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In a previous publication, we trained predictive models based on Raman bulk spectra of microorganisms placed on a silicon dioxide protected silver mirror slide to make predictions for new Raman spectra, unknown to the models, of microorganisms placed on a different substrate, namely stainless steel. Now we have combined large sections of this data and trained a convolutional neural network (CNN) to make predictions for single cell Raman spectra. We show that a database based on microbial bulk material is conditionally suited to make predictions for the same species in terms of single cells. Data of 13 different microorganisms (bacteria and yeasts) were used. Two of the 13 species could be identified 90% correctly and five other species 71%–88%. The six remaining species were correctly predicted by only 0%–49%. Especially stronger fluorescence in bulk material compared to single cells but also photodegradation of carotenoids are some effects that can complicate predictions for single cells based on bulk data. The results could be helpful in assessing universal Raman tools or databases.

Due to the current high awareness and rising energy costs, the energy label of domestic appliances is an important indicator for consumers when buying a dishwasher. However, not all consumers use efficient Eco cycles on a regular basis. When investigating the use of domestic dishwashers and the chosen programs in a survey with 6090 participants from 11 countries in Europe, it turned out that only 25% used Eco cycles regularly while the vast majority of 48% prefer to use Normal/Auto cycles. We also assessed individual energy and water consumption as well as duration and temperature of the programs. Considering the fact that Eco programs are not well accepted due to their duration, our study shows that Short programs, which on average only last about 30 min and consume little energy and water, might be an economical and convenient alternative to the more time-consuming Eco programs. In fact, some Short programs considered in this study proved to consume less energy for a full dishwasher load (0.74 kWh on average) than the standard Eco programs (0.90 kWh on average), especially due to their shorter duration, but it is important to note that, according to the manufacturer, these programs are designed for rather lightly soiled dishes.

Abstract The multifactorial nature of Pseudomonas aeruginosa biofilm development and genomic variabilities implicates its resistance to conventional antimicrobials and virulence. Therefore, genetic determinants need to be extensively studied to block the early steps of biofilm or already formed biofilms. In this study, a total of 20 multidrug resistant (MDR) clinical P. aeruginosa isolates were evaluated for their biofilm forming abilities and related genes. Of the isolates tested, all of them showed surface attachment tendencies in nutrient limiting conditions, and classified as strong (SBF = 45%), moderate (MBF = 30%) and weak (WBF = 25%) biofilm formers. Complete genome sequencing of representative strong (DMC-27b), moderate (DMC-20c) and weak biofilm former (DMC-30b) isolates was performed. Analysis of biofilm related genes in the sequenced genomes revealed that, 80 of the 88 biofilm related genes possess 98–100% sequence identity to the reference PAO1 strain. Complete and partial sequence data of LecB proteins from tested isolates indicate that isolates containing PA14-like LecB sequences produced strong biofilms. All of the 7 pel operon protein coding genes in weak biofilm former isolate 30b showed significant nucleotide sequence variation with other tested isolates, and their corresponding proteins are 99% identical with the pel operon proteins of PA7. Bioinformatics analyses identified divergent sequence and structural features that separate PA7 like pel operon proteins from reference PAO1-like pel operon. Congo red and pellicle forming assays revealed that the sequence and structure variations may have interfered with the Pel production pathway and resulted in impaired Pel production in isolate 30b that has a PA7 like pel operon. Expression analysis also showed that both pelB and lecB genes were about 5 to 6 folds upregulated after 24 h in SBF 27b in comparison with WBF 30b. Our findings indicate significant genomic divergence in biofilm related genes of P. aeruginosa strains that affect their biofilm phenotypes.

Numerous publications showing that robust prediction models for microorganisms based on Raman micro-spectroscopy in combination with chemometric methods are feasible, often with very precise predictions. Advances in machine learning and easier accessibility to software make it increasingly easy for users to generate predictive models from complex data. However, the question regarding why those predictions are so accurate receives much less attention. In our work, we use Raman spectroscopic data of fungal spores and carotenoid-containing microorganisms to show that it is often not the position of the peaks or the subtle differences in the band ratios of the spectra, due to small differences in the chemical composition of the organisms, that allow accurate classification. Rather, it can be characteristic effects on the baselines of Raman spectra in biochemically similar microorganisms that can be enhanced by certain data pretreatment methods or even neutral-looking spectral regions can be of great importance for a convolutional neural network. Using a method called Gradient-weighted Class Activation Mapping, we attempt to peer into the black box of convolutional neural networks in microbiological applications and show which Raman spectral regions are responsible for accurate classification.

