Suitability of Fourier transform infrared microscopy for the diagnosis of cystic echinococcosis in human tissue sections.

Cystic echinococcosis (CE) is a global health concern caused by cestodes, posing diagnostic challenges due to nonspecific symptoms and inconclusive radiographic results. Diagnosis relies on histopathological evaluation of affected tissue, demanding comprehensive tools. In this retrospective case study, Fourier transform infrared microscopy was explored for detecting and identifying CE through biochemical changes in human tissue sections. Tissue samples from 11 confirmed CE patients were analyzed. Archived FFPE blocks were cut and stained, and then CE-positive unstained sections were examined using Fourier transform infrared microscopy post-deparaffinization. Results revealed the method's ability to distinguish echinococcus elements from human tissue, irrespective of organ type. This research showcases the potential of mid-infrared microscopy as a valuable diagnostic tool for CE, offering promise in enhancing diagnostic precision in the face of the disease's complexities.

Deep learning analysis of mid-infrared microscopic imaging data for the diagnosis and classification of human lymphomas.

The present study presents an alternative analytical workflow that combines mid-infrared (MIR) microscopic imaging and deep learning to diagnose human lymphoma and differentiate between small and large cell lymphoma. We could show that using a deep learning approach to analyze MIR hyperspectral data obtained from benign and malignant lymph node pathology results in high accuracy for correct classification, learning the distinct region of 3900 to 850 cm-1 . The accuracy is above 95% for every pair of malignant lymphoid tissue and still above 90% for the distinction between benign and malignant lymphoid tissue for binary classification. These results demonstrate that a preliminary diagnosis and subtyping of human lymphoma could be streamlined by applying a deep learning approach to analyze MIR spectroscopic data.

Visible- and near-infrared hyperspectral imaging for the quantitative analysis of PD-L1+ cells in human lymphomas: Comparison with fluorescent multiplex immunohistochemistry.

INTRODUCTION: We analyzed the expression of PD-L1 in human lymphomas using hyperspectral imaging (HSI) compared to visual assessment (VA) and conventional digital image analysis (DIA) to strengthen further the value of HSI as a tool for the evaluation of brightfield-based immunohistochemistry (IHC). In addition, fluorescent multiplex immunohistochemistry (mIHC) was used as a second detection method to analyze the impact of a different detection method. MATERIAL AND METHODS: 18 cases (6 follicular lymphomas and 12 diffuse large B-cell lymphomas) were stained for PD-L1 by IHC and for PD-L1, CD3, and CD8 by fluorescent mIHC. The percentage of positively stained cells was evaluated with VA, HSI, and DIA for IHC and VA and DIA for mIHC. Results were compared between the different methods of detection and analysis. RESULTS: An overall high concordance was found between VA, HSI, and DIA in IHC (Cohens Kappa = 0.810VA/HSI, 0.710 VA/DIA, and 0.516 HSI/DIA) and for VAmIHCversus DIAmIHC (Cohens Kappa = 0.894). Comparing IHC and mIHC general agreement differed depending on the methods compared but reached at most a moderate agreement (Cohens Kappa between 0.250 and 0.483). This is reflected by the significantly higher percentage of PD-L1+ cells found with mIHC (pFriedman = 0.014). CONCLUSION: Our study shows a good concordance for the different analysis methods. Compared to VA and DIA, HSI proved to be a reliable tool for assessing IHC. Understanding the regulation of PD-L1 expression will further enlighten the role of PD-L1 as a biomarker. Therefore it is necessary to develop an instrument, such as HSI, which can offer a reliable and objective evaluation of PD-L1 expression.

Raman microscopic spectroscopy as a diagnostic tool to detect Staphylococcus epidermidis in bone grafts.

