178 research outputs found
Analytica—A Journal of Analytical Chemistry and Chemical Analysis
No full textBack in 1894, Wilhelm Ostwald defined analytical chemistry as “the art of recognizing different substances and determining their constituents”, which “occupies a prominent position among the applications of science, since the questions it allows us to answer arise wherever chemical processes are used for scientific or technical purposes”. In 1993, the Working Party of Analytical Chemistry (WPAC), held in Edinburgh, UK, stated that analytical chemistry “is that scientific discipline that develops and applies methods, tools and strategies to obtain information on the composition and nature in space and time”.
Nowadays, these definitions remain very modern and, above all, they are reflected in an uncountable number of application sectors, ranging from biology, geology, environmental sciences, agricultural chemistry, physics, engineering, medicine, and materials science, to social sciences and, of course, in chemistry itself. Often, this discipline is relegated into a corner and considered merely a support for other subjects; this, however, is without realizing that without it and, above all, without its principles, theories, and tools, nothing could make any applicative or rational sense
Preliminary evaluation of an automated autoencoder-UNet pipeline for chemical image segmentation and compression with reference to serial ground truth pathology
No full textThe rapid advancement of imaging technologies in pathology has ushered in an era of data-intensive diagnostic workflows, generating large volumes of data that demand sophisticated segmentation and compression techniques. Chemical imaging approaches offer an all-digital objective approach to pathological analysis, though image segmentation is required for efficient computation. Convolutional autoencoders are highly connected deep learning networks which can learn salient features within imaging data for the purposes of compression, data recovery, development of classifiers and/or segmentation. In this study an objective analysis of a U-Net convolutional autoencoders for unsupervised image segmentation is conducted with respect to haematoxylin-eosin based ground-truth diagnostic pathology. We find that a light-weight network architecture may provide a suitable segmentation approach for chemical imaging
Micro-Raman Spectroscopy: Theory and Application/ Jürgen Popp, Thomas Mayerhöfer.
No full textIn English.Includes bibliographical references and index.Micro-Raman Spectroscopy introduces readers to the theory and application of Raman microscopy. Raman microscopy is used to study the chemical signature of samples with little preperation in a non-destructive manner. An easy to use technique with ever increasing technological advances, Micro-Raman has significant application for researchers in the fields of materials science, medicine, pharmaceuticals, and chemistry.Cialla-May, Dana / Schmitt, Michael / Popp, Jürgen -- Bettignies, Philippe de -- Jahn, Izabella Jolan / Lehniger, Lydia / Weber, Karina / Cialla-May, Dana / Popp, Jürgen -- Ryabchykov, Oleg / Guo, Shuxia / Bocklitz, Thomas -- Jung, Nathalie / Windbergs, Maike -- Lankers, Markus -- Krafft, Christoph / Popp, Jürgen -- Rousaki, Anastasia / Moens, Luc / Vandenabeele, Peter -- Fikiet, Marisia A. / Khandasammy, Shelby R. / Mistek, Ewelina / Ahmed, Yasmine / Halámková, Lenka / Bueno, Justin / Lednev, Igor K. -- Frontmatter -- Contents -- List of contributing authors -- 1. Theoretical principles of Raman spectroscopy / 2. Optics/instrumentation / 3. Sample preparation for Raman microspectroscopy / 4. Analyzing Raman spectroscopic data / 5. Raman spectroscopy in pharmaceutical research and industry / 6. Applications in: Environmental Analytics (fine particles) / 7. Micro-Raman spectroscopy in medicine / 8. Archaeological investigations (archaeometry) / 9. Forensics: evidence examination via Raman spectroscopy / Index1 online resource (XII, 219 pages
Entropy-based spatial heterogeneity analysis in pathological images for diagnostic applications