Viruses contribute significantly to the burden of infectious diseases worldwide. Although there are multiple infection routes associated with viruses, it is important to break the chain of infection and thus consider all possible transmission routes. Consequently, laundering can be a means to eliminate viruses from textiles, in clinical settings well as for domestic laundry procedures. Several factors influence the survival and inactivation of microorganisms, including viruses on hard surfaces and textiles. Therefore, textiles should be regarded as potential fomites. While in clinical and industrial settings laundry hygiene is ensured by standardized processes, temperatures of at least 60 °C and the use of oxidizing agents, domestic laundry is not well defined. Thus, the parameters affecting viral mitigation must be understood and prudently applied, especially in domestic laundering. Laundering can serve as a means to break the chain of infection for viral diseases by means of temperature, time, chemistry and mechanical action.

The use of prebiotic substances in cosmetic products in order to improve skin appearance by influencing the skin microbiome has become quite common. As known from the food industry, prebiotic cosmetics aim for specific support of beneficial microorganisms, while others, especially pathogenic bacteria, are sought to be inhibited. To develop such products and evaluate their efficacy, it is crucial to understand the mode of action of prebiotic substances already before an in vivo use phase, e.g., by applying appropriate lab methods. We describe the development of a suitable in vitro model in order to test the efficacy of prebiotic substances incorporated in different types of formulas. A leather surface inoculated with different bacterial species of the human residential skin microbiota proved effective in simulating the action of prebiotic agents on the skin. It could be shown that the growth of bacteria was positively or negatively influenced by cosmetic formulas containing prebiotic substances. Thus, the model allows the investigation of prebiotic agents in cosmetic formulas with respect to their efficacy on a skin-like surface.

The elimination of microbial contaminations from textile is an important aspect of laundering apart from the removal of stains and dirt from used and worn textiles. Although the framework for institutional laundering is well regulated to ensure hygienic cleanliness via the use of e.g. high temperatures and bleaching agents, there are several open points, especially in domestic laundering. In both cases, energy efficiency of appliances is a main driver for innovation and has resulted in a general decrease in washing temperatures which in turn can impact the antimicrobial efficacy of laundering. Thus, the different factors influencing the input and removal of microbial cells in the laundering process and possible adverse effects of microbial contaminants in the washing machine and on the textiles as well as suitable counteractions are discussed in this article, focusing on the clinical area but also considering the domestic environment, which will gain importance in the future, e.g. by the increase of elderly and ill persons being cared for at home.

Moist/hydrated biofilms have been well-studied in the medical area, and their association with infections is widely recognized. In contrast, dry-surface biofilms (DSBs) on environmental surfaces in healthcare settings have received less attention. DSBs have been shown to be widespread on commonly used items in hospitals and to harbor bacterial pathogens that are known to cause healthcare-acquired infections (HAI). DSBs cannot be detected by routine surface swabbing or contact plates, and studies have shown DSBs to be less susceptible to cleaning/disinfection products. As DSBs are increasingly reported in the medical field, and there is a likelihood they also occur in food production and manufacturing areas, there is a growing demand for the rapid in situ detection of DSBs and the identification of pathogens within DSBs. Raman microspectroscopy allows users to obtain spatially resolved information about the chemical composition of biofilms, and to identify microbial species. In this study, we investigated Staphylococcus aureus mono-species DSB on polyvinylchloride blanks and stainless steel coupons, and dual-species (S. aureus/Bacillus licheniformis) DSB on steel coupons. We demonstrated that Raman microspectroscopy is not only suitable for identifying specific species, but it also enables the differentiation of vegetative cells from their sporulated form. Our findings provide the first step towards the rapid identification and characterization of the distribution and composition of DSBs on different surface areas.

An easy, inexpensive, and rapid method to identify microorganisms is in great demand in various areas such as medical diagnostics or in the food industry. In our study, we show the development of several predictive models based on Raman spectroscopy combined with support vector machines (SVM) for 21 species of microorganisms. The microorganisms, grown under standardized conditions, were placed on a silver mirror slide to record the data for model development. Additional data was obtained from microorganisms on a polished stainless-steel slide in order to validate the models in general and to assess possible negative influences of the material change on the predictions. The theoretical prediction accuracies for the most accurate models, based on a five-fold cross-validation, are 98.4%. For practical validation, new spectra (from stainless-steel surfaces) have been used, which were not included in the calibration data set. The overall prediction accuracy in practice was about 80% and the inaccurate predictions were only due to a few species. The development of a database provides the basis for further investigations such as the application and extension to single-cell analytics and for the characterization of biofilms.