INTRODUCTION: Raman microscopic spectroscopyis a new approach for further characterization and detection of molecular features in many pathological processes. This technique has been successfully applied to scrutinize the spatial distribution of small molecules and proteins within biological systems by in situ analysis. This study uses Raman microscopic spectroscopyto identify any in-depth benefits and drawbacks in diagnosing Staphylococcus epidermidis in human bone grafts. MATERIAL AND METHODS: 40 non-infected human bone samples and 10 human bone samples infected with Staphylococcus epidermidis were analyzed using Raman microscopic spectroscopy. Reflectance data were collected between 200 cm-1 and 3600 cm-1 with a spectral resolution of 4 cm-1 using a Senterra II microscope (Bruker, Ettlingen, Germany). The acquired spectral information was used for spectral and unsupervised classification, such as principal component analysis. RESULTS: Raman measurements produced distinct diagnostic spectra that were used to distinguish between non-infected human bone samples and Staphylococcus epidermidis infected human bone samples by spectral and principal component analyses. A substantial loss in bone quality and protein conformation was detected by human bone samples co-cultured with Staphylococcus epidermidis. The mineral-to-matrix ratio using the phosphate/Amide I ratio (p = 0.030) and carbonate/phosphate ratio (p = 0.001) indicates that the loss of relative mineral content in bones upon bacterial infection is higher than in non-infected human bones. Also, an increase of alterations in the collagen network (p = 0.048) and a decrease in the structural organization and relative collagen in infected human bone could be detected. Subsequent principal component analyses identified Staphylococcus epidermidis in different spectral regions, respectively, originating mainly from CH2 deformation (wagging) of protein (at 1450 cm-1) and bending and stretching modes of C-H groups (∼2800-3000 cm-1). CONCLUSION: Raman microscopic spectroscopyis presented as a promising diagnostic tool to detect Staphylococcus epidermidis in human bone grafts. Further studies in human tissues are warranted.

Visible and Near-Infrared hyperspectral imaging (HSI) can reliably quantify CD3 and CD45 positive inflammatory cells in myocarditis: Pilot study on formalin-fixed paraffin-embedded specimens from myocard obtained during autopsy.

INTRODUCTION: To implement Hyperspectral Imaging (HSI) as a tool for quantifying inflammatory cells in tissue specimens by the example of myocarditis in a collective of forensic patients. MATERIAL AND METHODS: 44 consecutive patients with suspected myocardial inflammation at autopsy, diagnosed between 2013 and 2018 at the Institute of ForensicMedicine, Medical University of Innsbruck, were selected for this study. Using the IMEC SNAPSCAN camera, visible and near infrared hyperspectral images were collected from slides stained with CD3 and CD45 to assess quantity and spatial distribution of positive cells. Results were compared with visual assessment (VA) and conventional digital image analysis (DIA). RESULTS: Finally, specimens of 40 patients were evaluated, of whom 36 patients (90%) suffered from myocarditis, two patients (5%) had suspected healing/healed myocarditis, and two did no have myocarditis (5%). The amount of CD3 and CD45 positive cells did not differ significantly between VA, HSI, and DIA (pVA/HSI/DIA = 0.46 for CD3 and 0.81 for CD45). Cohens Kappa showed a very high correlation between VA versus HSI, VA versus DIA, and HSI versus DIA for CD3 (Cohens Kappa = 0.91, 1.00, and 0.91, respectively). For CD45 an almost as high correlation was seen for VA versus HSI and HSI versus DIA (Cohens Kappa = 0.75 and 0.70) and VA versus DIA (Cohens Kappa = 0.89). CONCLUSION: HSI is a reliable and objective method to count inflammatory cells in tissue slides of suspected myocarditis. Implementation of HSI in digital pathology might further expand the possibility of a sophisticated method.

Vibrational spectroscopic methods for the overall quality analysis of washing powders.

The aim of this study was to compare and evaluate the ability of near infrared- (NIR), Raman- and attenuated-total-reflection infrared (ATR-IR) spectroscopy as tools for the identification of washing powder brands as well as for an overall quantitative analysis of all ingredients of the analyzed laundry detergents. The laundry detergents used in this work were composed of 22 different ingredients. For this purpose, principal component analysis (PCA) cluster models and partial least-squares (PLS) regression models were developed and different data pre-processing algorithms such as standard-normal-variate (SNV), multiplicative scatter correction (MSC), first derivative BCAP (db1), second derivative smoothing (ds2), smoothing Savitzky Golay 9 points (sg9) as well as different normalization procedures such as normalization between 0 and 1 (n01), normalization unit length (nle) or normalization by closure (ncl) were applied to reduce the influence of systematic disturbances. The performance of the methods was evaluated by comparison of the number of principal components (PCs), regression coefficient (r), Bias, Standard error of prediction (SEP), ratio performance deviation (RPD) and range error ratio (RER) for each calibration model. For each of the 22 ingredients separate calibration models were developed. Raman spectroscopy was suitable for the analysis of only two ingredients (dye transfer inhibitor 1 and surfactant 6) and it was not possible to record all Raman spectra due to high fluorescence. NIR and ATR-IR are powerful methods to analyze washing detergents with low numbers of PCs being necessary, regression coefficients of only little below 1, small Biases and SEPs compared to the range and high RPDs and RERs.

