Potential strategies for the eradication of multi-drug resistant Gram-negative bacterial infections

Antimicrobial resistance is one of the leading threats to society. The increasing burden of multidrug-resistant Gram-negative infection is particularly concerning as such bacteria are demonstrating resistance to nearly all currently licensed therapies. Various strategies have been hypothesized to treat multidrug-resistant Gram-negative infections including: targeting the Gram-negative outer membrane; neutralization of lipopolysaccharide; inhibition of bacterial efflux pumps and prevention of protein folding. Silver and silver nanoparticles, fusogenic liposomes and nanotubes are potential strategies for extending the activity of licensed, Gram-positive selective, antibiotics to Gram-negatives. This may serve as a strategy to fill the current void in pharmaceutical development in the short term. This review outlines the most promising strategies that could be implemented to solve the threat of multidrug-resistant Gram-negative infections

Development of polymeric materials to inhibit bacterial quorum sensing

Bacterial infections are an increasing problem for human health. In fact, an increasing number of infections are caused by bacteria that are resistant to most antibiotics and their combinations. Therefore, the scientific community is currently searching for new solutions to fight bacteria and infectious diseases, without promoting antimicrobial resistance. One of the most promising strategies is the disruption or attenuation of bacterial Quorum Sensing (QS), a refined system that bacteria use to communicate. In a QS event, bacteria produce and release specific small chemicals, signal molecules - autoinducers (AIs) - into the environment. At the same time that bacterial population grows, the concentration of AIs in the bacterial environment increases. When a threshold concentration of AIs is reached, bacterial cells respond to it by altering their gene expression profile. AIs regulate gene expression as a function of cell population density. Phenotypes mediated by QS (QSphenotypes) include virulence factors, toxin production, antibiotic resistance and biofilm formation. In this work, two polymeric materials (linear polymers and molecularly imprinted nanoparticles) were developed and their ability to attenuate QS was evaluated. Both types of polymers should to be able to adsorb bacterial signal molecules, limiting their availability in the extracellular environment, with expected disruption of QS. Linear polymers were composed by one of two monomers (itaconic acid and methacrylic acid), which are known to possess strong interactions with the bacterial signal molecules. Molecularly imprinted polymer nanoparticles (MIP NPs) are particles with recognition capabilities for the analyte of interest. This ability is attained by including the target analyte at the synthesis stage. Vibrio fischeri and Aeromonas hydrophila were used as model species for the study. Both the linear polymers and MIP NPs, tested free in solutions and coated to surfaces, showed ability to disrupt QS by decreasing bioluminescence of V. fischeri and biofilm formation of A. hydrophila. No significant effect on bacterial growth was detected. The cytotoxicity of the two types of polymers to a fibroblast-like cell line (Vero cells) was also tested in order to evaluate their safety. The results showed that both the linear polymers and MIP NPs were not cytotoxic in the testing conditions. In conclusion, the results reported in this thesis, show that the polymers developed are a promising strategy to disrupt QS and reduce bacterial infection and resistance. In addition, due to their low toxicity, solubility and easy integration by surface coating, the polymers have potential for applications in scenarios where bacterial infection is a problem: medicine, pharmaceutical, food industry and in agriculture or aquaculture.

Development of infrared spectroscopy for assessing bacterial quality in foods

Rapid and specific detection of foodborne bacteria that can cause food spoilage or illness associated to its consumption is an increasingly important task in food industry. Bacterial detection, identification, and classification are generally performed using traditional methods based on biochemical or serological tests and the molecular methods based on DNA or RNA fingerprints. However, these methodologies are expensive, time consuming and laborious. Infrared spectroscopy is a reliable, rapid, and economic technique which could be explored as a tool for bacterial analysis in the food industry. In this thesis it was evaluated the potential of IR spectroscopy to study the bacterial quality of foods. In Chapter 2, it was developed a calibration model that successfully allowed to predict the bacterial concentration of naturally contaminated cooked ham samples kept at refrigeration temperature during 8 days. In this part, it was developed the methodology that allowed the best reproducibility of spectra from bacteria colonies with minimal sample preparation, which was used in the subsequent work. Several attempts trying different resolutions and number of scans in the IR were made. A spectral resolution of 4 cm-1, with 32 scans were the settings that allowed the best results. Subsequently, in Chapter 3, it was made an attempt to identify 22 different foodborne bacterial genera/species using IR spectroscopy coupled with multivariate analysis. The principal component analysis, used as an exploratory technique, allowed to form distinct groups, each one corresponding to a different genus, in most of the cases. Then, a hierarchical cluster analysis was performed to further analyse the group formation and the possibility of distinction between species of the same bacterial genus. It was observed that IR spectroscopy not only is suitable to the distinction of the different genera, but also to differentiate species of the same genus, with the simultaneous use of principal component analysis and cluster analysis techniques. The utilization of IR spectroscopy and multivariate statistical analysis were also investigated in Chapter 4, in order to confirm the presence of Listeria monocytogenes and Salmonella spp. isolated from contaminated foods, after growth in selective medium. This would allow to substitute the traditional biochemical and serological methods that are used to confirm these pathogens and that delay the obtainment of the results up to 2 days. The obtained results allowed the distinction of 3 different Listeria species and the distinction of Salmonella spp. from other bacteria that can be mistaken with them. Finally, in chapter 5, high pressure processing, an emerging methodology that permits to produce microbiologically safe foods and extend their shelf-life, was applied to 12 foodborne bacteria to determine their resistance and the effects of pressure in cells. A treatment of 300 MPa, during 15 minutes at room temperature was applied. Gram-negative bacteria were inactivated to undetectable levels and Gram-positive showed different resistances. Bacillus cereus and Staphylococcus aureus decreased only 2 logs and Listeria innocua decreased about 5 logs. IR spectroscopy was performed in bacterial colonies before and after HPP in order to investigate the alterations of the cellular compounds. It was found that high pressure alters bands assigned to some cellular components as proteins, lipids, oligopolysaccharides, phosphate groups from the cell wall and nucleic acids, suggesting disruption of the cell envelopes. In this work, bacterial quantification and classification, as well as assessment of cellular compounds modification with high pressure processing were successfully performed. Taking this into account, it was showed that IR spectroscopy is a very promising technique to analyse bacteria in a simple and inexpensive manner.

