Synchronous Fluorescence Spectroscopy for the Detection and Characterization of Cervical Cancers In Vitro

The objective of this study was to assess the diagnostic potential of synchronous fluorescence (SF) spectroscopy (SFS) technique for the detection and characterization of normal and different malignancy stages of moderately differentiated squamous cell carcinoma (MDSCC), poorly differentiated squamous cell carcinoma (PDSCC) cervical tissues. SF spectra were measured from 45 biopsies from 30 patients in vitro . Characteristic, highly resolved peaks and significant spectral differences between normal and MDSCC, PDSCC cervical tissues were obtained. Nine potential ratios were calculated and used as input variables for a discriminant analysis across different groups. The potentiality of the SFS technique was estimated by two discriminant analyses. Discriminant analysis I performed across normal and abnormal (including MDSCC and PDSCC) cervical tissues classified as 100% both original and the cross-validated grouped cases. In discriminant analysis II performed across the three groups, normal, MDSCC and PDSCC, 100% of both original and the cross-validated grouped cases were correctly classified. Using the SFS technique, one can obtain all the key biochemical markers such as tryptophan, collagen, hemoglobin, reduced form of nicotinamide adenine dinucleotide and flavin adenine dinucleotide in a single scan and hence they can be targeted as tumor markers in the detection of normal from abnormal cervical tissues.     

Characterization and Diagnosis of Cancer by Native Fluorescence Spectroscopy of Human Urine

Urine is one of the diagnostically important bio fluids, as it has different metabolites in it, where many of them are native fluorophores. Native fluorescence characteristics of human urine samples were studied using excitation–emission matrices (EEMs) over a range of excitation and emission wavelengths, and emission spectra at 405 nm excitation, to discriminate patients with cancer from the normal subjects. The fluorescence spectra of urine samples of cancer patients exhibit considerable spectral differences in both EEMs and emission spectra with respect to normal subjects. Different ratios were calculated using the fluorescence intensity values of the emission spectra and they were used as input variables for a multiple linear discriminant analysis across different groups. The discriminant analysis classifies 94.7% of the original grouped cases and 94.1% of the cross‐validated grouped cases correctly. Based on the fluorescence emission characteristics of urine and statistical analysis, it may be concluded that the fluorophores nicotinamide adenine dinucleotide (NADH) and flavins may be considered as metabolomic markers of cancer.     

Spectroscopic whole-blood indicators of end-stage renal disease and the hemodialysis treatment

The diffuse reflection spectrum in the 500-1670 nm region for whole blood taken from healthy subjects and end-stage renal disease (ESRD) patients was measured to test the feasibility of optically monitoring ESRD and its treatment by hemodialysis. Spectral regions where optical absorption significantly differed between healthy subjects and ESRD patients were used to form a multiple linear discriminant classification model. With this model a total of 41 whole-blood samples were classified into healthy, pretreatment and posttreatment ESRD classes. 96.7% of original and cross-validated cases and 100% of independent validation cases were correctly classified, indicating ESRD and its treatment exhibit characteristic spectral features in whole blood. Upon comparison of the discriminant model variables with a few key clinical blood parameters, model variables were found to significantly correlate with hematocrit and plasma levels of urea and potassium (P<0.05). The results of this study suggest that the optical signature of whole blood conveys basic clinical status information, and provides a path for investigating improved indices of hemodialysis toxicity, adequacy and patient outcome.     

Forensic discrimination of photocopy and printer toners II. Discriminant analysis applied to infrared reflection-absorption spectroscopy

Copy toner samples were analyzed using reflection-absorption infrared microscopy (R-A IR). The grouping of copy toners into distinguishable classes achieved by visual comparison and computer-assisted spectral matching was compared to that achieved by multivariate discriminant analysis. For a data set containing spectra of 430 copy toners, 90% (388/430) of the spectra were initially correctly grouped into the classifications previously established by spectral matching. Three groups of samples that did not classify well contained too few samples to allow reliable classification. Samples from two other pairs of groups were similar and often misclassified. Closer examination of spectra from these groups revealed discriminating features that could be used in separate discriminant analyses to improve classification. For one pair of groups, the classification accuracy improved to 91% (81/89) and 97% (28/29), for the two groups, respectively. The other pair of groups were completely distinguishable from one another. With these additional tests, multivariate discriminant analysis correctly classified 96% of the 430 R-A IR toner spectra into the toner groups found previously by spectral matching.This is publication number 03–03 of the Laboratory Division of the Federal Bureau of Investigation. Names of commercial manufacturers are provided for identification only, and inclusion does not imply endorsement by the Federal Bureau of Investigation.     