No full textClinical pathological diagnosis and prognosis for cancer is often confounded by spatial tissue heterogeneity. This study investigates the utility of entropy as a robust quantitative metric of spatial disorder within Fourier Transform Infrared (FTIR) chemical images of breast cancer tissue. The use of entropy is grounded in its capacity to encapsulate the complexities of pixel-wise spectral intensity distributions, thus providing a detailed assessment of the spatial variations in biochemistry within tissue samples. Here we explore the use of Shannon’s entropy as a single image-based metric of spectral biochemical heterogeneity within FTIR chemical images of breast cancer tissue. This metric was then analyzed statistically with respect to hormone receptor status. Our results suggest that while entropy effectively captures the heterogeneity of tissue samples, its role as a standalone predictor for diagnostic subtyping may be limited without considering additional variables or interaction effects. This work emphasizes the need for a multifaceted approach in leveraging entropy with chemical imaging for diagnostic subtyping in cancer
Development and characterization of a microscope based on pump-probe spectroscopy: a valuable tool for the study of photoactivated drugs in cellulo
No full textThis thesis presents the construction and characterization of a microscope based on pumpprobe spectroscopy and its application to observe and study the evolution of photoinduced reactions. As compared to the typical pump-probe systems reported in the literature, which utilizes either 1 kHz or 80 MHz pulse-trains, the developed microscope was fed by pump pulse-trains at 125 kHz and probe pulse-trains at 250 kHz repetition rates in order to perform transient absorption microscopy (TAM). The use of such intermediate repetition rate reduces the unnecessary exposure of the sample to input field leading to risk of photo-induced damage and it also relaxes the use of lock-in detection, an unavoidable element for any MHz system, which increases the cost and complexity of the system. This gives the possibility to perform microspectroscopic studies of micro-structures with minimal photo-induced damage, a property that is especially important to the study of biological samples, without the incorporation of lock-in detection. Therefore, the developed TAM system by itself is a proof of concept.In der vorliegenden Arbeit wurde die Konstruktion, Wirkungsweise und Anwendung eines mikroskopgestützten Pump-Probe Spektroskops Mikroskops sowie dessen Einsatz zu Lokalisierung und Beobachtung photoneninduzierter Reaktionen beschrieben. Sie soll sowohl als Einführung in die Pump-Probe Mikroskopie dienen als auch die Möglichkeiten dieser Technik herausstellen, welche Wissenschaftlern neue Sichtweisen auf ultraschnelle photonenindizierte Prozesse sowie Informationen zur Verteilung von medizinischen Wirkstoffen innerhalb einer Zelle liefern könnten. Dies kann ein Hinweis darauf liefern, wie sich ein untersuchtes Medikament in der Zelle bzw. des jeweiligen Zellbestandteils verhält. Ein solches mikrospektroskopisches
Werkzeug ermöglicht nun die Charakterisierung von ausgewählten Molekülen in cellulo bezüglich ihrer photo-physikalischen Eigenschaften. Es wurde gezeigt, dass durch die spezifischen Eigenschaften der Pump-Probe Mikroskopie die Möglichkeit besteht die zeitliche Entwicklung von photoaktivierter Stoffe mit beugungsbegrenzter Auflösung zu
untersuchen
Auf künstlicher Intelligenz basierende Technologien für biophotonische Daten
No full textFor decades, biophotonic technologies have been booming in various fields of sciences. These technologies reveal not only structural but also molecular and functional changes in the sample under investigation. Additionally, they have prominent advantages such as high molecular sensitivity, high usability, high compactness, and high spatial and temporal resolution. Due to these advantages, biophotonic technologies have great potential in clinical applications. Nowadays, researchers emphasize the use of biophotonic technologies for point-of-care testing in clinics and the in vivo imaging of live cells for automating the disease diagnosis workflow. Furthermore, researchers are also focusing on integrating multiple biophotonic technologies in a single unit for understanding diseases at the cellular, molecular, and tissue level. Such ever-increasing developments in biophotonic technologies result in a massive amount of biophotonic data, and analysis of large biophotonic data by a human being is challenging. Therefore, algorithms that can automatically analyze biophotonic data to extract useful "patterns" like an experienced person are crucial. Extracting patterns from data using algorithms which can imitate human intelligence by learning from the data itself is categorized into a field of "artificial intelligence" (AI). Utilizing AI to analyze data from biophotonic technologies like Raman spectroscopy, coherent anti-Stokes Raman scattering (CARS) microscopy, two-photon excitation fluorescence (TPEF) microscopy, and second-harmonic generation (SHG) microscopy is the main highlight of this thesis. Concisely, this thesis will use AI and biophotonic data for biomedical applications like the prediction of disease, segmentation of various regions in tissue, and transformation of one modality into another modality. The results in this thesis will show that utilizing AI, along with biophotonic technologies, can benefit the field of biomedicine and the life sciences
