Tatjana Dramićanin

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.

Three-dimensional Total Synchronous Luminescence Spectroscopy Criteria for Discrimination Between Normal and Malignant Breast Tissues

Abstract 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.

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.

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.

Authentication of the botanical origin of unifloral honey by infrared spectroscopy coupled with support vector machine algorithm

In recent years, the potential of Fourier-transform infrared spectroscopy coupled with different chemometric tools in food analysis has been established. This technique is rapid, low cost, and reliable and requires little sample preparation. In this work, 130 Serbian unifloral honey samples (linden, acacia, and sunflower types) were analyzed using attenuated total reflectance infrared spectroscopy (ATR-IR). For each spectrum, 64 scans were recorded in wavenumbers between 4000 and 500 cm−1 and at a spectral resolution of 4 cm−1. These spectra were analyzed using principal component analysis (PCA), and calculated principal components were then used for support vector machine (SVM) training. In this way, the pattern-recognition tool is obtained for building a classification model for determining the botanical origin of honey. The PCA was used to analyze results and to see if the separation between groups of different types of honeys exists. Using the SVM, the classification model was built and classification errors were acquired. It has been observed that this technique is adequate for determining the botanical origin of honey with a success rate of 98.6%. Based on these results, it can be concluded that this technique offers many possibilities for future rapid qualitative analysis of honey.

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.

Amplification of Cycline D1, C-MYC And EGFR Oncogenes in Tumour Samples of Breast Cancer Patients / AMPLIFIKACIJA CIKLIN D1, C-MYC AND EGFR ONKOGENA U TUMORSKIM UZORCIMA PACIJENTKINJA OBOLELIH OD KANCERA DOJKE

Summary Background: Breast cancer is the most common form of cancer in women. It arises from multiple genetic changes in oncogenes and tumor suppressor genes. Among so far studied oncogenes relatively few, including epdermal growth factor receptor (EGFR), cyclinD1 (CCND1)and cmyc, have been found to play an important role in progression of this type of human malignancy. The aim of this study was to examine the prognostic potential of CCND1, c-myc and EGFR amplification and their possible cooperation in breast carcinogenesis. Methods: Copy number analyses of CCND1 and c-myc genes were done by TaqMan based quantitative real time PCR. Am pli fication status of EGFR was determined by differential PCR. Results: Amplification of CCND1, c-myc and EGFR onco- gene has been found in 20.4%, 26.5% and 26.5% of breast cancer cases, respectively. Analysis showed that amplification of CCND1 oncogene was significantly associated with the stage II of disease while amplification of EGFR gene was sig- nificantly associated with overexpression of HER-2/neu. Tu- mour stage and expression of HER-2/neu appeared to be significant predictors of patients outcome. Stage I patients lived significantly longer then stage III patients (p=0.04) while patients with FiER-2/neu overexpression had worse prognoses and lived significantly shorter (p=0.001). Finally, survival of patients who underwent hormone therapy only was significantly longer (p=0.001) then survival of the rest of patients. Conclusions: Amplification of CCND1 or EGFR oncogene is associated with the progression of breast cancer and bad prognosis. No co-ordination in amplification of CCND1, c- myc and EGFR oncogenes were established in this cohort of breast cancer patients. Kratak sadržaj Uvod: Kancer dojke je najčešči tip maligniteta koji se javlja kod žena. Tumori dojke nastaju kao rezultat akumulacije genetičkih promena kako u onkogenima tako i u tumor supresorskim genima. Medu mnogim onkogenima čija je uloga u genezi tumora dojke ispitivana do danas, samo se neki smatraju značajnim za razviče ovih karcinoma. U tu se grupu svakako ubrajaju receptor za epidermalni factor rasta (EGFR), c-myc i ciklinDI (CCND7). Cilj rada je bio utvrditi prognostickí značaj amplifikacije CCND1, c-myc i EGFR onkogena u razvicu tumora dojke kao i eventualne medusob- ne koalteracije ovih gena. Metode: Amplifikacioni status CCND1 i c-myc gena odreden je kvantitativnim PCR-om u reálnom vremenu, a amplifikacioni status EGFR onkogena je definisan diferencijalnim PCR-om. Rezultati: Amplifikacija CCND1 gena detektovana je kod 20,4%, a c-myc i EGFR onkogena kod 26,5% ispitanih uzo- raka. Analize su pokazale da je amplifikacija CCND1 onko- gena statistički značajno povezana sa stadijumom II tumora dojke kao i da amplifikacija EGFR-a značajno korelira sa povečanom ekspresijom HER2/neu. Analize kliničkih i histo- patoloških parametara su jasno pokazale da stadijum tumo- ra i nivo ekspresije HER2/neu gena predstavljaju značajne pokazatelje daljeg toka bolesti, odnosno sudbine pacijenta. Utvrdeno je da pacijentkinje sa tumorima dojke stadijuma I žive značajno duže od onih sa tumorom stadijuma III (p= 0,04) kao i da pacijentkinje sa HER2/neu pozitivnim statu- som imaju goru prognózu i žive značajno krače (p=0,001). Na kraju, študija je pokazala da pacijentkinje podvrgnute samo hormonskoj terapiji imaju najbolju prognózu i žive značajno duže od ostalih (p=0,001). Zaključak: Amplifikacija CCND1 i EGFR onkogena je po- vezana sa losom prognozom i progresijom karcinoma dojke. U ispitivanom tumorskom uzorku nisu detektovane nikakve koalteracije CCND1, c-myc i EGFR onkogena.