Rapid determination of baicalin and total baicalein content in Scutellariae radix by ATR-IR and NIR spectroscopy.

In this study methods for the quantification of baicalin and total baicalein in Scutellariae radix with near infrared (NIR) spectroscopy and attenuated-total-reflectance mid-infrared (ATR-IR) spectroscopy in hyphenation with multivariate analysis were developed and compared. The reference analysis was performed by high performance liquid chromatography coupled to diode array detection (HPLC-DAD). Different pretreatments like standard normal variate (SNV), multiplicative scatter correction (MSC), first and second derivative Savitzky-Golay were applied on the spectra to optimize the calibrations. A principal component analysis was performed with both spectroscopic methods to distinguish wild and cultivated samples. Quality parameters obtained for test-set calibration models of ATR-IR spectroscopy (baicalin: standard error of prediction (SEP)=1.31, ratio performance to deviation (RPD)=2.91 and R(2)=0.88; total baicalein: SEP=1.02, RPD=3.24 and R(2)=0.89) and NIR spectroscopy (baicalin: SEP=1.50, RPD=2.54 and R(2)=0.88; total baicalein: SEP=1.19, RPD=2.76 and R(2)=0.84) demonstrate that both spectroscopic techniques in combination with multivariate analysis are successful tools for the quantification of baicalin and total baicalein in Scutellariae radix, but it was found that ATR-IR spectroscopy provides higher accuracy in the given application. Furthermore it was proved that wild and cultivated samples can be distinguished by ATR-IR.

Simultaneous quantification of verbenalin and verbascoside in Verbena officinalis by ATR-IR and NIR spectroscopy.

Attenuated-total-reflectance infrared spectroscopy (ATR-IR) and near-infrared diffuse reflectance spectroscopy (NIR) in hyphenation with multivariate analysis was utilized to quantify verbenalin and verbascoside in Verbena officinalis. A new high performance liquid chromatography (HPLC) method as a reference was established and validated. For both vibrational spectroscopic methods test-set and cross validation were performed. Different data-pre-treatments like SNV, 1st and 2nd derivative were applied to remove systematic errors and were evaluated. Quality parameters obtained for the test-set validation revealed that ATR-IR (verbenalin: R(2)=0.94, RPD=4.23; verbascoside: R(2)=0.93, RPD=3.63) has advantages over NIR (verbenalin: R(2)=0.91, RPD=3.75; verbascoside: R(2)=0.80, RPD=2.35) in the given application.

Fourier transform infrared imaging analysis in discrimination studies of bladder cancer.

Bladder carcinoma represents more than 4% of all cancer diseases in Austria. The histomorphological evaluation is invasive and remains a subjective and time consuming technique. On account of this it is necessary to find novel non-invasive approaches which support the pathologists for histological recognition to identify malignancy at an early stage. In the present study, Fourier transform infrared (FTIR) microscopic imaging was combined with univariate and multivariate data analysis methods to study bladder carcinoma tissue sections in detail. The possibility to collect IR spectra of bladder carcinoma tissue sections employing an optimized analytical protocol is demonstrated. The correlation between FTIR microscopic imaging and the morphological tissue features obtained by histological staining of the sections demonstrated that many histomorphological tissue patterns can be visualized in the colour images. The routine generation of high quality imaging data is enabled because of the combination of FTIR technology and optimized sample preparation techniques. This opens a new quality of spectroscopic analyses of cancerous tissue, allowing exploration of molecular changes associated with the histopathological morphology.

Comparison of NIR chemical imaging with conventional NIR, Raman and ATR-IR spectroscopy for quantification of furosemide crystal polymorphs in ternary powder mixtures.

