2022, Spange, Stefan, Weiß, Nadine, Mayerhöfer, Thomas G.

The DHBD quantity represents the hydroxyl group density of alcoholic solvents or water. DHBD is purely physically defined by the product of molar concentration of the solvent (N) and the factor Σn=n×f which reflects the number n and position (f-factor) of the alcoholic OH groups per molecule. Whether the hydroxyl group is either primary, secondary or tertiary is taken into account by f. Σn is clearly linearly correlated with the physical density or the refractive index of the alcohol derivative. Relationships of solvent-dependent UV/Vis absorption energies as ET(30) values, 129Xe NMR shifts and kinetic data of 2-chloro-2-methylpropane solvolysis with DHBD are demonstrated. It can be shown that the ET(30) solvent parameter reflects the global polarity of the hydrogen bond network rather than specific H-bond acidity. Significant correlations of the log k1 rate constants of the solvolysis reaction of 2-chloro-2-methylpropane with DHBD show the physical reasoning of the approach.

2021, Rodewald, Marko, Bae, Hyeonsoo, Huschke, Sophie, Meyer-Zedler, Tobias, Schmitt, Michael, Press, Adrian Tibor, Schubert, Stephanie, Bauer, Michael, Popp, Juergen

Here we present a microscope setup for coherent anti-Stokes Raman scattering (CARS) imaging, devised to specifically address the challenges of in vivo experiments. We exemplify its capabilities by demonstrating how CARS microscopy can be used to identify vitamin A (VA) accumulations in the liver of a living mouse, marking the positions of hepatic stellate cells (HSCs). HSCs are the main source of extracellular matrix protein after hepatic injury and are therefore the main target of novel nanomedical strategies in the development of a treatment for liver fibrosis. Their role in the VA metabolism makes them an ideal target for a CARS-based approach as they store most of the body's VA, a class of compounds sharing a retinyl group as a structural motive, a moiety that is well known for its exceptionally high Raman cross section of the C=C stretching vibration of the conjugated backbone.

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

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

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

2022, Du, Shengjun, Leistenschneider, Desirée, Xiao, Jing, Dellith, Jan, Troschke, Erik, Oschatz, Martin

Sorption-based water capture is an attractive solution to provide potable water in arid regions. Heteroatom-decorated microporous carbons with hydrophilic character are promising candidates for water adsorption at low humidity, but the strong affinity between the polar carbon pore walls and water molecules can hinder the water transport within the narrow pore system. To reduce the limitations of mass transfer, C2N-type carbon materials obtained from the thermal condensation of a molecular hexaazatriphenylene-hexacarbonitrile (HAT-CN) precursor were treated mechanochemically via ball milling. Scanning electron microscopy as well as static light scattering reveal that large pristine C2N-type particles were split up to a smaller size after ball milling, thus increasing the pore accessibility which consequently leads to faster occupation of the water vapor adsorption sites. The major aim of this work is to demonstrate the applicability of thermal response measurements to track these enhanced kinetics of water adsorption. The adsorption rate constant of a C2N material condensed at 700 °C remarkably increased from 0.026 s−1 to 0.036 s−1 upon ball milling, while maintaining remarkably high water vapor capacity. This work confirms the advantages of small particle sizes in ultramicroporous materials on their vapor adsorption kinetics. It is demonstrated that thermal response measurements are a valuable and time-saving method to investigate water adsorption kinetics, capacities, and cycling stability.