The Parallel Factor Analysis of Beer Fluorescence.

Fluorescence excitation-emission matrices were measured for 111 samples of different types of beer and studied by the parallel factor analysis (PARAFAC). The 5-component PARAFAC model was found to suitably describes the beer fluorescence, accounting for 99.4% of the fluorescence variance in the measured set of samples, and providing the completely resolved excitation and emission spectra of each component. The model was chosen based on a model's core consistency and split-half analysis. It is shown that beer fluorescence is the sum of fluorescence of aromatic amino acids (tryptophan, tyrosine, and phenylalanine), different forms of vitamin B, and phenolic compounds. Obtained PARAFAC model of beer fluorescence demonstrated the potential for the quantification and quality analysis of beer fluorophores and classification of different beer types.

Support Vector Machine on fluorescence landscapes for breast cancer diagnostics.

Excitation-emission matrices (EEM) and total synchronous fluorescence spectra (SFS) of normal and malignant breast tissue specimens are measured in UV-VIS spectral region to serve as data inputs in development of Support Vector Machine (SVM) based breast cancer diagnostics tool. Various input data combinations are tested for classification accuracy using SVM prediction against histopathology findings to discover the best combination regarding diagnostics sensitivity and specificity. It is shown that with EEM data SVM provided 67% sensitivity and 62% specificity diagnostics. With SFS data SVM provided 100% sensitivity and specificity for a several input data combinations. Among these combinations those that require minimal data inputs are identified.

Importance of Copy Number Alterations of FGFR1 and C-MYC Genes in Triple Negative Breast Cancer.

BACKGROUND: Triple negative breast cancer (TNBC) is characterized by aggressive clinical course and is unresponsive to anti-HER2 and endocrine therapy. TNBC is difficult to treat and is often lethal. Given the need to find new targets for therapy we explored clinicopathological significance of copy number gain of FGFR1 and c-MYC. Our aim was to determine the impact of FGFR1 and c-MYC copy number gain on clinical course and outcome of TNBC. METHODS: FGFR1 and c-MYC gene copy number alterations were evaluated in 78 archive TNBC samples using TaqMan based quantitative real time PCR assays. RESULTS: 50% of samples had increased c-MYC copy number. c-MYC copy number gain was associated with TNBC in contrast to ER positive cancers. Our results showed significant correlation between c-MYC copy number gain and high grade of TNBCs. This suggests that c-MYC copy number could be an useful prognostic marker for TNBC patients. c-MYC copy number gain was associated with high pTNM stage as well as lobular and medullary tumor subtypes. 43% of samples had increased FGFR1 copy number. No correlations between FGFR1 copy number gain and clinicopathological variables were observed. CONCLUSIONS: We identified c-MYC copy number gain as a prognostic marker for TNBC. Our results indicate that c- MYC may contribute to TNBC progression. We observed no significant association between c-MYC and/or FGFR1 copy number status and patient survival.

Characterization of cereal flours by fluorescence spectroscopy coupled with PARAFAC.

This paper presents parallel factor analysis (PARAFAC) of fluorescence of cereal flours. Excitation-emission matrices (EEM's) of different cereal flours (wheat, corn, rye, rice, oat, spelt, barley and buckwheat) were measured in a front-face configuration over the ultraviolet-visible spectral range. EEM's showed that flours strongly fluoresce in two spectral regions, where amino acids, tocopherols, pyridoxine and 4-aminobenzoic acid show intense emissions. 4-component PARAFAC was used to model flour fluorescence and to decompose EEM's into excitation and emission spectra of each component. PARAFAC also provided relative concentrations of these components. The largest differences between flours were found in the concentration levels of the first and third component. Finally, variations in concentrations of PARAFAC modelled components were analysed in relation to the botanical origin of flour samples.

Three-dimensional total synchronous luminescence spectroscopy criteria for discrimination between normal and malignant breast tissues.

Specimens of malignant and normal female human breast tissues were analyzed after surgery by means of synchronous luminescence spectroscopy. Measurements were performed in the ranges of excitation wavelengths from 330 to 650 nm and synchronous wavelengths from 30 to 120 nm to obtain ordinary and first derivative three-dimensional total synchronous luminescence spectra (3d-TSLS) of each specimen. Arithmetic mean of these spectra has been calculated for normal and malignant specimens and analyzed to establish criteria for tissue differentiation. Spectral domain volumes (volumes below luminescence intensity surface) and mean spectral slopes have been calculated and also analyzed as tissue discrimination criteria. The obtained results are discussed in view of the possible relevance of synchronous luminescence spectroscopy in discrimination between normal and malignant breast tissue.

