Detection of VIM-1 and IMP-1 genes in Klebsiella pneumoniae and relationship with biofilm formation

Klebsiella pneumoniae is an important human pathogen that is considered in recent years due to nosocomial infections resistant to treatment as well as the ability to form biofilms particularly in patients with urinary tract infection in ICU or hospital. The aim of this study was to evaluate the prevalence of VIM1, IMP1 genes and their ability to form biofilm in K. pneumoniae strains isolated from patients with urinary tract infection. In the study, using culture and biochemical methods, 1807 K. pneumoniae samples were isolated from patients with urinary tract infection hospitalized or referred to hospitals in Qom in 2013–2014. For isolation of MBL producing isolates, Double Disk Synergy Test (DDST) was used. Then MBL positive isolates were examined for the presence of VIM1, IMP1 genes using PCR method. Furthermore, all strains were investigated for biofilm formation by phenotypic microplate method. From 3165 urine samples cultured, 1807 isolates of K. pneumoniae were isolated and 109 strains (93.2%) were positive for MBL enzymes production. PCR results showed that the prevalence of VIM1 and IMP1 genes are 15.6 and 6.4%, respectively. The Phenotypic method indicated that 91.2% of isolates formed biofilm. Biofilm formation in K. pneumoniae isolates is high and there is a significant relationship between strong biofilm formation and prevalence of VIM1 and IMP1 genes. Also due to the presence of MBL genes in K. pneumoniae and horizontal transfer of genes to other bacteria, and to control the indiscriminate use of antibiotics, the hospital infection control methods must be considered

Prevalence of metallo-beta-lactamase enzyme and patern of antibiotic resistance in Klebsiella pnomoniae isolated from patients with urinary tract infection in Qom city during 2013-2014

Bachground: Klebsiella pnomoniae is one of the most important etiologic agents of urinary tract infection (UTI). An increasing occurrence of antimicrobial resistance among uropathogenic bacterial isolates has complicated the treatment process. The aim of this study was to determine antibiotic susceptibility patterns and prevalence of the metallo-beta-lactamase enzyme of K. pneumoniae isolates collected from UTI. Materials and Methods: This cross-sectional study was conducted on patients with complicated UTI reffered to hospitals in Qom city, Iran. A total of 1807 culture positive samples of pathogens were collected from the patients, among which 457 isolates were K. pneumoniae. The isolates were tested for antimicrobial susceptibility by the disc-diffusion method recommended by the guidelines of Clinical and Laboratory Standards Institute (CLSI 2013). In addition, the dubble disk synergy test was used to detect the K. pneumoniae isolates of metallo-beta-lactamase enzyme. Results: The prevalence of UTI infection due to K. pneumoniae was 25.3. Among 1807 positive urine cultures, 62.4 were from females and 37.6 from males. Results of antimicrobial susceptibility showed that the highest antibiotic resistance was seen for trimetoprium-sulfametoxazole (98.5) and the lowest resistance levels were seen for amikacin (9.4), meropenem (22.8) and imipenem (25.6). The results of the imipenem-EDTA combined disk showed that 93.2 imipenem resistance isolates were positive for the metallo-beta- lactamase enzyme. Conclusion: Carbapenem resistance and production of the metallo-beta-lactamase enzyme in K. pneumoniae uropathogenic are increasing. However, amikacin is still effective against these bacterial infections and its effectiveness should be maintained

Testicular expression of TDRD1, TDRD5, TDRD9 and TDRD12 in azoospermia.

BACKGROUND: Tudor domain-containing proteins (TDRDs) play a critical role in piRNA biogenesis and germ cell development. piRNAs, small regulatory RNAs, act by silencing of transposons during germline development and it has recently been shown in animal model studies that defects in TDRD genes can lead to sterility in males. METHODS: Here we evaluate gene and protein expression levels of four key TDRDs (TDRD1, TDRD5, TDRD9 and TDRD12) in testicular biopsy samples obtained from men with obstructive azoospermia (OA, n = 29), as controls, and various types of non-obstructive azoospermia containing hypospermatogenesis (HP, 28), maturation arrest (MA, n = 30), and Sertoli cell-only syndrome (SCOS, n = 32) as cases. One-way ANOVA test followed by Dunnett's multiple comparison post-test was used to determine inter-group differences in TDRD gene expression among cases and controls. RESULTS: The results showed very low expression of TDRD genes in SCOS specimens. Also, the expression of TDRD1 and TDRD9 genes were lower in MA samples compared to OA samples. The expression of TDRD5 significantly reduced in SCOS, MA and HP specimens than the OA specimens. Indeed, TDRD12 exhibited a very low expression in HP specimens in comparison to OA specimens. All these results were confirmed by Western blot technique. CONCLUSION: TDRDs could be very important in male infertility, which should be express in certain stages of spermatogenesis

Sensor management for multi-target tracking using random finite sets

Sensor management in multi-target tracking is commonly focused on actively scheduling and managing sensor resources to maximize the visibility of states of a set of maneuvering targets in a surveillance area. This project focuses on two types of sensor management techniques: - controlling a set of mobile sensors (sensor control), and - scheduling the resources of a sensor network (sensor selection).​ In both cases, agile sensors are employed to track an unknown number of targets. We advocate a Random Finite Set (RFS)-based approach for formulation of a sensor control/selection technique for multi-target tracking problem. Sensor control/scheduling offers a multi-target state estimate that is expected to be substantially more accurate than the classical tracking methods without sensor management. Searching for optimal sensor state or command in the relevant space is carried out by a decision-making mechanism based on maximizing the utility of receiving measurements.​ In current solutions of sensor management problem, the information of the clutter rate and uncertainty in sensor Field of View (FoV) are assumed to be known in priori. However, accurate measures of these parameters are usually not available in practical situations. This project presents a new sensor management solution that is designed to work within a RFS-based multi-target tracking framework. Our solution does not require any prior knowledge of the clutter distribution nor the probability of detection profile to achieve similar accuracy. Also, we present a new sensor management method for multi-object filtering via maximizing the state estimation confidence. Confidence of an estimation is quantified by measuring the dispersion of the multi-object posterior about its statistical mean using Optimal Sub-Pattern Assignment (OSPA). The proposed method is generic and the presented algorithm can be used with any statistical filter

