Advancements in quantum cascade laser-based infrared microscopy of aqueous media.

The large mid-infrared absorption coefficient of water frequently hampers the rapid, label-free infrared microscopy of biological objects in their natural aqueous environment. However, the high spectral power density of quantum cascade lasers is shifting this limitation such that mid-infrared absorbance images can be acquired in situ within signal-to-noise ratios of up to 100. Even at sample thicknesses well above 50 µm, signal-to-noise ratios above 10 are readily achieved. The quantum cascade laser-based microspectroscopy of aqueous media is exemplified by imaging an aqueous yeast solution and quantifying glucose consumption, ethanol generation as well as the production of carbon dioxide gas during fermentation.

Rapid identification of goblet cells in unstained colon thin sections by means of quantum cascade laser-based infrared microspectroscopy.

Changes in the volume covered by mucin-secreting goblet cell regions within colon thin sections may serve as a means to differentiate between ulcerative colitis and infectious colitis. Here we show that rapid, quantum cascade laser-based mid-infrared microspectroscopy might be able to contribute to the differential diagnosis of colitis ulcerosa, an inflammatory bowel disease. Infrared hyperspectral images of mouse colon thin sections were obtained within 7.5 minutes per section with a pixel size of 3.65 × 3.65 µm(2) and a field of view of 2.8 × 3.1 mm(2). The spectra were processed by training a random decision forest classifier on the basis of k-means clustering on one thin section. The trained algorithm was then applied to 5 further thin sections for a blinded validation and it was able to identify goblet cells in all sections. The rapid identification of goblet cells within these unstained, paraffinized thin sections of colon tissue was enabled by the high content of glycopeptides within the goblet cells as revealed by the pronounced spectral signatures in the 7.6 µm-8.6 µm and the 9.2 µm-9.7 µm wavelength ranges of the electromagnetic spectrum. More so, the simple calculation of the ratio between the absorbance values at 9.29 µm and 8.47 µm provides the potential to further shorten the time for measurement and analysis of a thin section down to well below 1 minute.

Potential of mid-infrared spectroscopy to aid the triage of patients with acute chest pain.

A total of 1,429 serum samples from 389 consecutive patients with acute chest pain were analyzed with the goal to aid the rapid diagnosis of acute myocardial infarction. To the best of our knowledge this is the largest and most comprehensive study on mid-infrared spectroscopy in cardiology. We were able to identify those signatures in the mid-infrared spectra of the samples, which were specific to either acute myocardial infarction or chest pain of other origin (angina pectoris, oesophagitis, etc). These characteristic spectral differences were used to distinguish between the cause of the donor's acute chest pain using robust linear discriminant analysis. A sensitivity of 88.5% and a specificity of 85.1% were achieved in a blind validation. The area under the receiver operating characteristics curve amounts to 0.921, which is comparable to the performance of routine cardiac laboratory markers within the same study population. The biochemical interpretation of the spectral signatures points towards an important role of carbohydrates and potentially glycation. Our studies indicate that the "Diagnostic Pattern Recognition (DPR)" method presented here has the potential to aid the diagnostic procedure as early as within the first 6 hours after the onset of chest pain.

Continuous glucose monitoring by means of fiber-based, mid-infrared laser spectroscopy.

The application of mid-infrared laser spectroscopy to the reagent-free quantification of the concentration of glucose was investigated using cryogenically cooled lead salt lasers or, alternatively, quantum cascade lasers operating at room temperature. The concentration of glucose in aqueous solutions was quantified by means of fiber-based attenuated total reflection (ATR) spectroscopy (fiber-optical evanescent field analysis, FEFA) as well as fiber-based transmission spectroscopy. Both methods have the potential to be utilized by small fiber sensors, which can be inserted into the subcutaneous tissue in order to continuously measure the local concentration of glucose. In our in vitro experiments, noise-equivalent concentrations as low as 10 mg/dL were achieved. The mid-term stability of the measurement schemes was investigated by means of Allan variance analysis. Based on the research presented in this manuscript, an all-room-temperature measurement scheme using quantum cascade lasers, miniaturized fiber-optic sensors, and pyroelectric detectors appears well suited for the continuous monitoring of glucose concentrations at physiological levels.

Comparison of mid-infrared and Raman spectroscopy in the quantitative analysis of serum.

Mid-infrared or Raman spectroscopy together with multivariate data analysis provides a novel approach to clinical laboratory analysis, offering benefits due to its reagent-free nature, the speed of the analysis and the possibility of obtaining a variety of information from one single measurement. We compared mid-infrared and Raman spectra of the sera obtained from 247 blood donors. Partial least squares analysis of the vibrational spectra allowed for the quantification of total protein, cholesterol, high and low density lipoproteins, triglycerides, glucose, urea and uric acid. Glucose (mean concentration: 154 mg/dl) is frequently used as a benchmark for spectroscopic analysis and we achieved a root mean square error of prediction of 14.7 and 17.1 mg/dl for mid-infrared and Raman spectroscopy, respectively. Using the same sample set, comparable sample throughput, and identical mathematical quantification procedures Raman and mid-infrared spectroscopy of serum deliver similar accuracies for the quantification of the analytes under investigation. In our experiments vibrational spectroscopy-based quantification appears to be limited to accuracies in the 0.1 mmol/l range.

