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Current techniques for chromosome analysis need to be improved for rapid, economical identification of complex chromosomal defects by sensitive and selective visualisation. In this paper, we present a straightforward method for characterising unstained human metaphase chromosomes. Backscatter imaging in a dark-field setup combined with visible and short near-infrared spectroscopy is used to monitor morphological differences in the distribution of the chromosomal fine structure in human metaphase chromosomes. The reasons for the scattering centres in the fine structure are explained. Changes in the scattering centres during preparation of the metaphases are discussed. FDTD simulations are presented to substantiate the experimental findings. We show that local scattering features consisting of underlying spectral modulations of higher frequencies associated with a high variety of densely packed chromatin can be represented by their scatter profiles even on a sub-microscopic level. The result is independent of the chromosome preparation and structure size. This analytical method constitutes a rapid, costeffective and label-free cytogenetic technique which can be used in a standard light microscope.
Unter der Zielsetzung der multimodalen, ortsaufgelösten optischen Spektroskopie für die markierungsfreie Charakterisierung biologischer Materialien nach Morphologie und Chemie werden vier Themenschwerpunkte behandelt.
1. Theorie der elastischen / inelastischen Lichtstreuung und laterale Auflösung in der Mikroskopie
2. Erweiterung eines Raman Mikroskops zu einem multimodalen spektralen Imaging System (MSIS) mit Photonenmigrations-Technologie
3. Erweiterung des MSIS zu Super-Resolution Raman Mikroskopie mit einer Festkörper-Immersionslinse
4. Anwendung des entwickelten MSIS auf biologische Materialien
Newly developed active pharmaceutical ingredients (APIs) are often poorly soluble in water. As a result the bioavailability of the API in the human body is reduced. One approach to overcome this restriction is the formulation of amorphous solid dispersions (ASDs), e.g., by hot-melt extrusion (HME). Thus, the poorly soluble crystalline form of the API is transferred into a more soluble amorphous form. To reach this aim in HME, the APIs are embedded in a polymer matrix. The resulting amorphous solid dispersions may contain small amounts of residual crystallinity and have the tendency to recrystallize. For the controlled release of the API in the final drug product the amount of crystallinity has to be known. This review assesses the available analytical methods that have been recently used for the characterization of ASDs
and the quantification of crystalline API content. Well established techniques like near- and mid-infrared spectroscopy (NIR and MIR, respectively), Raman spectroscopy, and emerging ones like UV/VIS, terahertz, and ultrasonic spectroscopy are considered in detail. Furthermore, their advantages and limitations are discussed with regard to general practical applicability as process analytical technology (PAT) tools in industrial manufacturing. The review focuses on spectroscopic methods which have been proven as most suitable for in-line and on-line process analytics. Further aspects are spectroscopic techniques that have been or could be integrated into an extruder.
Different sensor types using chemical and biochemical principles are described. The former are mainly gas sensors, the latter are applied especially to liquids. Those label-free direct detection methods are compared with applications where assays take advantage of labeled receptors.
Furthermore, selected applications in the area of gas sensors are discussed, and sensors for process control, point-of-care diagnostics, environmental analytics, and food analytics are reviewed. In addition, multiplexing approaches used in microplates and microarrays are described.
On account of the huge number of sensor types and the wide range of possible applications, only the most important ones are selected here.
This paper presents an approach for label-free brain tumor tissue typing. For this application, our dual modality microspectroscopy system combines inelastic Raman scattering spectroscopy and Mie elastic light scattering spectroscopy. The system enables marker-free biomedical diagnostics and records both the chemical and morphologic changes of tissues on a cellular and subcellular level. The system setup is described and the suitability for measuring morphologic features is investigated.
