Quantifying the effects of heating temperature, and combined effects of heating medium pH and recovery medium pH on the heat resistance of Salmonella typhimurium

MXenes Antibacterial Properties and Applications: A Review and Perspective

The mutations of bacteria due to the excessive use of antibiotics, and generation of antibiotic-resistant bacteria have made the development of new antibacterial compounds a necessity. MXenes have emerged as biocompatible transition metal carbide structures with extensive biomedical applications. This is related to the MXenes' unique combination of properties, including multifarious elemental compositions, 2D-layered structure, large surface area, abundant surface terminations, and excellent photothermal and photoelectronic properties. The focus of this review is the antibacterial application of MXenes, which has attracted the attention of researchers since 2016. A quick overview of the synthesis strategies of MXenes is provided and then summarizes the effect of various factors (including structural properties, optical properties, surface charges, flake size, and dispersibility) on the biocidal activity of MXenes. The main mechanisms for deactivating bacteria by MXenes are discussed in detail including rupturing of the bacterial membrane by sharp edges of MXenes nanoflakes, generating the reactive oxygen species (ROS), and photothermal deactivating of bacteria. Hybridization of MXenes with other organic and inorganic materials can result in materials with improved biocidal activities for different applications such as wound dressings and water purification. Finally, the challenges and perspectives of MXene nanomaterials as biocidal agents are presented.

The importance of what we cannot observe: Experimental limitations as a source of bias for meta-regression models in predictive microbiology

Meta-regression models have gained in popularity during the last years as a way to create more generic models for Microbial Risk Assessments that also include variability. However, as with most meta-analyses and empirical models, systematic biases in the data can result in inaccurate models. In this article, we define experimental bias as a type of selection bias due to the practical limitations of microbial inactivation experiments. Conditions with extremely high D-values (i.e. slow inactivation) need very long experimental runs to cause significant reductions. On the other hand, when the D-value is extremely low, not enough data points can be gathered before the microbial population is below the detection limit. Consequently, experimental designs favour conditions within a practical experimental range, introducing a selection bias in the D-values. We demonstrate the impact of experimental bias in meta-regression models using numerical simulations. Models fitted to data with experimental bias overestimated the z-value and underestimated variability. We propose a rapid heuristic method to identify experimental bias in datasets, and we propose truncated regression to mitigate its impact in meta-regression models. Both methods were validated using simulated data. Thereafter the procedures were tested by building a meta-regression model for actual data for the inactivation of Bacillus cereus spores. We concluded that the dataset included experimental bias, and that it would cause an overestimation of the microbial resistance at high temperatures (>120 °C) for classical meta-regression models. This effect was mitigated when the model was built using truncated regression. In conclusion, we demonstrate that experimental bias could potentially result in inaccurate models for predictive microbiology. Therefore, checking for experimental bias should be a routine step in meta-regression modelling, and be included in guidelines on data analysis for meta-regression.

Recent Advancements on Photothermal Conversion and Antibacterial Applications over MXenes-Based Materials

HIGHLIGHTS

Fabrication, characterizations and photothermal properties of MXenes are systematically described. Photothermal-derived antibacterial performances and mechanisms of MXenes-based materials are summarized and reviewed. Recent advances in the derivative applications relying on antibacterial properties of MXenes-based materials, including in vitro and in vivo sterilization, solar water evaporation and purification, and flexible antibacterial fabrics, are investigated.

ABSTRACT

The pernicious bacterial proliferation and emergence of super-resistant bacteria have already posed a great threat to public health, which drives researchers to develop antibiotic-free strategies to eradicate these fierce microbes. Although enormous achievements have already been achieved, it remains an arduous challenge to realize efficient sterilization to cut off the drug resistance generation. Recently, photothermal therapy (PTT) has emerged as a promising solution to efficiently damage the integrity of pathogenic bacteria based on hyperthermia beyond their tolerance. Until now, numerous photothermal agents have been studied for antimicrobial PTT. Among them, MXenes (a type of two-dimensional transition metal carbides or nitrides) are extensively investigated as one of the most promising candidates due to their high aspect ratio, atomic-thin thickness, excellent photothermal performance, low cytotoxicity, and ultrahigh dispersibility in aqueous systems. Besides, the enormous application scenarios using their antibacterial properties can be tailored via elaborated designs of MXenes-based materials. In this review, the synthetic approaches and textural properties of MXenes have been systematically presented first, and then the photothermal properties and sterilization mechanisms using MXenes-based materials are documented. Subsequently, recent progress in diverse fields making use of the photothermal and antibacterial performances of MXenes-based materials are well summarized to reveal the potential applications of these materials for various purposes, including in vitro and in vivo sterilization, solar water evaporation and purification, and flexible antibacterial fabrics. Last but not least, the current challenges and future perspectives are discussed to provide theoretical guidance for the fabrication of efficient antimicrobial systems using MXenes. [Image: see text]

Physiology of the Inactivation of Vegetative Bacteria by Thermal Treatments: Mode of Action, Influence of Environmental Factors and Inactivation Kinetics

Heat has been used extensively in the food industry as a preservation method, especially due to its ability to inactivate microorganisms present in foods. However, many aspects regarding the mechanisms of bacterial inactivation by heat and the factors affecting this process are still not fully understood. The purpose of this review is to offer a general overview of the most important aspects of the physiology of the inactivation or survival of microorganisms, particularly vegetative bacteria, submitted to heat treatments. This could help improve the design of current heat processes methods in order to apply milder and/or more effective treatments that could fulfill consumer requirements for fresh-like foods while maintaining the advantages of traditional heat treatments.