Fluorescence spectroscopy coupled with PARAFAC and PLS DA for characterization and classification of honey.

Fluorescence spectroscopy coupled with parallel factor analysis (PARAFAC) and Partial least squares Discriminant Analysis (PLS DA) were used for characterization and classification of honey. Excitation emission spectra were obtained for 95 honey samples of different botanical origin (acacia, sunflower, linden, meadow, and fake honey) by recording emission from 270 to 640 nm with excitation in the range of 240-500 nm. The number of fluorophores present in honey, excitation and emission spectra of each fluorophore, and their relative concentration are determined using a six-component PARAFAC model. Emissions from phenolic compounds and Maillard reaction products exhibited the largest difference among classes of honey of different botanical origin. The PLS DA classification model, constructed from PARAFAC model scores, detected fake honey samples with 100% sensitivity and specificity. Honey samples were also classified using PLS DA with errors of 0.5% for linden, 10% for acacia, and about 20% for both sunflower and meadow mix.

Erythrocytotoxicity of tiazofurin in vivo and in vitro detected by scanning probe microscopy.

Tiazofurin (TZF) is a cytostatic drug that leads to depletion of the GTP pool in tumor and normal cells via its active metabolite tiazofurin-adenine dinucleotide (TAD). TAD was detected in different cell lines, but not in erythrocytes, so the mechanism of erythrocytotoxicity of TZF remains unclear. The purpose of this study was to evaluate in vitro and in vivo action of tiazofurin on rat erythrocytes (RBC). After two decades of clinical trials the question of erythrocytotoxicity of TZF had remained unexplained making this study justified. Since we have previously demonstrated early erythrocytotoxic effects in male Wistar rats, we extend this finding on isolated RBC. Isolated erythrocytes from untreated animals were treated in buffered solution or plasma containing TZF. In addition, groups of 10 rats were treated with 200 and 1000 mg/kg of TZF and hematologic parameters were analyzed by flowcytometry and by the analysis of the peripheral blood smears. Early signs of hemolysis or aberrant structures were monitored by scanning probe microscopy (SPM). We suggest that correlation exists between early erythrocytotoxicity and irregularities in erythrocyte morphology and membrane integrity. We also found that TZF affects responsiveness to oxidative stress. This is in concordance with flowcytometric findings describing anisocytosis and anisochromosis of RBC.

Discrimination among melanoma, nevi, and normal skin by using synchronous luminescence spectroscopy.

Novel optical spectroscopy and imaging methods may be valuable in the early detection of cancer. This paper reports differences in the luminescence responses of pigmented skin lesions (melanomas and nevi) and apparently normal non-pigmented human skin, based on analyses of synchronous luminescence spectroscopy measurements. Measurements were performed in the excitation range of 330-545 nm, with synchronous intervals varying from 30-120 nm. Normal skin, nevi, and melanomas differ in the way they fluoresce, and these differences are more distinct in the synchronous fluorescence spectra than in the conventional emission and excitation spectra. The differences in the fluorescence characteristics of pigmented and normal skin samples were ascribed to differences in concentrations of endogenous fluorophores and chromophores. Principal component and linear discriminant analysis of the synchronous spectra measured at different synchronous intervals showed that the greatest variance among the sample groups was at the 70 nm interval spectra. These spectra were then used to create partial least squares discriminant analysis-based classification models. Evaluation of the quality of these models from the receiver operating characteristic curves showed they performed well, with a maximum value of 1 for the area under the curve for melanoma detection. Hence, synchronous luminescence spectroscopy coupled with statistical methods may be advantageous in the early detection of skin cancer.

Determination of the botanical origin of honey by front-face synchronous fluorescence spectroscopy.

Front-face synchronous fluorescence spectroscopy combined with chemometrics is used to classify honey samples according to their botanical origin. Synchronous fluorescence spectra of three monofloral (linden, sunflower, and acacia), polyfloral (meadow mix), and fake (fake acacia and linden) honey types (109 samples) were collected in an excitation range of 240-500 nm for synchronous wavelength intervals of 30-300 nm. Chemometric analysis of the gathered data included principal component analysis and partial least squares discriminant analysis. Mean cross-validated classification errors of 0.2 and 4.8% were found for a model that accounts only for monofloral samples and for a model that includes both the monofloral and polyfloral groups, respectively. The results demonstrate that single synchronous fluorescence spectra of different honeys differ significantly because of their distinct physical and chemical characteristics and provide sufficient data for the clear differentiation among honey groups. The spectra of fake honey samples showed pronounced differences from those of genuine honey, and these samples are easily recognized on the basis of their synchronous fluorescence spectra. The study demonstrated that this method is a valuable and promising technique for honey authentication.