Due to the wide variety of benign and malignant salivary gland tumors, classification and malignant behavior determination based on histomorphological criteria can be difficult and sometimes impossible. Spectroscopical procedures can acquire molecular biological information without destroying the tissue within the measurement processes. Since several tissue preparation procedures exist, our study investigated the impact of these preparations on the chemical composition of healthy and tumorous salivary gland tissue by Fourier-transform infrared (FTIR) microspectroscopy. Sequential tissue cross-sections were prepared from native, formalin-fixed and formalin-fixed paraffin-embedded (FFPE) tissue and analyzed. The FFPE cross-sections were dewaxed and remeasured. By using principal component analysis (PCA) combined with a discriminant analysis (DA), robust models for the distinction of sample preparations were built individually for each parotid tissue type. As a result, the PCA-DA model evaluation showed a high similarity between native and formalin-fixed tissues based on their chemical composition. Thus, formalin-fixed tissues are highly representative of the native samples and facilitate a transfer from scientific laboratory analysis into the clinical routine due to their robust nature. Furthermore, the dewaxing of the cross-sections entails the loss of molecular information. Our study successfully demonstrated how FTIR microspectroscopy can be used as a powerful tool within existing clinical workflows.
The early detection of head and neck cancer is a prolonged challenging task. It requires a precise and accurate identification of tissue alterations as well as a distinct discrimination of cancerous from healthy tissue areas. A novel approach for this purpose uses microspectroscopic techniques with special focus on hyperspectral imaging (HSI) methods. Our proof-of-principle study presents the implementation and application of darkfield elastic light scattering spectroscopy (DF ELSS) as a non-destructive, high-resolution, and fast imaging modality to distinguish lingual healthy from altered tissue regions in a mouse model. The main aspect of our study deals with the comparison of two varying HSI detection principles, which are a point-by-point and line scanning imaging, and whether one might be more appropriate in differentiating several tissue types. Statistical models are formed by deploying a principal component analysis (PCA) with the Bayesian discriminant analysis (DA) on the elastic light scattering (ELS) spectra. Overall accuracy, sensitivity, and precision values of 98% are achieved for both models whereas the overall specificity results in 99%. An additional classification of model-unknown ELS spectra is performed. The predictions are verified with histopathological evaluations of identical HE-stained tissue areas to prove the model’s capability of tissue distinction. In the context of our proof-of-principle study, we assess the Pushbroom PCA-DA model to be more suitable for tissue type differentiations and thus tissue classification. In addition to the HE-examination in head and neck cancer diagnosis, the usage of HSI-based statistical models might be conceivable in a daily clinical routine.
The article analyzes experimentally and theoretically the influence of microscope parameters on the pinhole-assisted Raman depth profiles in uniform and composite refractive media. The main objective is the reliable mapping of deep sample regions. The easiest to interpret results are found with low magnification, low aperture, and small pinholes. Here, the intensities and shapes of the Raman signals are independent of the location of the emitter relative to the sample surface. Theoretically, the results can be well described with a simple analytical equation containing the axial depth resolution of the microscope and the position of the emitter. The lower determinable object size is limited to 2–4 μm. If sub-micrometer resolution is desired, high magnification, mostly combined with high aperture, becomes necessary. The signal intensities and shapes depend now in refractive media on the position relative to the sample surface. This aspect is investigated on a number of uniform and stacked polymer layers, 2–160 μm thick, with the best available transparency. The experimental depth profiles are numerically fitted with excellent accuracy by inserting a Gaussian excitation beam of variable waist and fill fraction through the focusing lens area, and by treating the Raman emission with geometric optics as spontaneous isotropic process through the lens and the variable pinhole, respectively. The intersectional area of these two solid angles yields the leading factor in understanding confocal (pinhole-assisted) Raman depth profiles.
We report on the reflectance, transmittance and fluorescence spectra (λ=200–1200nm) of four types of chicken eggshells (white, brown, light green, dark green) measured in situ without pretreatment and after ablation of 20–100 μm of the outer shell regions. The color pigment protoporphyrin IX (PPIX) is embedded in the protein phase of all four shell types as highly fluorescent monomers, in the white and light green shells additionally as non-fluorescent dimers, and in the brown and dark green shells mainly as non-fluorescent poly-aggregates. The green shell colors are formed from an approximately equimolar mixture of PPIX and biliverdin. The axial distribution of protein and color pigments were evaluated from the combined reflectances of both the outer and inner shell surfaces, as well as from the transmittances. For the data generation we used the radiative transfer model in the random walk and Kubelka-Munk approaches.