The aim of this study was to evaluate the ability of near-infrared chemical imaging (NIR-CI), near-infrared (NIR), Raman and attenuated-total-reflectance infrared (ATR-IR) spectroscopy to quantify three polymorphic forms (I, II, III) of furosemide in ternary powder mixtures. For this purpose, partial least-squares (PLS) regression models were developed, and different data preprocessing algorithms such as normalization, standard normal variate (SNV), multiplicative scatter correction (MSC) and 1st to 3rd derivatives were applied to reduce the influence of systematic disturbances. The performance of the methods was evaluated by comparison of the standard error of cross-validation (SECV), R(2), and the ratio performance deviation (RPD). Limits of detection (LOD) and limits of quantification (LOQ) of all methods were determined. For NIR-CI, a SECVcorr-spec and a SECVsingle-pixel corrected were calculated to assess the loss of accuracy by taking advantage of the spatial information. NIR-CI showed a SECVcorr-spec (SECVsingle-pixel corrected) of 2.82% (3.71%), 3.49% (4.65%), and 4.10% (5.06%) for form I, II, III. NIR had a SECV of 2.98%, 3.62%, and 2.75%, and Raman reached 3.25%, 3.08%, and 3.18%. The SECV of the ATR-IR models were 7.46%, 7.18%, and 12.08%. This study proves that NIR-CI, NIR, and Raman are well suited to quantify forms I-III of furosemide in ternary mixtures. Because of the pressure-dependent conversion of form II to form I, ATR-IR was found to be less appropriate for an accurate quantification of the mixtures. In this study, the capability of NIR-CI for the quantification of polymorphic ternary mixtures was compared with conventional spectroscopic techniques for the first time. For this purpose, a new way of spectra selection was chosen, and two kinds of SECVs were calculated to achieve a better comparability of NIR-CI to NIR, Raman, and ATR-IR.

Fourier transform infrared imaging analysis in discrimination studies of St. John's wort (Hypericum perforatum).

In the present study, Fourier transform infrared (FTIR) imaging and data analysis methods were combined to study morphological and molecular patterns of St. John's wort (Hypericum perforatum) in detail. For interpretation, FTIR imaging results were correlated with histological information gained from light microscopy (LM). Additionally, we tested several evaluation processes and optimized the methodology for use of complex FTIR microscopic images to monitor molecular patterns. It is demonstrated that the combination of the used spectroscopic method with LM enables a more distinct picture, concerning morphology and distribution of active ingredients, to be gained. We were able to obtain high-quality FTIR microscopic imaging results and to distinguish different tissue types with their chemical ingredients.

Fourier transform infrared imaging analysis in discrimination studies of squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) of the oral cavity and oropharynx represents more than 95% of all malignant neoplasms in the oral cavity. Histomorphological evaluation of this cancer type is invasive and remains a time consuming and subjective technique. Therefore, novel approaches for histological recognition are necessary to identify malignancy at an early stage. Fourier transform infrared (FTIR) imaging has become an essential tool for the detection and characterization of the molecular components of biological processes, such as those responsible for the dynamic properties of tumor progression. FTIR imaging is a modern analytical technique enabling molecular imaging of a complex biological sample and is based on the absorption of IR radiation by vibrational transitions in covalent bonds. One major advantage of this technique is the acquisition of local molecular expression profiles, while maintaining the topographic integrity of the tissue and avoiding time-consuming extraction, purification, and separation steps. With this imaging technique, it is possible to obtain unique images of the spatial distribution of proteins, lipids, carbohydrates, cholesterols, nucleic acids, phospholipids, and small molecules with high spatial resolution. Analysis and visualization of FTIR imaging datasets are challenging and the use of chemometric tools is crucial in order to take advantage of the full measurement. Therefore, methodologies for this task based on the novel developed algorithm for multivariate image analysis (MIA) are often necessary. In the present study, FTIR imaging and data analysis methods were combined to optimize the tissue measurement mode after deparaffinization and subsequent data evaluation (univariate analysis and MIAs). We demonstrate that it is possible to collect excellent IR spectra from formalin-fixed paraffin-embedded (FFPE) tissue microarrays (TMAs) of OSCC tissue sections employing an optimised analytical protocol. The correlation of FTIR imaging to the morphological tissue features obtained by histological staining of the sections demonstrated that many histomorphological tissue patterns can be visualized in the colour images. The different algorithms used for MIAs of FTIR imaging data dramatically increased the information content of the IR images from squamous cell tissue sections. These findings indicate that intra-operative and surgical specimens of squamous cell carcinoma tissue can be characterized by FTIR imaging.

Selected Extracts of Chinese Herbal Medicines: Their Effect on NF-κB, PPARα and PPARγ and the Respective Bioactive Compounds.

