Machine Learning-Driven Classification of Urease Inhibitors Leveraging Physicochemical Properties as Effective Filter Criteria.

Urease, a pivotal enzyme in nitrogen metabolism, plays a crucial role in various microorganisms, including the pathogenic Helicobacter pylori. Inhibiting urease activity offers a promising approach to combating infections and associated ailments, such as chronic kidney diseases and gastric cancer. However, identifying potent urease inhibitors remains challenging due to resistance issues that hinder traditional approaches. Recently, machine learning (ML)-based models have demonstrated the ability to predict the bioactivity of molecules rapidly and effectively. In this study, we present ML models designed to predict urease inhibitors by leveraging essential physicochemical properties. The methodological approach involved constructing a dataset of urease inhibitors through an extensive literature search. Subsequently, these inhibitors were characterized based on physicochemical properties calculations. An exploratory data analysis was then conducted to identify and analyze critical features. Ultimately, 252 classification models were trained, utilizing a combination of seven ML algorithms, three attribute selection methods, and six different strategies for categorizing inhibitory activity. The investigation unveiled discernible trends distinguishing urease inhibitors from non-inhibitors. This differentiation enabled the identification of essential features that are crucial for precise classification. Through a comprehensive comparison of ML algorithms, tree-based methods like random forest, decision tree, and XGBoost exhibited superior performance. Additionally, incorporating the "chemical family type" attribute significantly enhanced model accuracy. Strategies involving a gray-zone categorization demonstrated marked improvements in predictive precision. This research underscores the transformative potential of ML in predicting urease inhibitors. The meticulous methodology outlined herein offers actionable insights for developing robust predictive models within biochemical systems.

Calcium-Alginate-Chitosan Nanoparticle as a Potential Solution for Pesticide Removal, a Computational Approach.

Pesticides have a significant negative impact on the environment, non-target organisms, and human health. To address these issues, sustainable pest management practices and government regulations are necessary. However, biotechnology can provide additional solutions, such as the use of polyelectrolyte complexes to encapsulate and remove pesticides from water sources. We introduce a computational methodology to evaluate the capture capabilities of Calcium-Alginate-Chitosan (CAC) nanoparticles for a broad range of pesticides. By employing ensemble-docking and molecular dynamics simulations, we investigate the intermolecular interactions and absorption/adsorption characteristics between the CAC nanoparticles and selected pesticides. Our findings reveal that charged pesticide molecules exhibit more than double capture rates compared to neutral counterparts, owing to their stronger affinity for the CAC nanoparticles. Non-covalent interactions, such as van der Waals forces, π-π stacking, and hydrogen bonds, are identified as key factors which stabilized the capture and physisorption of pesticides. Density profile analysis confirms the localization of pesticides adsorbed onto the surface or absorbed into the polymer matrix, depending on their chemical nature. The mobility and diffusion behavior of captured compounds within the nanoparticle matrix is assessed using mean square displacement and diffusion coefficients. Compounds with high capture levels exhibit limited mobility, indicative of effective absorption and adsorption. Intermolecular interaction analysis highlights the significance of hydrogen bonds and electrostatic interactions in the pesticide-polymer association. Notably, two promising candidates, an antibiotic derived from tetracycline and a rodenticide, demonstrate a strong affinity for CAC nanoparticles. This computational methodology offers a reliable and efficient screening approach for identifying effective pesticide capture agents, contributing to the development of eco-friendly strategies for pesticide removal.

Macrolides: a novel risk factor for carbapenemase-producing Enterobacterales in intensive care units.

INTRODUCTION: Carbapenemase-producing Enterobacterales (CPE) have emerged as a substantial cause of morbi-mortality worldwide, with a prevalence of approximately 5% in areas with high endemicity. However, available data may not be representative of developing countries, such as Ecuador. In this study, the incidence of CPE in Ecuador and risk factors for infection/colonisation were evaluated. METHODOLOGY: A prospective cohort study was performed from February to April 2016 in seven intensive-care units of Guayaquil, Ecuador. Samples were processed according to the Centers for Disease Control and Prevention laboratory protocol and the CHROMagar mSuper CARBA agar method. Resistance to carbapenems was defined according to Clinical and Laboratory Standards Institute breakpoints. A modified carbapenemase inactivation method was used to identify carbapenamase production phenotypically with molecular confirmation by multiplex polymerase chain reaction. RESULTS: In total, 640 patients were enrolled. The incidence of CPE was 36.4% (N = 233). A multivariate analysis indicated that several factors were associated with CPE acquisition, included a long intensive care unit stay (OR 1.05; 95% CI 1.03-1.08; p < 0.01), tracheostomy (OR 3.52; 95% CI 1.90-6.75; p < 0.01), hospitalisation 3 months prior to admission (OR 2.07; 95% CI 1.17-3.71; p < 0.01), vancomycin use (OR 3.31; 95% CI 2.02-5.18; p < 0.01), and macrolide use (OR 3.31; 95% CI 1.43-7.76; p < 0.01). CONCLUSIONS: Macrolide use was a risk factor for CPE acquisition. This association should be evaluated further, especially in developing countries.

Carbapenemase producing Enterobacteriaceae in intensive care units in Ecuador: Results from a multicenter study.

INTRODUCTION: Carbapenemase-producing Enterobacteriaceae (CPE) are of global concern due to the growing number of patients who acquire them and their association with high mortality rates. Although there are some reports of endemicity in developing countries, little is known about this microorganism, and Ecuador is not an exception. Subsequently, our objective was to clinically and molecularly characterize carbapenemase producing-Enterobacteriaceae in intensive care units (ICUs) in Guayaquil, Ecuador. METHODS: To determine CPE colonization, we obtained perineal and inguinal swabs from patients admitted to seven intensive-care adult units in Guayaquil-Ecuador between February and April 2016. The Centers for Disease Control and Prevention (CDC) laboratory protocol and chromogenic agar were used to process the cultures. Polymerase chain reaction was used to confirm carbapenemase production. Genotypic analysis was performed by Multilocus Sequence Typing (MLST) and pulsed-field electrophoresis (PFEG). Demographic and clinical data were obtained from the electronic charts and patient's relatives. RESULTS: Six hundred seventy-seven patients were included in the study, of whom 255 were colonized/infected by CPE. The CPE prevalence was 37.67%. Previous use of antimicrobials, use of invasive procedures and being burned at admission were associated with CPE. The most frequent infection was found after a surgical procedure. Klebsiella pneumoniae (n=249) was the predominant microorganism harbouring blaKPC, followed by Enterobactercloacae (n=8), Klebsiella aerogenes (n=4), Escherichia coli (n=4) and Klebsiella oxytoca (n=1). NDM was present in Proteus mirabilis. The strains were distributed in 19 sequence types (ST), and 10 were not reported previously in Ecuador. ST 258 was the sequence type isolated most frequently. CONCLUSION: This study shows a high prevalence of CPE in ICUs, particularly K. pneumoniae blaKPC ST 258. The identification of KPC alleles may help to understand the routes of dissemination and control spread within ICUs in Guayaquil, Ecuador.