Simultaneous determination of the tobacco smoke uptake parameters nicotine,cotinine and thiocyanate in urine,saliva and hair,using gas chromatography-mass spectrometry for characterisation of smoking status of recently exposed subjects

A method using gas chromatography (GC)-mass spectrometry (MS) for the simultaneous determination of the smoke uptake parameters thiocyanate, nicotine and cotinine in human tissues is reported. Nicotine, cotinine and thiocyanate, in combination with a phase-transfer catalyst, were extracted from urine, saliva and hair into dichloromethane (DCM). Thiocyanate was alkylated in the DCM-layer to form a pentafluorobenzyl derivative. The biochemical markers in DCM were directly injected into the GC system and separated on a DB-1MS column using a 9.4 min temperature program. The method was validated in urine and saliva between the limits of quantitation (1.0-15 microg ml(-1) thiocyanate, 0.010-3.0 microg ml(-1) nicotine and cotinine in urine, 0.010-1.0 microg ml(-1) nicotine and cotinine in saliva). The calibration curves were found to be linear (r > 0.996), the within- and between-day accuracy's were 83-120%, the repeatability coefficients of variation were 3-20% and the limits of detection were 0.060 ng ml(-1) thiocyanate and 0.60 ng ml(-1) nicotine and cotinine. The results of the analysis of the biomarkers in the urine of 44 volunteers were used to develop a predictive model for smoking status, using discriminant analysis. The classification model correctly classified 93.2% of cross-validated grouped cases. Saliva samples were used to confirm the results of the classification method.     

Optimisation of a new headspace mass spectrometry instrument. Discrimination of different geographical origin olive oils

A fast head-space analysis instrument, constituted by an automatic sample introduction system directly coupled to a mass detector without performing any chromatographic separation, was assembled. A suitable and original response was computed to optimise, by experimental design, the measured signals for discrimination purposes. The volatile fractions of 105 extra virgin olive oils coming from five different Mediterranean areas were analysed. The rough information collected by this system was unravelled and explained by well-known chemometrical techniques of display (principal component analysis), feature selection (stepwise linear discriminant analysis) and classification (linear discriminant analysis). The 93.4% of samples resulted to be correctly classified and the 90.5% correctly predicted by cross-validation procedure, whilst the 80.0% of an external test set, created to full validate the classification rule, were correctly assigned.     

Near-infrared Fluorescence Spectroscopy Detects Alzheimer's Disease In Vitro

The purpose of this study was to investigate whether near-infrared (NIR) fluorescence spectroscopy could be used to detect Alzheimer's disease (AD) by brain tissue autofluorescence. Unfixed temporal cortex specimens from AD cases and age-matched, non-AD controls were frozen at autopsy and then thawed just prior to spectral measurement. Spectra of intrinsic tissue fluorescence induced by 647 nm light were recorded from 650 to 850 nm. We used principal component analysis of the tissue spectra from 17 AD cases and 5 non-AD control cases in a calibration study to establish a diagnostic algorithm. Retrospectively applied to the calibration set, the algorithm correctly classified 23 of 24 specimens. In a prospective study of 19 specimens from 5 AD brains and 2 non-AD control brains, 3 of the 4 control specimens and all AD specimens were correctly diagnosed. Both the excitation light used and the measured brain tissue autofluorescence are at NIR wavelengths that can propagate through skull and overlying tissue. Therefore, our results demonstrate an optical spectroscopic technique that carries direct molecular level information about disease. This is the first step toward a clinical tool that has the potential to be applied to the noninvasive diagnosis of AD in living patients.     