Chinese herbal medicinal (CHM) extracts from fourteen plants were investigated in cell-based in vitro assays for their effect on nuclear factor κB (NF-κB), a key regulator of inflammation, as well as on peroxisome proliferator-activated receptors (PPARs) being key regulators of genes involved in lipid and glucose metabolism. 43% of the investigated CHMs showed NF-κB inhibitory and 50% PPARα and PPARγ activating effects. Apolar extracts from cortex and flos of Albizia julibrissin Durazz. and processed rhizomes of Arisaema sp. and Pinellia ternata (Thunb.) Breit. that effectively inhibited TNF-α-induced NF-κB activation and dose-dependently activated PPARα and PPARγ were further investigated. Bioassay-guided fractionation and analysis by GC-MS led to the identification of fatty acids as PPAR agonists, including linoleic and palmitic acid.

Near infrared spectroscopy patents for the physicochemical characterization of nanomaterials: the road from production to routine high-throughput quality control.

The measurement of the physical and chemical ("physicochemical") properties of nanomaterials used in industry and science including chemistry, pharmacy, medicine, toxicology, etc., is time-consuming, expensive and requires a lot of experience of a well trained lab staff. Near-infrared spectroscopy (NIR; 4.000-12.000 cm(-1)), working in the wavelength region with the highest IR energy, allows obtaining multifactorial information of the material under investigation due to the occurrence of a high number of combination and overtone vibrations. Coupling of an optimized and well-designed measurement technique with multivariate data analysis (MVA) leads to a non-destructive, fast, reliable and robust novel NIR technique for the fast and non-invasive physicochemical characterization, which is suitable for high-throughput quality control due to the short analyses times of only a few seconds. In the following chapters, the patented basic NIR techniques full-filling these aims are introduced, described, summarized and critically discussed.

Chemically modified diamond-like carbon (DLC) for protein enrichment and profiling by MALDI-MS.

The development of new high throughput methods based on different materials with chemical modifications for protein profiling of complex mixtures leads towards biomarkers; used particularly for early diagnosis of a disease. In this work, diamond-like carbon (DLC) is developed and optimized for serum protein profiling by matrix-assisted laser/desorption ionization mass spectrometry (MALDI-MS). This study is carried out in connection with a material-based approach, termed as material-enhanced laser desorption ionization mass spectrometry. DLC is selected as carrier surface which provides large surface to volume ratio and offers high sensitivity. DLC has a dual role of working as MALDI target while acting as an interface for protein profiling by specifically binding peptides and proteins out of serum samples. Serum constituents are bound through immobilized metal ion affinity chromatography (IMAC) functionality, created through glycidyl methacrylate polymerization under ultraviolet light followed by further derivatization with iminodiacetic acid and copper ion loading. Scanning electron microscopy highlights the morphological characteristics of DLC surface. It could be demonstrated that IMAC functionalized DLC coatings represent a powerful material in trapping biomolecules for their further analysis by MALDI-MS resulting in improved sensitivity, specificity and capacity in comparison to other protein-profiling methods.

Morphological and tissue characterization of the medicinal fungus Hericium coralloides by a structural and molecular imaging platform.

In this study the potential of new imaging techniques such as Magnetic Resonance Imaging (MRI), Matrix-Assisted Laser Desorption/Ionization (MALDI) profiling mass spectrometry ("MALDI Profiling") and Fourier Transform Infrared (FTIR) spectroscopic imaging was evaluated to study morphological and molecular patterns of the potential medicinal fungus Hericium coralloides. For interpretation, the MALDI profiling, FTIR imaging and MRI results were correlated with histological information gained from Scanning Electron Microscopy (SEM) and Light Microscopy (LM). Additionally we tested several evaluation processes and optimized the methodology for use of complex FTIR images to monitor molecular patterns. It is demonstrated that the combination of these spectroscopic methods enables to gain a more distinct picture concerning morphology and distribution of active ingredients. We were able to obtain high quality FTIR imaging and MALDI-profiling results and to distinguish different tissue types with their chemical ingredients. Beside this, we have created a 3-D reconstruction of a mature Hericium basidioma, based on the MRI dataset: analyses allowed, for the first time, a realistic approximation of the "evolutionary effectiveness" of this bizarrely formed basidioma type, concerning the investment of sterile tissue and its reproductive output (production of basidiospores).

Protein profiling for cancer biomarker discovery using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and infrared imaging: a review.