Inverse and forward modeling tools for biophotonic data
No full textBiophotonic data require specific treatments due to the difficulty of directly extracting information from them. Therefore, artificial intelligence tools including machine learning and deep learning brought into play. These tools can be grouped into inverse modeling, preprocessing and data modeling categories. In each of these three categories, one research question was investigated. First, the aim was to develop a method that can acquire the Raman-like spectra from coherent anti-Stokes Raman scattering (CARS) spectra without apriori knowledge. In general, CARS spectra suffer from the non-resonant background (NRB) contribution, and existing methods were commonly implemented to remove it. However, these methods were not able to completely remove the NRB and need additional preprocessing afterward. Therefore, deep learning via the long-short-term memory network was applied and outperformed these existing methods. Then, a denoising technique via deep learning was developed for reconstructing high-quality (HQ) multimodal images (MM) from low-quality (LQ) ones. Since the measurement of HQ MM images is time-consuming, which is impractical for clinical applications, we developed a network, namely incSRCNN, to directly predict HQ images using only LQ ones. This network shows better performance when compared with standard methods. Finally, we intended to improve the accuracy of the classification model in particular when LQ Raman data or Raman data with varying quality are obtained. Therefore, a novel method based on functional data analysis was implemented, which converts the Raman data into functions and then applies functional dimension reduction followed by a classification method. The results showed better performance for the functional approach in comparison with the classical method
Investigations on chemometric approaches for diagnostic applications utilizing various combinations of spectral and image data types
No full textIn the presented work, several data fusion and machine learning approaches were explored within the frame of the data combination for various measurement techniques in biomedical applications. For each of the measurement techniques used in this work, the data was ana-lyzed by means of machine learning. Prior to applying these machine learning algorithms, a specific preprocessing pipeline for each type of data had to be established. These pipelines made it possible to standardize the data and to decrease sample-to-sample variations which originate from the instability of devices or small deviations in the sample preparation or measurement routine. The preprocessed data sets were used for various analyses of biological samples. Separate data analyses were performed for microscopic images, Raman spectra, and SERS data. However, this work mainly focused on the application of data fusion methods for the analy-sis of biological tissues and cells. To do so, different data fusion pipelines were constructed for each task, depending on the data structure. Both low-level (centralized) and high-level (distributed) data fusion approaches were tested and investigated within in this work. To demonstrate centralized and distributed data fusion, two examples were implemented for tissue investigation. In both examples, a combination of Raman spectroscopic and MALDI spectrometric data were analyzed. One example demonstrated centralized data fusion for the analysis of the chemical composition of a mouse brain section, and the other example employed distributed data fusion for liver cancer detection. Other data fusion examples were demonstrated for cell-based analysis. It was demonstrated that leukocyte cell subtype identification can be improved by a centralized data fusion of Raman spectroscopic data and morphological features obtained from microscopic images of stained cells. The last example presented in this work demonstrated a sepsis diagnostic pipeline based on the combination of Raman spectroscopic data and biomarkers. Besides the measured values, the demographic information of the patient was included in the analysis process for considering