Raman microscopy for the chemometric analysis of tumor cells

Raman spectroscopy is recognized as a tool for chemometric analysis of biological materials due to the high information content relating to specific physical and chemical qualities of the sample. Thirty cells belonging to two different prostatic cell lines, PNT1A (immortalized normal prostate cell line) and LNCaP (malignant cell line derived from prostate metastases), were mapped using Raman microscopy. A range of spectral preprocessing methods (partial least-squares discriminant analyses (PLSDAs), principal component analyses (PCAs), and adjacent band ratios (ABRs)) were compared for input into linear discriminant analysis to model and classify the two cell lines. PLSDA and ABR were able to correctly classify 100% of cells into benign and malignant groups, while PLSDA correctly classified a greater proportion of individual spectra. PCA was used to image the distribution of various biochemicals inside each cell and confirm differences in composition/distribution between benign and malignant cell lines. This study has demonstrated that PLSDAs and ABRs of Raman data can identify subtle differences between benign and malignant prostatic cells in vitro.     

Geographical classification of some Australian wines by discriminant analysis using HPLC with UV and chemiluminescence detection

HPLC with UV and acidified potassium permanganate chemiluminescence detection, combined with multivariate data analysis techniques, were used for the geographical classification of some Australian red (Cabernet Sauvignon) and white (Chardonnay) wines from two regions (Coonawarra and Geelong). Identification of the wine constituents prominent in the chromatography was performed by mass spectrometry. Principal components analysis and linear discriminant analysis were used to classify the wines according to region of production. Separation between regions was achieved with both detection systems and key components leading to discrimination of the wines were identified. Using two principal components, linear discriminant analysis with UV detection correctly classified 100% of the Chardonnay wines and, overall 91% of the Cabernet Sauvignon wines. With acidified potassium permanganate chemiluminescence detection, 75% of the Chardonnay wines and 94% of the Cabernet Sauvignon wines were correctly classified using two factors.     

In and ex vivo breast disease study by Raman spectroscopy

In this work, Raman spectra in the 900?C1,800?cm^?1 wavenumber region of in vivo and ex vivo breast tissues of both healthy mice (normal) and mice with induced mammary gland tumors (abnormal) were measured. In the case of the in vivo tissues, the Raman spectra were collected for both transcutaneous (with skin) and skin-removed tissues. To identify the spectral differences between normal and cancer breast tissue, the paired t-test was carried out for each wavenumber using the whole spectral range from both groups. Quadratic discriminate analysis based on principal component analysis (PCA) was also used to determine and evaluate differences in the Raman spectra for the various samples as a basis for diagnostic purposes. The differences in the Raman spectra of the samples were due to biochemical changes at the molecular, cellular and tissue levels. The sensitivity and specificity of the classification scheme based on the differences in the Raman spectra obtained by PCA were evaluated using the receiver operating characteristic (ROC) curve. The in vivo transcutaneous normal and abnormal tissues were correctly classified based on their measured Raman spectra with a discriminant proportion of 73%, while the in vivo skin-removed normal and abnormal tissues were correctly classified again based on their measured Raman spectra with a discriminant proportion of 86%. This result reveals a strong influence due to the skin of the breast, which decreased the specificity by 11%. Finally, the results from ex vivo measurements gave the highest specificity and sensitivity: 96 and 97%, respectively, as well as a largest percentage for correct discrimination: 94%. Now that the important bands have been experimentally determined in this and other works, what remains is for first principles molecular-level simulations to determine whether the changes are simply due to conformational changes, due to aggregation, due to changes in the environment, or complex interactions of all of the above.     

Comparison of PLS1-DA, PLS2-DA and SIMCA for classification by origin of crude petroleum oils by MIR and virgin olive oils by NIR for different spectral regions

This study compares results obtained with several chemometric methods: SIMCA, PLS2-DA, PLS2-DA with SIMCA, and PLS1-DA in two infrared spectroscopic applications. The results were optimized by selecting spectral ranges containing discriminant information. In the first application, mid-infrared spectra of crude petroleum oils were classified according to their geographical origins. In the second application, near-infrared spectra of French virgin olive oils were classified in five registered designations of origins (RDOs). The PLS-DA discrimination was better than SIMCA in classification performance for both applications. In both cases, the PLS1-DA classifications give 100% good results. The encountered difficulties with SIMCA analyses were explained by the criteria of spectral variance. As a matter of fact, when the ratio between inter-spectral variance and intra-spectral variance was close to the F_c (Fisher criterion) threshold, SIMCA analysis gave poor results. The discrimination power of the variable range selection procedure was estimated from the number of correctly classified samples.     