Comparative analyses of individual and multiple alterations of p53, PTEN and p16 in non-small cell lung carcinoma, glioma and breast carcinoma samples.

p53, p16 and PTEN are the most commonly altered tumor suppressor genes in human cancers. In the present study, we compared the presence of individual and multiple alterations of these tumor suppressors in non-small cell lung carcinoma (NSCLC), glioma and breast carcinoma, in order to evaluate specificity of each tumor type regarding the number of altered genes, as well as their combinations. We tested the mutational status, loss of heterozygosity and methylation status of these genes. Effects of gene alterations on patients' survival were also assessed. In NSCLC samples, single gene alterations occurred rarely, while there was considerable increase in incidence of double gene alterations. Furthermore, coexistence of aberrant p53, PTEN and p16 was the most frequent and had significant adverse effect on the survival of NSCLC patients. On the contrary, in glioma and breast cancer specimens, substantial number of cases had aberrant single gene only. Moreover, glioma and breast carcinoma also differ in genotypes that were predominant. Specifically, in glioma samples, prevalent were co-alterations of PTEN and p16, followed by aberrant only PTEN. In breast cancer samples, alterations in all three genes as well as in p53 and p16 were the most common. Moreover, PTEN was altered exclusively with aberrant p53, with statistically significant correlation among them. Overall, our results suggest that NSCLC, glioma and breast cancer need different approaches in molecular diagnosis and treatment with particular attention toward the number and combination of targeted genes.

The impact of PTEN tumor suppressor gene on acquiring resistance to tamoxifen treatment in breast cancer patients.

Tamoxifen is a standard therapeutical treatment in patients with estrogen receptor positive breast carcinoma. However, less than 50% of estrogen receptor positive breast cancers do not respond to tamoxifen treatment whereas 40% of tumors that initially respond to treatment develop resistance over time. The underlying mechanisms for tamoxifen resistance are probably multifactorial but remain largely unknown. The primary aim of this study was to investigate the impact of PTEN tumor suppressor gene on acquiring resistance to tamoxifen by analyzing loss of heterozygosity (LOH) and immunohystochemical expression of PTEN in 49 primary breast carcinomas of patients treated with tamoxifen as the only adjuvant therapy. The effect of PTEN inactivation on breast cancer progression and disease outcome was also analyzed. Reduced or completely lost PTEN expression was observed in 55.1% of samples, while 63.3% of samples displayed LOH of PTEN gene. Inactivation of PTEN immunoexpression significantly correlated with the PTEN loss of heterozygosity, suggesting LOH as the most important genetic mechanism for the reduction or complete loss of PTEN expression in primary breast carcinoma. Most importantly, LOH of PTEN and consequential reduction of its immunoexpression showed significant correlation with the recurrence of the disease. Besides, our study revealed that LOH of PTEN tumor suppressor was significantly associated with shorter disease free survival, breast cancer specific survival and overall survival. In summary, our results imply that LOH of PTEN could be used as a good prognostic characteristic for the outcome of breast cancer patients treated with tamoxifen.

Application of supervised self-organizing maps in breast cancer diagnosis by total synchronous fluorescence spectroscopy.

Data from total synchronous fluorescence spectroscopy (TSFS) measurements of normal and malignant breast tissue samples are introduced in supervised self-organizing maps, a type of artificial neural network (ANN), to obtain diagnosis. Three spectral regions in both TSFS patterns and first-derivative TSFS patterns exhibited clear differences between normal and malignant tissue groups, and intensities measured from these regions served as inputs to neural networks. Histology findings are used as the gold standard to train self-organizing maps in a supervised way. Diagnostic accuracy of this procedure is evaluated with sample test groups for two cases, when the neural network uses TSFS data and when the neural network uses data from first-derivative TSFS. In the first case diagnostic sensitivity of 87.1% and specificity of 91.7% are found, while in the second case sensitivity of 100% and specificity of 94.4% are achieved.