Modeling and Validation of the Ecological Behavior of Wild-Type Listeria monocytogenes and Stress-Resistant Variants

Listeria monocytogenes exhibits a heterogeneous response upon stress exposure which can be partially attributed to the presence of stable stress-resistant variants. This study aimed to evaluate the impact of the presence of stress-resistant variants of Listeria monocytogenes and their corresponding trade-offs on population composition under different environmental conditions. A set of stress robustness and growth parameters of the wild type (WT) and an rpsU deletion variant was obtained and used to model their growth behavior under combined mild stress conditions and to model their kinetics under single- and mixed-strain conditions in a simulated food chain. Growth predictions for the WT and the rpsU deletion variant matched the experimental data generally well, although some deviations from the predictions were observed. The data highlighted the influence of the environmental conditions on the ratio between the WT and variant. Prediction of performance in the simulated food chain proved to be challenging. The trend of faster growth and lower stress robustness for the WT than for the rpsU variant in the different steps of the chain was confirmed, but especially for the inactivation steps and the time needed to resume growth after an inactivation step, the experimental data deviated from the model predictions. This report provides insights into the conditions which can select for stress-resistant variants in industrial settings and discusses their potential persistence in food processing environments.

IMPORTANCE

Listeria monocytogenes exhibits a heterogeneous stress response which can partially be attributed to the presence of genetic variants. These stress-resistant variants survive better under severe conditions but have, on the other hand, a reduced growth rate. To date, the ecological behavior and potential impact of the presence of stress-resistant variants is not fully understood. In this study, we quantitatively assessed growth and inactivation behavior of wild-type L. monocytogenes and its stress-resistant variants. Predictions were validated under different conditions, as well as along a model food chain. This work illustrates the effects of environmental factors on population dynamics of L. monocytogenes and is a first step in evaluating the impact of population diversity on food safety.

Cross-Laboratory Comparative Study of the Impact of Experimental and Regression Methodologies on Salmonella Thermal Inactivation Parameters in Ground Beef

Isothermal inactivation studies are commonly used to quantify thermal inactivation kinetics of bacteria. Meta-analyses and comparisons utilizing results from multiple sources have revealed large variations in reported thermal resistance parameters for Salmonella, even when in similar food materials. Different laboratory or regression methodologies likely are the source of methodology-specific artifacts influencing the estimated parameters; however, such effects have not been quantified. The objective of this study was to evaluate the effects of laboratory and regression methodologies on thermal inactivation data generation, interpretation, modeling, and inherent error, based on data generated in two independent laboratories. The overall experimental design consisted of a cross-laboratory comparison using two independent laboratories (Michigan State University and U.S. Department of Agriculture, Agricultural Research Service, Eastern Regional Research Center [ERRC] laboratories), both conducting isothermal Salmonella inactivation studies (55, 60, 62°C) in ground beef, and each using two methodologies reported in prior studies. Two primary models (log-linear and Weibull) with one secondary model (Bigelow) were fitted to the resultant data using three regression methodologies (two two-step regressions and a one-step regression). Results indicated that laboratory methodology impacted the estimated D60°C- and z-values (α = 0.05), with the ERRC methodology yielding parameter estimates ∼25% larger than the Michigan State University methodology, regardless of the laboratory. Regression methodology also impacted the model and parameter error estimates. Two-step regressions yielded root mean square error values on average 40% larger than the one-step regressions. The Akaike Information Criterion indicated the Weibull as the more correct model in most cases; however, caution should be used to confirm model robustness in application to real-world data. Overall, the results suggested that laboratory and regression methodologies have a large influence on resultant data and the subsequent estimation of thermal resistance parameters.

Predictive thermal inactivation model for the combined effect of temperature, cinnamaldehyde and carvacrol on starvation-stressed multiple Salmonella serotypes in ground chicken

We investigated the combined effect of three internal temperatures (60, 65 and 71.1 °C) and four concentrations (0.0, 0.1, 0.5 and 1% vol/wt) of two natural antimicrobials on the heat resistance of an eight-strain cocktail of Salmonella serovars in chicken meat. A complete factorial design (3×4×4) was used to assess the effects and interactions of heating temperature and the two antimicrobials, carvacrol and cinnamaldehyde. The 48 variable combinations were replicated to provide a total of 96 survivor curves from the experimental data. Mathematical models were then developed to quantify the combined effect of these parameters on heat resistance of starved Salmonella cells. The theoretical analysis shows that the addition of plant-derived antimicrobials overcomes the heat resistance of starvation-stressed Salmonella in ground chicken meat. The influence of the antimicrobials allows reduced heat treatments, thus reducing heat-induced damage to the nutritional quality of ground-chicken products. Although the reported omnibus log-linear model with tail and the omnibus sigmoid model could represent the experimental survivor curves, their discrepancy only became apparent in the present study when lethality times (D-values and t7.0) from each of the models were calculated. Given the concave nature of the inactivation curves, the log-linear model with tail greatly underestimates the times needed to obtain 7.0 log lethality. Thus, a polynomial secondary model, based on the sigmoid model, was developed to accurately predict the 7.0-log reduction times. The three-factor predictive model can be used to estimate the processing times and temperatures required to achieve specific log reductions, including the regulatory recommendation of 7.0-log reduction of Salmonella in ground chicken.