Biocompatibility of an electrochemical sensor for continuous glucose monitoring in subcutaneous tissue.

BACKGROUND: The continuous monitoring of glucose allows for tighter control of the glucose concentration and thus may prevent hyper- and hypoglycemia as well as long-term complications of diabetes. While most current systems depend on the transport of fluid to a glucose sensor outside the body, we investigate the possibility of implanting a reagent-based sensor directly into the skin. In this manuscript, the biocompatibility of an electrochemical sensor for continuous glucose monitoring was assessed in vitro and in vivo. METHODS: Cytotoxicity was investigated in vitro using agar diffusion testing. In vivo biocompatibility was assessed by means of histomorphological examination of the surrounding tissue 10 days after sensor implantation in rats. RESULTS: The grade of cytotoxicity of the individual sensor components in vitro was between none and mild based on agar diffusion testing. The complete sensor also showed no cytotoxic effects when coated with the co-polymer MPC (2-methacryloyloxyethyl phosphorylcholine, Lipidure CM 5206, NOF Corp., Tokyo, Japan) and when assessed under working conditions, i.e., when a bias voltage was applied to the sensor. Additionally, the hydrogen peroxide-which is inherently generated by the enzymatic glucose detection process using glucose oxidase (GOD)-is likely to have been sufficiently decomposed under these working conditions. Finally, no toxic leachable substances were found during the cytotoxicity testing of sensors and its extracts in vitro. In the in vivo experiments, the strongest foreign body reaction (FBR) was found near the GOD-electrode using a sensor without MPC coating and without a porous membrane. Covering the sensor with MPC, a porous membrane, or both led to a gradual decrease of the FBR down to the level of the negative control. CONCLUSIONS: The electrochemical, reagent-based sensor with MPC coating and/or a porous membrane is suitable for continuous monitoring of glucose from a biocompatibility standpoint.

Classification of signatures of Bovine Spongiform Encephalopathy in serum using infrared spectroscopy.

Signatures of Bovine Spongiform Encephalopathy (BSE) have been identified in serum by means of "Diagnostic Pattern Recognition (DPR)". For DPR-analysis, mid-infrared spectroscopy of dried films of 641 serum samples was performed using disposable silicon sample carriers and a semi-automated DPR research system operating at room temperature. The combination of four mathematical classification approaches (principal component analysis plus linear discriminant analysis, robust linear discriminant analysis, artificial neural network, support vector machine) allowed for a reliable assignment of spectra to the class "BSE-positive" or "BSE-negative". An independent, blinded validation study was carried out on a second DPR research system at the Veterinary Laboratory Agency, Weybridge, UK. Out of 84 serum samples originating from terminally-ill, BSE-positive cattle, 78 were classified correctly. Similarly, 73 out of 76 BSE-negative samples were correctly identified by DPR such that, numerically, an accuracy of 94.4 % can be calculated. At a confidence level of 0.95 (alpha = 0.05) these results correspond to a sensitivity > 85% and a specificity > 90%. Identical class assignment by all four classifiers occurred in 75% of the cases while ambiguous results were obtained in only 8 of the 160 cases. With an area under the ROC (receiver operating charateristics) curve of 0.991, DPR may potentially supply a valuable surrogate marker for BSE even in cases in which a deliberate bias towards improved sensitivity or specificity is desired. To the best of our knowledge, DPR is the first and--up to now--only method which has demonstrated its capability of detecting BSE-related signatures in serum.

Disease pattern recognition testing for rheumatoid arthritis using infrared spectra of human serum.

BACKGROUND: In view of the importance of the diagnosis of rheumatoid arthritis, a novel diagnostic method based on spectroscopic pattern recognition in combination with laboratory parameters such as the rheumatoid factor is described in the paper. Results of a diagnostic study of rheumatoid arthritis employing this method are presented. METHOD: The method uses classification of infrared (IR) spectra of serum samples by means of discriminant analysis. The spectroscopic pattern yielding the highest discriminatory power is found through a complex optimization procedure. In the study, IR spectra of 384 serum samples have been analyzed in this fashion with the objective of differentiating between rheumatoid arthritis and healthy subjects. In addition, the method integrates results from the classification with levels of the rheumatoid factor in the sample by optimized classifier weighting, in order to enhance classification accuracy, i.e. sensitivity and specificity. RESULTS: In independent validation, sensitivity and specificity of 84% and 88%, respectively, have been obtained purely on the basis of spectra classification employing a classifier designed specifically to provide robustness. Sensitivity and specificity are improved by 1% and 6%, respectively, upon inclusion of rheumatoid factor levels. Results for less robust methods are also presented and compared to the above numbers. CONCLUSION: The discrimination between RA and healthy by means of the pattern recognition approach presented here is feasible for IR spectra of serum samples. The method is sufficiently robust to be used in a clinical setting. A particular advantage of the method is its potential use in RA diagnosis at early stages of the disease.

Disease pattern recognition in infrared spectra of human sera with diabetes mellitus as an example.

To benefit from the full information content of the mid-IR spectra of human sera, we directly related the overall shape of the spectra to the donors' disease states. For this approach of disease pattern recognition we applied cluster analysis and discriminant analysis to the example of the disease states diabetes type 1, diabetes type 2, and healthy. In a binary, supervised classification of any pair of these disease states we achieved specificities and sensitivities of approximately 80% within our data set.