The internal link of serum steroid hormones levels in insomnia,depression, and Alzheimer's disease rats: Is there an effective way to distinguish among these three diseases based on potential biomarkers?

Insomnia, depression, and Alzheimer's disease are all neurodegenerative diseases and are associated with the levels of steroid hormones. To investigate the internal connection and difference of steroid hormones among these three diseases and distinguish them from the perspective of biomarkers, an easy, quick, and efficient high‐performance liquid chromatography with tandem mass spectrometry method was established and validated to determine six steroid hormones simultaneously in rat serum. The separation was accomplished on a SHIM‐PACK XR‐ODS chromatographic column with 0.1% v/v formic acid and methanol as the mobile phase and the detection was performed with electrospray ionization source in the positive ion mode. Based on the concentrations of steroid hormones, all the groups could be distinguished obviously from each other by using partial least square discriminant analysis. Meanwhile, 11‐deoxycortisol, corticosterone, and cortisol were identified as potential biomarkers and 100% of samples were classified correctly by Bayes’ discriminant function. These biomarkers were further screened by one‐way analysis of variance and cortisol was significantly different among all these groups. Bayes’ discriminant function was also built by cortisol and the classification accuracy was 87.2%. This workflow including determination of steroid hormones and discrimination among three neurological diseases would provide a basis for further clinical studies.     

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.     

Optimal excitation wavelengths for in vivo detection of oral neoplasia using fluorescence spectroscopy

There is no satisfactory mechanism to detect premalignant lesions in the upper aero-digestive tract. Fluorescence spectroscopy has potential to bridge the gap between clinical examination and invasive biopsy; however, optimal excitation wavelengths have not yet been determined. The goals of this study were to determine optimal excitation-emission wavelength combinations to discriminate normal and precancerous/cancerous tissue, and estimate the performance of algorithms based on fluorescence. Fluorescence excitation-emission matrices (EEM) were measured in vivo from 62 sites in nine normal volunteers and 11 patients with a known or suspected premalignant or malignant oral cavity lesion. Using these data as a training set, algorithms were developed based on combinations of emission spectra at various excitation wavelengths to determine which excitation wavelengths contained the most diagnostic information. A second validation set of fluorescence EEM was measured in vivo from 281 sites in 56 normal volunteers and three patients with a known or suspected premalignant or malignant oral cavity lesion. Algorithms developed in the training set were applied without change to data from the validation set to obtain an unbiased estimate of algorithm performance. Optimal excitation wavelengths for detection of oral neoplasia were 350, 380 and 400 nm. Using only a single emission wavelength of 472 nm, and 350 and 400 nm excitation, algorithm performance in the training set was 90% sensitivity and 88% specificity and in the validation set was 100% sensitivity, 98% specificity. These results suggest that fluorescence spectroscopy can provide a simple, objective tool to improve in vivo identification of oral cavity neoplasia.     

Pattern recognition study of biochemical assays for liver function

To extract discriminant information from analytical data, results from eight conventional biochemical tests of liver function and from determinations of two serum bile acids are studied by supervised pattern recognition methods. The population comprised healthy subjects and seven groups of people affected by different liver diseases. The principal components, linear discriminant, k nearest neighbours and Bayesian methods were applied. Because the prediction ability computed on the whole data set was poor, the problem was simplified by dividing the data set into three subsets, each comprising two liver diseases which were contiguous and overlapped in the hyperspace of variables. The prediction ability of the Bayesian method reached 96% at best, 75% at minimum, in the three subsets. Best performance was achieved in distinguishing between healthy subjects and those with mild liver diseases on the basis of four biochemical assays.     

