Staining-Enhanced Peroxidase-Mimicking Gold Nanoparticles in Nano-ELISA for Highly Sensitive Detection of Klebsiella pneumoniae.

Klebsiella pneumoniae, a member of the family Enterobacteriaceae, is a rod-shaped, Gram-negative bacterium, mainly found in the hospital environment and medical tools. It is the leading cause of nosocomial infection, characterized by bloodstream infection, wound site infection, urinary tract infection, and sepsis, mostly in older adults, newborn infants, and immunocompromised patients. This present study demonstrated a novel diagnostic method for K. pneumoniae detection based on the gold nanozyme activity for the oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) in the presence of H2O2. The nanozyme activity of AuNPs with staining enhancement was statistically three times higher than that of the bare AuNPs in solid absorption at 650 nm. Nano-ELISA with staining enhancement could detect as low as 102 CFUs/mL of K. pneumoniae concentration, as the cutoff value was determined to be 0.158, which boosted the sensitivity of the immunoreactions by up to 100-fold. The detection limit of our assays was 26.023 CFUs/mL, and the limit of quantification was 78.857 CFUs/mL. There was no cross-reaction against other bacteria, which proved the immunoassays' remarkable specificity for recognizing K. pneumoniae. Taken together, we successfully developed and optimized the highly sensitive and decently specific nano-ELISA strategy that might be applicable for detecting various other bacterial pathogens.

Machine learning-assisted evaluation of potential biochars for pharmaceutical removal from water.

A popular approach to select optimal adsorbents is to perform parallel experiments on adsorbents based on an initially decided goal such as specified product purity, efficiency, or binding capacity. To screen optimal adsorbents, we focused on the max adsorption capacity of the candidates at equilibrium in this work because the adsorption capacity of each adsorbent is strongly dependent on certain conditions. A data-driven machine learning tool for predicting the max adsorption capacity (Qm) of 19 pharmaceutical compounds on 88 biochars was developed. The range of values of Qm (mean 48.29 mg/g) was remarkably large, with a high number of outliers and large variability. Modified biochars enhanced the Qm and surface area values compared with the original biochar, with a statistically significant difference (Chi-square value = 7.21-18.25, P < 0.005). K- nearest neighbors (KNN) was found to be the most optimal algorithm with a root mean square error (RMSE) of 23.48 followed by random forest and Cubist with RMSE of 26.91 and 29.56, respectively, whereas linear regression and regularization were the worst algorithms. KNN model achieved R2 of 0.92 and RMSE of 16.62 for the testing data. A web app was developed to facilitate the use of the KNN model, providing a reliable solution for saving time and money in unnecessary lab-scale adsorption experiments while selecting appropriate biochars for pharmaceutical adsorption.

Quantitative value chain approaches for animal health and food safety.

Economic impact assessments are increasingly important in the context of animal health and food safety, although much of the existing literature does not address the broader systems context in which disease transmission takes place. In this paper, we discuss the role of system dynamics modeling in addressing the value chain impacts associated with animal health and food safety issues. System dynamics methods hold promise as a means of capturing the complex feedbacks that exist between the biology, economics, and behavioral aspects of food safety and animal health systems. We provide a proof-of-concept of this approach in the context of food safety and animal health in the smallholder pig sector of Viet Nam. Results highlight the important tradeoffs that exist between policy objectives and the costs required to achieve them.