Detection of VIM-1 and IMP-1 genes in Klebsiella pneumoniae and relationship with biofilm formation

Klebsiella pneumoniae is an important human pathogen that is considered in recent years due to nosocomial infections resistant to treatmentas well as the ability to form biofilms particularly in patients with urinary tract infection in ICU or hospital. The aim of this study was to evaluate the prevalence of VIM1, IMP1 genes and their ability to form biofilm in K. pneumoniae strains isolated from patients with urinary tract infection. In the study, using culture and biochemical methods, 1807 K. pneumoniae samples were isolated from patients with urinary tract infection hospitalized or referred to hospitals in Qom in 2013�2014. For isolation of MBL producing isolates, Double Disk Synergy Test (DDST) was used. Then MBL positive isolates were examined for the presence of VIM1, IMP1 genes using PCR method. Furthermore, all strains were investigated for biofilm formation by phenotypic microplate method. From 3165 urine samples cultured, 1807 isolates of K. pneumoniae were isolated and 109 strains (93.2) were positive for MBL enzymes production. PCR results showed that the prevalence of VIM1 and IMP1 genes are 15.6 and 6.4, respectively. The Phenotypic method indicated that 91.2 of isolates formed biofilm. Biofilm formation in K. pneumoniae isolates is high and there is a significant relationship between strong biofilm formation and prevalence of VIM1 and IMP1 genes. Also due to the presence of MBL genes in K. pneumoniae and horizontal transfer of genes to other bacteria, and to control the indiscriminate use of antibiotics, the hospital infection control methods must be considered. © 201

Robust multi-bernoulli sensor selection for multi-target tracking in sensor networks

This letter addresses the sensor selection problem for tracking multiple dynamic targets within a sensor network. Since the bandwidth and energy of the sensor network are constrained, it would not be feasible to directly use the entire information of sensor nodes for detection and tracking of the targets and hence the need for sensor selection. Our sensor selection solution is formulated using the multi-Bernoulli random finite set framework. The proposed method selects a minimum subset of sensors which are most likely to provide reliable measurements. The overall scheme is a robust method that works in challenging scenarios where no prior information are available on clutter intensity or sensor detection profile. Simulation results demonstrate successful sensor selection in a challenging case where five targets move in a close vicinity to each other. Comparative results show the superior performance of our method in terms of accuracy of estimating the number of targets and their states

Multi-Bernoulli sensor selection for multi-target tracking with unknown clutter and detection profiles

A new sensor-selection solution within a multi-Bernoulli-based multi-target tracking framework is presented. The proposed method is especially designed for the general multi-target tracking case with no prior knowledge of the clutter distribution or the probability of detection, and uses a new task-driven objective function for this purpose. Step-by-step sequential Monte Carlo implementation of the method is presented along with a similar sensor-selection solution formulated using an information-driven objective function (Rényi divergence). The two solutions are compared in a challenging scenario and the results show that while both methods perform similarly in terms of accuracy of cardinality and state estimates, the task-driven sensor-selection method is substantially faster

A novel task-driven sensor-management method in multi-object filters using stochastic geometry

Multi-object estimation refers to applications where there are unknown number of objects with unknown states, and the problem is to estimate both the number of objects and their individual state vectors, from observations acquired by sensors. The solution is usually called a multi-object filter. In many modern complex systems, multi-object estimation is one of the most challenging problems to be solved for satisfactory performance of the dedicated tasks by the system. A wide range of practical applications involve multi-object estimation, from multi-target tracking in radar to visual tracking in sport, to cell tracking in biomedicine, to data clustering in big data analytics. In the past decade, a new generation of multi-object filters has been developed and rapidly adopted by researchers in various fields, that is based on using stochastic geometric models and approximations. In such methods, the multi-object entity is treated as a random finite set (RFS) variable (with random variations in its cardinality and elements), and the stochastic geometric-based notions of density and integration, developed in the new theory of finite set statistics (FISST), are used to formulate Bayesian filters for estimation of cardinality (number of objects) and state of the multi-object RFS variable. This chapter reviews the most recent developments in sensor management (control or selection) solutions devised for multi-Bernoulli solutions in various applications. It first presents basics of random set theory and formulation of the cardinality-balanced and labeled multi-Bernoulli filters. The most recent sensor-control and sensor-selection solutions that have been proposed by the authors and other researchers active in the field are then presented and comparative simulation results are discussed

Constrained sensor control for labeled multi-bernoulli filter using Cauchy-Schwarz divergence

A constrained sensor control method is presented for multi-object tracking using labeled multi-Bernoulli filters. The proposed framework is based on a novel approximation of the Cauchy-Schwarz divergence between the labeled multi-Bernoulli prior and posterior densities, which does not need Monte Carlo sampling of random sets in the multi-object space. The void probability functional is also formulated for labeled multi- Bernoulli distributions and used within our proposed method to form a constrained sensor control solution. Numerical studies demonstrate that reasonably acceptable movements are decided for the controlled sensor by our sensor control method, with the advantage that the void probability constraint is formally considered as part of the sensor control optimization algorithm