Differentiation of Normal and Neoplastic Cells by Synchronous Fluorescence: Rat Liver Epithelial and Rat Hepatoma Cell Models

ABSTRACT

In the present study, conventional and synchronous luminescence (SL) were utilized to investigate spectral differences in normal and neoplastic cells. The synchronous fluorescence (SF) method involves scanning simultaneously both emission and excitation wavelengths while keeping a constant wavelength interval between them. This SF procedure simplifies the emission spectrum and provides for greater selectivity and is used to detect subtle differences in the fluorescence emission of the biochemical species of cells from rat tissues. A difference between the fluorescent spectra of the normal rat liver epithelial (RLE) and hepatoma cell lines were detected using synchronous fluorescence. The potential use of SF as a screening tool for cancer diagnosis is discussed.     

Combining visible and near-infrared spectroscopy with chemometrics to trace muscles from an autochthonous breed of pig produced in Uruguay: a feasibility study

Visible (Vis) and near-infrared reflectance (NIR) spectroscopy combined with chemometrics was explored as a tool to trace muscles from autochthonous and crossbreed pigs from Uruguay. Muscles were sourced from two breeds, namely, the Pampa-Rocha (PR) and the Pampa-Rocha x Duroc (PRxD) crossbreed. Minced muscles were scanned in the Vis and NIR regions (400–2,500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), discriminant partial least square regression (DPLS), linear discriminant analysis (LDA) based on PCA scores and soft independent modelling of class analogy (SIMCA) were used to identify the origin of the muscles based on Vis and NIR data. Full cross validation was used as validation method when classification models were developed. DPLS correctly classified 87% of PR and 78% of PRxD muscle samples. LDA calibration models correctly classified 87 and 67% of muscles as PR and PRxD, respectively. SIMCA correctly classified 100% of PR muscles. The results demonstrated the usefulness of Vis and NIR spectra combined with chemometrics as rapid method for authentication and identification of muscles according to the breed of pig.     

Discrimination of almonds (Prunus dulcis) geographical origin by minerals and fatty acids profiling

Twenty-one almond samples from three different geographical origins (Sicily, Spain and California) were investigated by determining minerals and fatty acids compositions. Data were used to discriminate by chemometry almond origin by linear discriminant analysis. With respect to previous PCA profiling studies, this work provides a simpler analytical protocol for the identification of almonds geographical origin. Classification by using mineral contents data only was correct in 77% of the samples, while, by using fatty acid profiles, the percentages of samples correctly classified reached 82%. The coupling of mineral contents and fatty acid profiles lead to an increased efficiency of the classification with 87% of samples correctly classified.     

Differentiation of organic and non-organic ewe's cheeses using main mineral composition or near infrared spectroscopy coupled to chemometric tools: a comparative study

Two independent methodologies were investigated to achieve the differentiation of ewes’ cheeses from different systems of production (organic and non-organic). Eighty cheeses (40 organic and 40 non-organic) from two systems of production, two different breeds of ewe, different sizes, seasons (summer and winter) and ripening times up to 9 months were elaborated. Their mineral composition or the information provided by their spectra in the near infrared zone (NIR) coupled to chemometric tools were used in order to differentiate between organic and non-organic cheeses. Main mineral composition (Ca, K, Mg, Na and P) of cheeses and stepwise lineal discriminant analysis were used to develop a discriminant model. The results from canonical standardised coefficients indicated that the most important mineral was Mg (1.725) followed by P (0.764) and K (0.742). The percentage of correctly classified samples was 88% in internal validation and 90% in external validation, selecting Mg, K and P as variables.Spectral information in the NIR zone was used coupled to a discriminant analysis based on a regression by partial least squares in order to obtain a model which allowed a rate of samples correctly classified of 97% in internal validation and 85% in external validation.     

毛细管电泳-小波神经网络法辅佐诊断乳腺癌的研究

采用毛细管电泳法测定了46个健康人和26个乳腺癌病人尿样中的13种正常核苷和修饰核苷,以小波神经网络作为模式识别工具对健康人和乳腺癌病人的分类作了研究,随机选取的训练集的识别率达到100％,相应的预测集判别率正确性在96％以上,与经典的前向多层神经网络相比,小波神经网络具有更强的信息提取和逼近能力.研究结果还表明,小波神经网络的预测能力强于主成分分析和线性判别分析,毛细管电泳法与小波神经网络的结合有望成为乳腺癌的辅助诊断手段.