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 (EEMs) 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. EEMs 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 EEMs 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.

Artificial neural networks for processing fluorescence spectroscopy data in skin cancer diagnostics

Over the years various optical spectroscopic techniques have been widely used as diagnostic tools in the discrimination of many types of malignant diseases. Recently, synchronous fluorescent spectroscopy (SFS) coupled with chemometrics has been applied in cancer diagnostics. The SFS method involves simultaneous scanning of both emission and excitation wavelengths while keeping the interval of wavelengths (constant-wavelength mode) or frequencies (constant-energy mode) between them constant. This method is fast, relatively inexpensive, sensitive and non-invasive. Total synchronous fluorescence spectra of normal skin, nevus and melanoma samples were used as input for training of artificial neural networks. Two different types of artificial neural networks were trained, the self-organizing map and the feed-forward neural network. Histopathology results of investigated skin samples were used as the gold standard for network output. Based on the obtained classification success rate of neural networks, we concluded that both networks provided high sensitivity with classification errors between 2 and 4%.

Using Fluorescence Spectroscopy to Diagnose Breast Cancer

Optical spectroscopy methods have had considerable impact in the field of biomedical diagnostics, providing novel methods for the early or noninvasive diagnosis of various medical conditions. Among them, fluorescence spectroscopy has been the most widely explored mainly because fluorescence is highly sensitive to the biochemical makeup of tissues. It has been shown that tumors were easily detected on account of altered fluorescence properties with respect to fluorescence of ordinary tissue. Breast cancer is one of the most commonly diagnosed cancers among women in the world and also it is one of the leading causes of deaths from cancer for the female population. However, when detected in early stage, it is one of the most treatable forms of cancer. Therefore, fluorescence technologies could be highly beneficial in early detection and timely treatment of cancer. This chapter presents main results and conclusions that have been reported on the use of fluorescence spectroscopy for the investigation of breast cancer. It also gives an overview on the instruments and methodology of measurements, on the main endogenous fluorophores present in tissues, on the tissue fluorescence, and on the statistical methods that aid interpretations of fluorescence spectra. Finally, examples of using various fluorescence techniques, such as excitation, emission and synchronous spectroscopy, excitation-emission matrices, and lifetimes, for the breast cancer diagnosis are presented.