Effect of electric current in viability, biofilm formation and antibiotic resistance of Pseudomonas aeruginosa: A systematic review.

BACKGROUND: The bactericidal effect of electric current has been studied in various microorganisms such as Pseudomonas aeruginosa. The objective of this review is to identify the experimental parameters with the greatest antibacterial effect in the shortest time. METHODS: Literature search was conducted in the databases PubMed, Science Direct, and Google Scholar. Only original articles published between 2014 and 2023 were included, where the effect of electric current on viability, biofilm formation, and/or antibiotic resistance in P. aeruginosa was analyzed. Quality control criteria considered included specifying control and experimental groups, replicates performed, experimental parameters, and study limitations. RESULTS: Ten studies were included, which involved the strains Xen5, Xen41, PAO1 persistent cells, and PA14. An average reduction of 3.5 log in biofilm formation was observed in the included studies. The electric current parameters that achieved the greatest effect were 500 µA DC with platinum electrodes for 4 days [5.2-5.5 log], 200 µA intermittent with titanium electrodes for 4 days [4.99 log], and 150 ± 60 µA with silver electrodes for 24 h [4 log]. Complete eradication of PAO1 persistent cells was achieved in 1 h with a treatment of 70 µA/cm2 DC followed by 1.5 µg/mL tobramycin for 1 h each. CONCLUSIONS: The bactericidal effect of electric current is proportional to the exposure time and current intensity. The electrode material influences the effectiveness of the treatment, possibly because of redox reactions, while differences are observed in the effect on the cell membrane and gene expression when using metallic or carbon electrodes, suggesting differences in the mechanism of action.

MolGC: molecular geometry comparator algorithm for bond length mean absolute error computation on molecules.

Density Functional Theory (DFT) is extensively used in theoretical and computational chemistry to study molecular and crystal properties across diverse fields, including quantum chemistry, materials physics, catalysis, biochemistry, and surface science. Despite advances in DFT hardware and software for optimized geometries, achieving consensus in molecular structure comparisons with experimental counterparts remains a challenge. This difficulty is exacerbated by the lack of automated bond length comparison tools, resulting in labor-intensive and error-prone manual processes. To address these challenges, we propose MolGC, a Molecular Geometry Comparator algorithm that automates the comparison of optimized geometries from different theoretical levels. MolGC calculates the mean absolute error (MAE) of bond lengths by integrating data from various DFT software. It provides interactive and customizable visualization of geometries, enabling users to explore different views for enhanced analysis. In addition, it saves MAE computations for further analysis and offers a comprehensive statistical summary of the results. MolGC effectively addresses complex graph labeling challenges, ensuring accurate identification and categorization of bonds in diverse chemical structures. It achieves a 98.91% average rate in correct bond label assignments on an antibiotics dataset, showcasing its effectiveness for comparing molecular bond lengths across geometries of varying complexity and size. The executable file and software resources for running MolGC can be downloaded from https://github.com/AbimaelGP/MolGC/tree/main .

Deep learning algorithms applied to computational chemistry.

Recently, there has been a significant increase in the use of deep learning techniques in the molecular sciences, which have shown high performance on datasets and the ability to generalize across data. However, no model has achieved perfect performance in solving all problems, and the pros and cons of each approach remain unclear to those new to the field. Therefore, this paper aims to review deep learning algorithms that have been applied to solve molecular challenges in computational chemistry. We proposed a comprehensive categorization that encompasses two primary approaches; conventional deep learning and geometric deep learning models. This classification takes into account the distinct techniques employed by the algorithms within each approach. We present an up-to-date analysis of these algorithms, emphasizing their key features and open issues. This includes details of input descriptors, datasets used, open-source code availability, task solutions, and actual research applications, focusing on general applications rather than specific ones such as drug discovery. Furthermore, our report discusses trends and future directions in molecular algorithm design, including the input descriptors used for each deep learning model, GPU usage, training and forward processing time, model parameters, the most commonly used datasets, libraries, and optimization schemes. This information aids in identifying the most suitable algorithms for a given task. It also serves as a reference for the datasets and input data frequently used for each algorithm technique. In addition, it provides insights into the benefits and open issues of each technique, and supports the development of novel computational chemistry systems.

Disinfection mechanism of the photocatalytic activity of SnO2 thin films against Candida albicans, proposed from experimental and simulated perspectives.

Nosocomial infections are an important health problem and cause of complications and death in hospitalized patients. This problem should be solved from the preventive angle, avoiding the spread of infections by designing disinfection methods based on the photocatalytic activity of semiconductor materials such as tin oxide (SnO2). The antimicrobial activity of UV light was tested by using inoculation with Candida albicans ATCC10231 on SnO2 thin films and counting colony forming units (CFU). The interaction of UV light with SnO2 was analyzed by density functional theory (DFT) and the extension to the Hubbard model (DFT+U) schemes to predict the electron behavior at the subatomic level. After exposure to UV light, C. albicans showed a reduction of 36.5% in viable cells, and when SnO2 was included, cell viability was reduced by 60.2%. Measurements of the electronic structure obtained by the first-principle calculations under the DFT and DFT+U schemes showed that the O-p orbitals mediate the oxidation process in the bulk semiconductor. By including the surface effects when cleaving the (1 0 0) plane, the three orbitals O-p, Sn-p, and Sn-s are the mediators. SnO2 films are promising antimicrobial coatings because UV light has a synergic activity with thin films, resulting in faster disinfection.

Steps or Terraces? Dynamics of Aromatic Hydrocarbons Adsorbed at Vicinal Metal Surfaces.

The study of how molecules adsorb, diffuse, interact, and desorb from imperfect surfaces is essential for a complete understanding of elementary surface processes under relevant pressure and temperature conditions. Here we use first-principles calculations to study the adsorption of benzene and naphthalene on a vicinal Cu(443) surface with the aim to gain insight into the behavior of aromatic hydrocarbons on realistic surfaces at a finite temperature. Upon strong adsorption at step edges at a low temperature, the molecules then migrate from the step to the (111) terraces, where they can freely diffuse parallel to the step edge. This migration happens at temperatures well below the onset of desorption, suggesting a more complex dynamical picture than previously proposed from temperature-programed desorption studies. The increase of the adsorption strength observed in experiments for Cu(443) when compared to Cu(111) is explained by a stronger long-range van der Waals attraction between the hydrocarbons and the step edges of the Cu(443) surface. Our calculations highlight the need for time-resolved experimental studies to fully understand the dynamics of molecular layers on surfaces.