Cancer biomarker refers to a substance or process that is indicative of the presence of cancer in the body. A biomarker might be either a molecule secreted by a tumor or it can be a specific response of the body to the presence of cancer. Cancer biomarker-based diagnostics have applications for establishing disease predisposition, early detection, cancer staging, therapy selection, identifying whether or not a cancer is metastatic, therapy monitoring, assessing prognosis, and advances in the adjuvant setting. Full adoption of cancer biomarkers in the clinic has to date been slow, and only a limited number of cancer biomarker products are currently in routine use. Among proteomic technologies, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF/MS) is a technique that has allowed rapid progress in cancer biology. Different further developed methods including e.g. SELDI (surface-enhanced laser desorption/ionization) and MELDI (material-enhanced laser desorption/ionization) are simple and high-throughput techniques that analyze with high sensitivity and specificity intact proteins expressed in complex biological mixtures, such as serum, urine, and tissues. The combination of mass spectrometry (MS) with infrared (IR) spectroscopic imaging is an attempt to combine different technologies in systems analytics. Both MALDI-TOF and infrared tissue imaging enable studying proteins distribution in tissue samples with a resolution down to 50 and 5 µm, respectively. In this review, we summarize recent applications and the synergistic combination of these new technologies to proteomic profiling for cancer biomarker discovery.

Near-infrared reflection spectroscopy (NIRS) as a successful tool for simultaneous identification and particle size determination of amoxicillin trihydrate.

A successful application of NIR spectroscopy (NIRS) in combination with multivariate data analysis (MVA) for the simultaneous identification and particle size determination of amoxicillin trihydrate particles was developed. Particle size analysis was ascertained by NIRS in diffuse reflection mode on different particle size fractions of amoxicillin trihydrate with D90 particle diameters ranging from 6.9 to 21.7 µm. The present problem of fractionating the powder into good enough size fractions to achieve a stable calibration model was solved. By probing dried suspensions measurement parameters were optimized and further combined with the best suitable chemometric operations. Thereby the quality of established regression models could be improved considerably. A linear coherence between particle size and absorbance signal was found at specific wavenumbers. Satisfactory clustering by particle size was achieved by principal component analysis (PCA) whereas partial least squares regression (PLSR) and principal component regression (PCR) was compared for quantitatively calibrating the NIRS data. PLSR turned out to predict unknown test samples slightly better than PCR.

Thiolated polyacrylic acid-modified iron oxide nanoparticles for in vitro labeling and MRI of stem cells.

PURPOSE: The purpose of this study was to develop and characterize new surface-modified iron oxide nanoparticles demonstrating the efficiency to be internalized by human endothelial progenitor cells (EPCs) from umbilical cord blood. METHODS: Iron oxide nanoparticles were coated with polyacrylic acid-cysteine (PAA-Cys) by either in situ precipitation or postsynthesis. The nanoparticles were characterized by X-ray powder diffraction. EPCs were labeled with PAA-Cys-modified iron oxide nanoparticles or with uncoated nanoparticles. The relaxivity of uncoated and coated iron oxide nanoparticles as well as EPCs labeled with PAA-Cys-modified iron oxide were determined. RESULTS: Addition of PAA-Cys increased the particle size from 10.4 to 144 and 197 nm, respectively. The X-ray powder diffraction pattern revealed that the particles consist of Fe(3)O(4) with a spinal structure. Postsynthesis coated particles showed a cellular uptake of 85% and 15.26 pg iron/cell. For both types of particles the relaxivity ratio was at least 2-fold higher than that of the gold standard Resovist(®). CONCLUSION: The PAA-Cys coated iron oxide nanoparticles are a promising tool for labeling living cells such as stem cells for diagnostic and therapeutic application in cell-based therapies due to their high relaxivities and their easy uptake by cells.

Infrared-spectroscopy: a non-invasive tool for medical diagnostics and drug analysis.

Constant development enabled Infrared (IR) spectroscopy to become a widely used, non-invasive tool for fast sample analyses with less to no pre-preparation. Furthermore, computational data handling is no more a limiting factor and hence, IR measurements are predestined for clinical diagnostics and drug analysis. Within this review the focus was put on clinical topics of high interest. One example is Alzheimer's disease, where the exact metabolism is still not clarified, or blood glucose monitoring for high throughput screening of patients without taking any drop of blood. The second section of this manuscript was focused on the analysis of drugs. The detection of physico-chemical parameters in pharmaceutics and the improvement of industrial proceedings allowed a dramatic increase of quality of produced medicine. In pharmaceutical industries problems with the equable allocation of agents occurs especially in scaling up processes. IR-analyzing-techniques serve as fast and precise indicators for the detection of active components and their distribution in tablets. In combination with statistical factors and medical investigations pharmaceuticals can be improved from their development until their application, and every step can be easily controlled by IR spectroscopy.