Genetic programming and bacterial algorithm for neural networks and fuzzy systems design

In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of results given by two different biologically connected learning algorithms for the design of B-spline neural networks (BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP) for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the GP algorithm is outlined, enabling the designer to obtain models more adequate for their intended use.

Incipient turn-to-turn winding fault diagnosis of power transformers by the on-load exciting current Extended Park`s Vector Approach

This paper presents the application of the on-load exciting current Extended Park's Vector Approach for diagnosing incipient turn-to-turn winding faults in operating power transformers. Experimental and simulated test results demonstrate the effectiveness of the proposed technique, which is based on the spectral analysis of the AC component of the on-load exciting current Park's Vector modulus.

Transformers on-load exciting current Park`s vector approach as a tool for winding faults diagnostics

This paper presents the development of a new approach for diagnosing the occurrence of inter-turn short-circuits in the windings of three-phase transformers, which is based on the on-line monitoring of the on-load exciting current Park's Vector patterns. Experimental and simulated results demonstrate the effectiveness of the proposed technique for detecting winding inter-turn insulation faults in operating three-phase transformers.

Genetic and bacterial programming for B-Spline neural networks design

The design phase of B-spline neural networks is a highly computationally complex task. Existent heuristics have been found to be highly dependent on the initial conditions employed. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this paper, the Bacterial Programming approach is presented, which is based on the replication of the microbial evolution phenomenon. This technique produces an efficient topology search, obtaining additionally more consistent solutions.

Detection of trasformer intermittent winding faults by the on-load exciting current Park`s vector approach

This paper presents the application of the on-load exciting current Park's Vector Approach for diagnosing permanent and intermittent turn-to-turn winding faults in operating power transformers. First, an experimental investigation of the behaviour of the transformer under the occurrence of both permanent and intermittent winding faults is presented. Finally, experimental test results demonstrate the effectiveness of the proposed diagnostic technique, which is based on the on-line monitoring of the on-load exciting current Park's Vector patterns.

Design of B-spline neural networks using a bacterial programming approach

The design phase of B-spline neural networks represents a very high computational task. For this purpose, heuristics have been developed, but have been shown to be dependent on the initial conditions employed. In this paper a new technique, Bacterial Programming, is proposed, whose principles are based on the replication of the microbial evolution phenomenon. The performance of this approach is illustrated and compared with existing alternatives.

EU water framework directive: will the nitrate load reduction from diffuse sources produce the same results in all estuaries?

The impact of urban waste-water and non-point nitrate discharges in estuarine and near-shore coastal waters are analyzed. The study is focused on the effects of applying the European directives 91/271/EEC and 91/676/EEC to these systems. 4 Portuguese estuaries and two coastal lagoons with different characteristics are studied. A modelling system is applied and calibrated in each system. Three nitrate load scenarios are examined. It is shown that the morphologic and hydrodynamic characteristics of the domain largely control the ecological processes in these systems. The primary production limitation factors are split into “biologic” and “hydrodynamic” components. The physical limitation due to hydrodynamic and residence time is the most important factor. The combined limitation of “biologic” factors (temperature, light and nutrients availability) control productivity only in the systems where physical limitation is not important.

Bacterial algorithm applied for fuzzy rule extraction

This paper presents a method of using the so-colled "bacterial algorithm" (4,5) for extracting a fuzzy rule base from a training set. The bewly proposed bacterial evolutionary algorithm (BEA) is shown. In our application one bacterium corresponds to a fuzzy rule system.

Bacterial peroxide forming enzymes

Dissertação de mestrado, Engenharia Biológica, Faculdade de Ciências e Tecnologia, Universidade do Algarve; Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, 2015