non-disease-related variations. During the construction of data fusion pipelines, such issues as unbalanced data contribu-tion, missing values, and variations that are not related to the investigated responses were faced. To resolve these issues, data weighting, missing data imputation, and the introduc-tion of additional responses were employed. For further improvement of analysis reliability, the data fusion pipelines and data processing routine were adjusted for each study in this work. As a result, the most suitable data fusion approach was found for every example, and a combination of the machine learning methods with data fusion approaches was demon-strated as a powerful tool for data analysis in biomedical applications
Toward data science in biophotonics: biomedical investigations-based study
No full textBiophotonics aims to grasp and investigate the characteristics of biological samples based on their interaction with incident light. Over the past decades, numerous biophotonic technologies have been developed delivering various sorts of biological and chemical information from the studied samples. Such information is usually contained in high dimensional data that need to be translated into high-level information like disease biomarkers. This data translation is not straightforward, but it can be achieved using the advances in computer and data science. The scientific contributions presented in this thesis were established to cover two main aspects of data science in biophotonics: the design of experiments and the data-driven modeling and validation. For the design of experiment, the scientific contributions focus on estimating the sample size required for group differentiation and on evaluating the influence of experimental factors on unbalanced multifactorial designs. Both methods were designed for multivariate data and were checked on Raman spectral datasets. Thereafter, the automatic detection and identification of three diagnostic tasks were checked based on combining several image processing techniques with machine learning (ML) algorithms. In the first task, an improved ML pipeline to predict the antibiotic susceptibilities of E. coli bacteria was presented and evaluated based on bright-field microscopic images. Then, transfer learning-based classification of bladder cancer was demonstrated using blue light cystoscopic images. Finally, different ML techniques and validation strategies were combined to perform the automatic detection of breast cancer based on a small-sized dataset of nonlinear multimodal images. The obtained results exhibited the benefits of data science tools in improving the experimantal planning and the translation of biophotonic-associated data into high-level information for various biophotonic technologies
Natural NADH and FAD Autofluorescence as Label-Free Biomarkers for Discriminating Subtypes and Functional States of Immune Cells
No full text1 ZUSAMMENFASSUNG
Die Publikation mit dem Titel Natural NADH and FAD Autofluorescence as Label-Free Biomarkers for Discriminating Subtypes and Functional States of Immune Cells (Natürlich vorkommende Autofluoreszenzsignale von NADH und FAD als Label-Freie Biomarker zur Unterscheidung von Subtypen und funktionellen Zuständen von Im-munzellen) wurde am 20. Februar 2022 im Wissenschaftsjournal International Jour-nal of Molecular Sciences (Special Issue Solving the Puzzle: Molecular Research in Inflammatory Bowel Diseases) veröffentlicht.
1.1 Hintergrund und Ziele
Immunzellen spielen eine entscheidende Rolle in der Entstehung und Aufrechterhal-tung von chronisch entzündlichen Darmerkrankungen (CED). Sie sind daher wesent-liche klinisch-diagnostische Parameter zur Beurteilung von Art, Ausmaß und Fort-schreiten der Erkrankung. In diesem Zusammenhang könnten neue optische Tech-nologien, wie die Multiphotonenmikroskopie, klinische Relevanz erlangen, da sie na-türlich vorkommende, label-freie Autofluoreszenzsignale der Stoffwechselmoleküle NADH und FAD diagnostisch nutzbar machen können. Vielversprechende Erfolge dieser Technologie konnten bei der Evaluation von entzündetem Darmgewebe ge-zeigt werden. Allerdings war es bisher nicht möglich, einzelne Immunzellen innerhalb dieses Gewebes zu identifizieren. Ziel dieser Arbeit war es daher, NADH- und FAD-Autofluoreszenzsignale von Immunzellen mit Multiphotonenmikroskopie und Durch-flusszytometrie zu erfassen, um damit verschiedene Typen von Immunzellen in un-stimuliertem Zustand unterscheiden zu können. Außerdem sollten Auswirkungen von in-vitro Stimulation und Zelltod auf die Autofluoreszenzsignale untersucht werden.
