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Raman spectroscopy is an analytical method to identify medical samples of bacteria. Because Raman spectroscopy detects the biochemical properties of a cell, there are many factors that can influence and modify the Raman spectra of bacteria. One possible influence is a proper method for isolation of the bacteria. Medical samples in particular never occur in purified form, so a Raman-compatible isolation method is needed which does not affect the bacteria and thus the resulting spectra. In this study, we present a Raman-compatible method for isolation of bacteria from bronchoalveolar lavage (BAL) fluid using density gradient centrifugation. In addition to measuring the bacteria from a patient sample, the yield and the spectral influence of the isolation on the bacteria were investigated. Bacteria isolated from BAL fluid show additional peaks in comparison to pure culture bacteria, which can be attributed to components in the BAL sample. The isolation gradient itself has no effect on the spectra, and with a yield of 63% and 78%, the method is suitable for isolation of low concentrations of bacteria from a complex matrix. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s00216-021-03488-0.

In recent years, Raman spectroscopy has become an established method to study medical, biological or environmental samples. Since Raman spectroscopy is a phenotypic method, many parameters can influence the spectra. One of these parameters is the concentration of CO

Supplemental Digital Content is Available in the Text., Background: Adequate antibiotic treatment is a prerequisite for the successful treatment of systemic infections. Based on accumulating scientific evidence, a fixed dosage regimen can lead to insufficient and ineffective antibiotic therapy. Thus, the aim of this study was to develop and validate a simplified, but sensitive method for the simultaneous quantification of antimicrobials by using liquid chromatography with tandem mass spectrometry (LC-MS/MS) for the development of personalized therapy regimens using therapeutic drug monitoring. Methods: A method was developed for the simultaneous quantification of 9 antimicrobials (aciclovir, ampicillin, cefuroxime, ciprofloxacin, meropenem, metronidazole, piperacillin, rifampicin, and tazobactam) in lithium–heparin plasma. A simple sample preparation method and a chromatographic run time of 10 minutes enabled the quick processing of the samples. The method was validated according to the guidelines for bioanalytical method validation of the European Medicines Agency and addressed sensitivity, specificity, linearity, accuracy, precision, dilution integrity, carry-over, recovery, matrix effects, and stability. Results: The chromatographic run time was 10 minutes and antimicrobials eluted at retention times ranging from 1.1 to 2.2 minutes. Calibration curve for all antimicrobials was linear over a range of 1–100 mg/L, and a 2-fold or 5-fold dilution of the samples was possible. The method accuracy ranged from 85.1% to 114.9% for all measured antimicrobials, and the within- and between-run precision values were

Raman spectroscopy is currently advertised as a hot and ambitious technology that has all of the features needed to characterize and identify bacteria. Raman spectroscopy is rapid, easy to use, noninvasive, and it could complement established microbiological and biomolecular methods in the near future. To bring this vision closer to reality, ongoing research is being conducted on spectral fingerprinting. This can yield a wealth of information, from even single bacteria from various habitats which can be further improved by combining Raman spectroscopy with methods such as stable isotope probing to elucidate microbial interactions. In conjunction with extensive statistical analysis, Raman spectroscopy will allow identification of (non)pathogenic bacteria at different taxonomic levels.

This thesis explores the challenges and potential in crowdsourcing for the content of a travel app, and the factors which motivate travelers to share information in a travel app. Primary empirical data was collected through a questionnaire and interview based on a medium-fidelity prototype called Little Earth using an inductive methodological approach. The qualitative questionnaire was conducted with nine volunteer travelers at a hostel in San Francisco, USA, and the interviews were based on a predetermined set of questions and conducted with six travelers in Copenhagen, Denmark. Through an analysis and discussion of the primary data and relevant secondary literature, it was concluded that there are a number of clear points that indicate that crowdsourcing can contribute positively to a travel app, as respondents indicated that they take other travelers' reviews and recommendations into consideration with regards to the various travel choices they make. This indicates that a user of the travel app would be able to gain better and more local knowledge, through the crowdsourced reviews and recommendations, thereby rendering travelers a global workforce for the travel app and fellow users. Some challenges were identified pertaining to the requirement of user willingness to share information, however, it was found that altruism, publicity, and a 'community' feeling among users were motivating factors. The inclusion of personal user profiles could, therefore, increase the feeling of being part of a community and increase motivation to participate. Factors such as financial, enjoyment, curiosity and non-monetary personal gains remain unexplored, but cannot be ruled out as motivating elements. It can be further concluded that in addition to the above-mentioned motivations, the design should be simple and intuitive, so that it requires as little effort as possible of the user.

Recently, Raman spectroscopy has become more and more in the focus of bacterial identification as it is a culture-independent, nondestructive, and contact-less method. Since Raman spectroscopy is a phenotypic method, lots of parameters can influence the spectra. One of the least controllable factors is transport and storage, but it is often not taken into account, and therefore these influences on the Raman spectra of bacteria are unknown. In order to investigate this effect, we simulated the transport and storage of bacteria under different conditions and investigated them with Raman spectroscopy. With a look at the mean spectra, only one bacterium showed differences during the storage conditions. However, after applying chemometric methods, changes in the data could be found within all bacteria during storage times. This study shows how drastic the effect will influence a database, depending on the different handling or storage. Therefore, it is of the utmost importance to consider these nonbiological influences when planning further experiments and evaluating the resulting data.

In recent years, Raman spectroscopy has become an established method to study medical, biological or environmental samples. Since Raman spectroscopy is a phenotypic method, many parameters can influence the spectra. One of these parameters is the concentration of CO2, as this never remains stable in nature, but always adjusts itself in a dynamic equilibrium. So, it is obvious that the concentration of CO2 cannot be controlled but it might have a big impact on the bacteria and bacterial composition in medical samples. When using a phenotypic method like Raman spectroscopy it is also important to know the influence of CO2 to the dataset. To investigate the influence of CO2 towards Raman spectra we cultivated E. coli at different concentration of CO2 since this bacterium is able to switch metabolism from aerobic to microaerophilic conditions. After applying statistic methods small changes in the spectra became visible and it was even possible to observe the change of metabolism in this species according to the concentration of CO2.