular serotype and virulence genes were verified by polymerase chain reaction. ST of Klebsiella pneumoniae strain was identified using multilocus sequence typing. Results:Strains with a high viscosity phenotype were identified in 50.4% of the 135 strains. 54.1%, 54.8%, and 54.1% of the strains were positive for virulence genes iucA, iroN, rmpA. The proportion of strains with capsular Serotype K1 or K2 was 11.9% and 15.6%, respectively. A total of 65 kinds of ST were identified, with ST23 and ST37 being the most common, accounting for 11.1% and 6.7%, respectively. The resistance rate of the strains to 16 kinds of antibiotics was 0.0%-25.2%, and the resistance rate to Carbapenem antibiotics, Amikacin, and Tigecycline was less than 1%. The positive rate of virulence gene of strains with a high viscosity phenotype was significantly higher than that of strains without a high viscosity phenotype ( P < 0.001), and its resistance rate to Cephalosporin was significantly lower in strains with a high viscosity phenotype than that in strains without a high viscosity phenotype ( P < 0.001). Conclusion:Klebsiella pneumoniae in Zhongjiang County is characterized by "high virulence and low drug resistance". It is necessary to continuously monitor the changes in the virulence and drug resistance of Klebsiella pneumoniae in Zhongjiang County, Sichuan Province, and be alert to the rapid dissemination of highly virulent strains.

In-depth analysis of ultrasonic-induced geological pore re-structures.

Understanding and manipulating geological pore structures is of paramount importance for geo-energy productions and underground energy storages in porous media. Nevertheless, research emphases for long time have been focused on understanding the pore configurations, while few work conducted to modify and restructure the porous media. This study deploys ultrasonic treatments on typical geological in-situ core samples, with follow-up processes of high-pressure mercury injections and nitrogen adsorptions and interpretations from nuclear magnetic resonance and x-ray diffraction. The core permeability and porosity are found to increase by 8.3 mD, from 4.1 to 12.4 mD, and by 0.95%, from 14.03% to 14.98%, respectively. Meanwhile, the number and size of the micro- and mesopore are increased with progressing of ultrasonic treatment, while those of the macropore decrease, which finally increase the permeability and porosity. The increase of micro- and mesopore number, from x-ray diffraction results, is attributed to the migration and precipitation of clay minerals caused through ultrasonic wave. The relocation of clay minerals also helps to improve the pore-throat connectivity and modify the micro-scale heterogeneity. Basically, this study reveals the characterizations of geological pore reconfigurations post-ultrasonic treatments and interprets the associated mechanisms, which provides guidance to manipulate the geological pores and be of benefit for further porous media use in science and engineering.

Characterization of Ultrasonic-Induced Wettability Alteration under Subsurface Conditions.

Understanding and manipulating wettability alterations has tremendous implications in theoretical research and industrial applications. This study proposes a novel idea of applying ultrasonic for wettability alterations and also provides its quantitative characterizations and in-depth analyses. More specifically, with pretreatment of ultrasonic, mechanisms of wettability alteration were characterized from the contact angle measurements, as well as the in-depth analyses from atomic force microscopy (AFM), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR). After ultrasonic treatments, the wettability of mineral with low permeability is determined to altered from strong hydrophilic to intermediate wettability. The mechanism interpretations are conducted by means of the AFM, XRD, and FTIR. Basically, as the time of ultrasonic treatment increases, the AFM results indicate that the roughness of rock surface and oil/rock interface (contact area) with surroundings of brine is enhanced. Meanwhile, the XRD results show the diffusions of clays from the rock surface to the aqueous phase, and FTIR indicates that the number of functional groups of Si-O-Si, C-O-C, C-O, C═O, and OH decreases while the number of COOH and C═C═O groups increases. This study clearly reveals the surface chemistry of oil-rock wettability alteration in the subsurface conditions, which would provide technical support for subsurface usage of geo-energy productions and carbon sequestrations.

Support Vector Regression Based on the Particle Swarm Optimization Algorithm for Tight Oil Recovery Prediction.

Tight oil fields are affected by factors such as geology, technology, and development, so it is difficult to directly obtain an accurate recovery rate. The accurate prediction of the recovery rate is very important for measuring reservoir development effects and dynamic analysis. Traditional tight oil recovery predictions are obtained by conventional formula calculations and curve fitting, which are less applicable and very different from actual conditions. Machine learning can make accurate predictions based on a large amount of data, so it is used to predict the recovery rate of tight oil reservoirs. The recovery rate of 200 wells in M tight oil reservoirs ranges widely between 8.8 and 27.6%, with more than 14 factors affecting the recovery rate, and the overall declining rule is not clear. Therefore, this research combines the production data of horizontal wells with random forest, support vector regression (SVR), and other methods, establishing recovery prediction models to gain more accurate recovery predictions. First, the Pearson correlation coefficient and the random forest (RF) machine learning method are used to measure and calculate the degree of nonlinear influence of factors on oil well recovery. Second, SVR and optimization of support vector regression by particle swarm (PSO-SVR) recovery prediction models are developed and tested, with 75% of the data being used to train SVR and PSO-SVR recovery prediction models and 25% to verify the model. Third, the accuracy of the results of these two SVR oil recovery prediction models is compared, suggesting that when the data are scarce, the optimized model is more accurate than the unoptimized one by 10.85%. Thus, this model can assure a relatively more accurate prediction of oil recovery. Machine learning recovery prediction, being more accurate and applicable, enables the data of factors such as construction and production systems to be optimized in the future, enhancing the oil recovery rate.

Integrated Hierarchy-Correlation Model for Evaluating Water-Driven Oil Reservoirs.

With the increasing demands on energy and environmental domains, not only high oil production but also its accurate quantification has become one of the most important topics in academia and industry. This paper initially proposes a comprehensive workflow in which an integrated hierarchy-correlation model is used to thoroughly evaluate the influences of all relevant reservoir parameters on the ultimate oil recovery for water-flooding oil reservoirs. More specifically, the analytic hierarchy process, grey relation, and entropy weight are combined through the multiplicative weighting method to quantitatively describe the production parameters. Accordingly, novel multivariable linear and nonlinear correlations are developed to predict the production performance and validated through comparisons with numerical reservoir simulations. Seven factors, including five reservoir parameters, namely, permeability and its contrast, porosity, thickness, and saturation, and two production parameters, namely, the injection-production ratio and the operating pressure, have been identified as the most influential factors on recovery performances and thus are employed in the proposed correlations to predict the ultimate oil recovery factor. The results obtained by the proposed method are quite close to the real-time simulation data, while the accuracy is retained. The numerical results show that the recovery factors of water-flooding oil reservoirs are about 33.5-59.5%, and the corresponding linear and nonlinear correlation coefficients are 0.903 and 0.789, respectively. In comparison with the numerical simulation, the approximation error by the linear correlation is about 0.5%, which is lower than that of nonlinear correlation, for example, 12.3%. This study will be beneficial to analyze the reservoir-related parameters and provide a useful tool for rapid production performance evaluation of the water-flooding production scenario.