Search Results
Automated and rapid identification of multidrug resistant Escherichia coli against the lead drugs of acylureidopenicillins, cephalosporins, and fluoroquinolones using specific Raman marker bands
2020, Götz, Theresa, Dahms, Marcel, Kirchhoff, Johanna, Beleites, Claudia, Glaser, Uwe, Bohnert, Jürgen A., Pletz, Mathias W., Popp, Jürgen, Schlattmann, Peter, Neugebauer, Ute
A Raman-based, strain-independent, semi-automated method is presented that allows the rapid (<3 hours) determination of antibiotic susceptibility of bacterial pathogens isolated from clinical samples. Applying a priori knowledge about the mode of action of the respective antibiotic, we identified characteristic Raman marker bands in the spectrum and calculated batch-wise weighted sum scores from standardized Raman intensity differences between spectra of antibiotic exposed and nonexposed samples of the same strains. The lead substances for three relevant antibiotic classes (fluoroquinolone ciprofloxacin, third-generation cephalosporin cefotaxime, ureidopenicillin piperacillin) against multidrug-resistant Gram-negative bacteria (MRGN) revealed a high sensitivity and specificity for the susceptibility testing of two Escherichia coli laboratory strains and 12 clinical isolates. The method benefits from the parallel incubation of control and treated samples, which reduces the variance due to alterations in cultivation conditions and the standardization of differences between batches leading to long-term comparability of Raman measurements. © 2020 The Authors. Journal of Biophotonics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Deep learning a boon for biophotonics
2020, Pradhan, Pranita, Guo, Shuxia, Ryabchykov, Oleg, Popp, Juergen, Bocklitz, Thomas W.
This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data. © 2020 The Authors. Journal of Biophotonics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Bladder tissue characterization using probe-based Raman spectroscopy: Evaluation of tissue heterogeneity and influence on the model prediction
2020, Cordero, Eliana, Rüger, Jan, Marti, Dominik, Mondol, Abdullah S., Hasselager, Thomas, Mogensen, Karin, Hermann, Gregers G., Popp, Jürgen, Schie, Iwan W.
Existing approaches for early-stage bladder tumor diagnosis largely depend on invasive and time-consuming procedures, resulting in hospitalization, bleeding, bladder perforation, infection and other health risks for the patient. The reduction of current risk factors, while maintaining or even improving the diagnostic precision, is an underlying factor in clinical instrumentation research. For example, for clinic surveillance of patients with a history of noninvasive bladder tumors real-time tumor diagnosis can enable immediate laser-based removal of tumors using flexible cystoscopes in the outpatient clinic. Therefore, novel diagnostic modalities are required that can provide real-time in vivo tumor diagnosis. Raman spectroscopy provides biochemical information of tissue samples ex vivo and in vivo and without the need for complicated sample preparation and staining procedures. For the past decade there has been a rise in applications to diagnose and characterize early cancer in different organs, such as in head and neck, colon and stomach, but also different pathologies, for example, inflammation and atherosclerotic plaques. Bladder pathology has also been studied but only with little attention to aspects that can influence the diagnosis, such as tissue heterogeneity, data preprocessing and model development. The present study presents a clinical investigative study on bladder biopsies to characterize the tumor grading ex vivo, using a compact fiber probe-based imaging Raman system, as a crucial step towards in vivo Raman endoscopy. Furthermore, this study presents an evaluation of the tissue heterogeneity of highly fluorescent bladder tissues, and the multivariate statistical analysis for discrimination between nontumor tissue, and low- and high-grade tumor. © 2019 The Authors. Journal of Biophotonics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Effects of cold atmospheric pressure plasma and disinfecting agents on Candida albicans in root canals of extracted human teeth
2020, Kerlikowski, Anne, Matthes, Rutger, Pink, Christiane, Steffen, Heike, Schlüter, Rabea, Holtfreter, Birte, Weltmann, Klaus-Dieter, von Woedtke, Thomas, Kocher, Thomas, Jablonowski, Lukasz
Reinfection in endodontically treated teeth is linked to the complexity of the root canal system, which is problematic to reach with conventional disinfection methods. As plasma is expected to have the ability to sanitize narrow areas, the aim of this study was to analyze the effect of cold atmospheric pressure plasma (CAP) on Candida albicans in root canals of extracted human teeth. CAP was applied as mono treatment and in combination with standard endodontic disinfectants (sodium hypochlorite, chlorhexidine and octenidine). Disinfection efficiency was evaluated as reduction of the logarithm of colony forming units per milliliter (log10 CFU/mL) supported by scanning electron microscopy as imaging technique. Plasma alone showed the highest reduction of log10 CFU, suggesting the best disinfection properties of all tested agents. © 2020 The Authors. Journal of Biophotonics published by Wiley-VCH GmbH.