1.2 Methoden
Sechs verschiedene Immunzelltypen wurden aus der Milz (CD4+ / CD8+ T-Zellen, B-Zellen) und dem Knochenmark (neutrophile Granulozyten, Makrophagen, dendriti-sche Zellen) von Wildtyp C57BL/6 Mäusen isoliert. Alle Zelltypen wurden sowohl in nativem Zustand als auch nach in-vitro Stimulation und im Zelltod untersucht. Auto-fluoreszenzsignale von NADH und FAD wurden mit den beiden unterschiedlichen Methoden Multiphotonenmikroskopie und Durchflusszytometrie gemessen. Für die statistische Auswertung wurde das Programm GraphPad verwendet.
1.3 Ergebnisse und Beobachtungen
Im Vergleich der sechs unstimulierten Zelltypen wurden signifikante Unterschiede zwischen den Zellen des angeborenen und des erworbenen Immunsystems beo-bachtet. So zeigten neutrophile Granulozyten, Makrophagen und dendritische Zellen sowohl in den Messungen der Multiphotonenmikroskopie als auch in der Durch-flusszytometrie höhere Werte für NADH und FAD. Dies ermöglichte, in einer ge-mischten Zellsuspension von CD4+ T-Zellen und neutrophilen Granulozyten die An-teile dieser beiden Zelltypen allein anhand ihrer NADH-Signale zu bestimmen. In-vitro Stimulation erhöhte signifikant NADH- und FAD- Autofluoreszenzwerte in den Zellen des adaptiven Immunsystems sowie in Makrophagen. In dendritischen Zellen und neutrophilen Granulozyten wurde kein wesentlicher Effekt der in-vitro Stimulation beobachtet. In der Durchflusszytometrie konnte gezeigt werden, dass Zelltod die NADH-Signale signifikant erniedrigt, während die FAD-Signale gleichblieben.
1.4 Schlussfolgerungen und Diskussion
Mit dieser Arbeit konnte gezeigt werden, dass Autofluoreszenzsignale einzelner Im-munzellen mithilfe von Multiphotonenmikroskopie und Durchflusszytometrie evaluiert werden können. Zelltyp, Stimulationszustand und Zelltod wurden in dieser präklini-schen Studie als wichtige Einflussfaktoren auf die Stärke der Autofluoreszenz identi-fiziert und können damit in der immunologischen Charakterisierung mukosaler Ent-zündung eine wichtige Rolle spielen. So könnten beispielsweise verschiedene Sub-typen, sowie aktivierte oder tote Immunzellen in der Darmschleimhaut allein durch veränderte Autofluoreszenzsignale erkannt werden. Langfristig ergibt sich mithilfe dieser Technik eine interessante klinische Anwendungsmöglichkeit in der Diagnostik, Aktivitätsbeurteilung und Therapieüberwachung von Patienten mit CED. Da viele ak-tuell genutzte Therapeutika auf die Aktivität von Immunzellen Einfluss nehmen, könn-te die Messung von Autofluoreszenzsignalen in der Darmschleimhaut in Zukunft eine elegante Methode zur Kontrolle des Therapieerfolges sein. Um diese Ziele zu errei-chen ist allerdings noch weitere Forschung nötig. Diese könnte zum Beispiel die Ana-lyse von Immunzellen in Biopsien von entzündetem Darmgewebe mittels Autofluo-reszenz beinhalten. Langfristig wären auch KI-Systeme denkbar, die allein anhand von Autofluoreszenzsignalen in der Lage wären, einzelne Immunzellen im Darmge-webe zu identifizieren und in ihrem Stimulationsstatus zu beschreiben. Dies könnte die klinische Diagnostik von CED erheblich vereinfachen
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