Objectives: Infections that are inadequately treated owing to acquired bacterial resistance are a leading cause of mortality. Rates of multidrug-resistant bacteria are rising, resulting in increased antibiotic failures and worsening patient outcomes. Mathematical modelling makes it possible to predict the future spread of bacterial antimicrobial resistance. The aim of this study was to construct a mathematical model that can describe the dependency between the level of antimicrobial resistance and the amount of antibiotic usage.
Methods: After reviewing existing mathematical models, a cross-sectional, retrospective study was carried out to collect clinical and microbiological data across 3000 patients for the construction of the mathematical model. Based on these data, a model was developed and tested to determine the dependency between antibiotic usage and resistance.
Results: Consumption of inhibitor/cephalosporins and fluoroquinolones increases inhibitor/penicillin resistance. Consumption of inhibitor/penicillins increases cephalosporin resistance. Consumption of inhibitor/penicillins increases inhibitor/cephalosporin resistance.
Conclusions: It was demonstrated that in some antibiotic-micro-organism pairs, the level of antibiotic usage significantly influences the level of resistance. The model makes it possible to predict the change in resistance and also shows the quantitative effect of antibiotic consumption on the level of bacterial resistance.
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http://dx.doi.org/10.1016/j.jgar.2016.11.010 | DOI Listing |
Mol Divers
January 2025
Key Laboratory for Macromolecular Science of Shaanxi Province, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an, 710119, People's Republic of China.
Molecular Property Prediction (MPP) is a fundamental task in important research fields such as chemistry, materials, biology, and medicine, where traditional computational chemistry methods based on quantum mechanics often consume substantial time and computing power. In recent years, machine learning has been increasingly used in computational chemistry, in which graph neural networks have shown good performance in molecular property prediction tasks, but they have some limitations in terms of generalizability, interpretability, and certainty. In order to address the above challenges, a Multiscale Molecular Structural Neural Network (MMSNet) is proposed in this paper, which obtains rich multiscale molecular representations through the information fusion between bonded and non-bonded "message passing" structures at the atomic scale and spatial feature information "encoder-decoder" structures at the molecular scale; a multi-level attention mechanism is introduced on the basis of theoretical analysis of molecular mechanics in order to enhance the model's interpretability; the prediction results of MMSNet are used as label values and clustered in the molecular library by the K-NN (K-Nearest Neighbors) algorithm to reverse match the spatial structure of the molecules, and the certainty of the model is quantified by comparing virtual screening results across different K-values.
View Article and Find Full Text PDFAngiogenesis
January 2025
Department of Obstetrics and Gynaecology, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands.
Adenomyosis is characterized by abnormal uterine bleeding, dysmenorrhea and subfertility. Increased expression of angiogenesis markers in adenomyosis presents a treatment opportunity and was studied in an adenomyosis mouse model. Mice were administered tamoxifen (1 mg/kg) on neonatal days 2-5.
View Article and Find Full Text PDFJ Mol Model
January 2025
PG & Research Department of Mathematics, Sanatana Dharma College, Kerala University, Alappuzha, Kerala, 688003, India.
Holey nanographene, an allotrope of carbon arranged in two dimensions, has gained remarkable attention as a nanomaterial with several potential uses in numerous industries, such as electronics, energy storage, healthcare, and environmental cleanup, because of its high carrier mobility, flexibility, transparency, high surface area, conductivity, and chemical stability. The fundamental holey nanographene is assembled in a linear form to create the holey nanographene chain (HNC) that is being discussed. To fully utilize it in various applications, it is essential to comprehend the basic ideas guiding its behavior at the nanoscale; for that, we find various topological indices for this holey nanographene chain using the cut method.
View Article and Find Full Text PDFInflamm Res
January 2025
Departamento de Morfologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil.
Objective: We aimed to understand the potential therapeutic and anti-inflammatory effects of the phosphodiesterase-4 (PDE4) inhibitor roflumilast in models of pulmonary infection caused by betacoronaviruses.
Methods: Mice were infected intranasally with murine hepatitis virus (MHV-3) or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Roflumilast was given to MHV-3-infected mice therapeutically at doses of 1 mg/kg or 10 mg/kg, or prophylactically at 10 mg/kg.
Genetics
January 2025
Interfaculty Bioinformatics Unit, University of Bern, Bern 3012, Switzerland.
Purifying selection is a critical factor in shaping genetic diversity. Current theoretical models mostly address scenarios of either very weak or strong selection, leaving a significant gap in our knowledge. The effects of purifying selection on patterns of genomic diversity remain poorly understood when selection against deleterious mutations is weak to moderate, particularly when recombination is limited or absent.
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