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 4,687 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Canine non-Hodgkin's lymphoma (NHL) is a heterogeneous group of cancers representing approximately 15% of all canine cancers. Further, canine NHL mimics human disease in regards to histopathology and clinical behavior and could function as a comparative model. Diagnosis is currently performed by histopathological evaluation of surgical biopsy specimens and fine needle aspirate (FNA) cytology, an alternative and less invasive method for diagnosis. Desorption electrospray ionization - mass spectrometry (DESI-MS) imaging was performed on tissue sections of surgical biopsies and FNA smears. Mass spectra acquired from normal lymph nodes and NHL tumors were explored using multivariate statistics (e.g. principal component analysis). Tissue sections yielded a predicted sensitivity of 100% for normal and 93.1% for tumor. Further, preliminary results suggest B-cell and T-cell lymphoma can be discriminated (CV sensitivity of 95.5% and 85.7%, respectively). Normal and B-cell NHL FNA samples analyzed by DESI produced spectra that were similar to spectra obtained from surgical biopsies. FNA samples were evaluated using a PCA-LDA classification system built using tissue section data, exploring if the chemical information obtained from the different sample types is similar and whether DESI-MS performed on FNA samples is of diagnostic value. FNA prediction rate for normal (85.7%) and B-cell NHL (89.3%) indicated that DESI-MS analysis of FNA, not previously explored, could provide rapid preliminary diagnosis. Certainly, MS provides complementary molecular information to be used in conjunction with histopathology/cytology, potentially improving diagnostic confidence. The methodology outlined here is applicable to canine NHL, further supports canine models of human NHL, and translation to humans is envisioned.
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PMID:Characteristic lipid profiles of canine non-Hodgkin's lymphoma from surgical biopsy tissue sections and fine needle aspirate smears by desorption electrospray ionization--mass spectrometry. 2623 93

In this work we proposed a method to verify the differentiating characteristics of simple tea infusions prepared in boiling water alone (simulating a home-made tea cup), which represents the final product as ingested by the consumers. For this purpose we used UV-Vis spectroscopy and variable selection through the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA) for simultaneous classification of the teas according to their variety and geographic origin. For comparison, KNN, CART, SIMCA, PLS-DA and PCA-LDA were also used. SPA-LDA and PCA-LDA provided significantly better results for tea classification of the five studied classes (Argentinean green tea; Brazilian green tea; Argentinean black tea; Brazilian black tea; and Sri Lankan black tea). The proposed methodology provides a simpler, faster and more affordable classification of simple tea infusions, and can be used as an alternative approach to traditional tea quality evaluation as made by skilful tasters, which is evidently partial and cannot assess geographic origins.
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PMID:Using UV-Vis spectroscopy for simultaneous geographical and varietal classification of tea infusions simulating a home-made tea cup. 2630 62

An electronic nose (e-nose) was used to characterize sesame oils processed by three different methods (hot-pressed, cold-pressed, and refined), as well as blends of the sesame oils and soybean oil. Seven classification and prediction methods, namely PCA, LDA, PLS, KNN, SVM, LASSO and RF, were used to analyze the e-nose data. The classification accuracy and MAUC were employed to evaluate the performance of these methods. The results indicated that sesame oils processed with different methods resulted in different sensor responses, with cold-pressed sesame oil producing the strongest sensor signals, followed by the hot-pressed sesame oil. The blends of pressed sesame oils with refined sesame oil were more difficult to be distinguished than the blends of pressed sesame oils and refined soybean oil. LDA, KNN, and SVM outperformed the other classification methods in distinguishing sesame oil blends. KNN, LASSO, PLS, and SVM (with linear kernel), and RF models could adequately predict the adulteration level (% of added soybean oil) in the sesame oil blends. Among the prediction models, KNN with k = 1 and 2 yielded the best prediction results.
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PMID:Comparison of different classification methods for analyzing electronic nose data to characterize sesame oils and blends. 2650 50

Raman spectroscopy which is based upon inelastic scattering of photons has a potential to emerge as a noninvasive bedside in vivo or ex vivo molecular diagnostic tool. There is a need to improve the sensitivity and predictability of Raman spectroscopy. We developed a grid matrix-based tissue mapping protocol to acquire cellular-specific spectra that also involved digital microscopy for localizing malignant and lymphocytic cells in sentinel lymph node biopsy sample. Biosignals acquired from specific cellular milieu were subjected to an advanced supervised analytical method, i.e., cross-correlation and peak-to-peak ratio in addition to PCA and PC-LDA. We observed decreased spectral intensity as well as shift in the spectral peaks of amides and lipid bands in the completely metastatic (cancer cells) lymph nodes with high cellular density. Spectral library of normal lymphocytes and metastatic cancer cells created using the cellular specific mapping technique can be utilized to create an automated smart diagnostic tool for bench side screening of sampled lymph nodes. Spectral library of normal lymphocytes and metastatic cancer cells created using the cellular specific mapping technique can be utilized to develop an automated smart diagnostic tool for bench side screening of sampled lymph nodes supported by ongoing global research in developing better technology and signal and big data processing algorithms.
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PMID:A grid matrix-based Raman spectroscopic method to characterize different cell milieu in biopsied axillary sentinel lymph nodes of breast cancer patients. 2655 23

Disordered voices are frequently assessed by speech pathologists using perceptual evaluations. This might lead to problems caused by the subjective nature of the process and due to the influence of external factors which compromise the quality of the assessment. In order to increase the reliability of the evaluations, the design of automatic evaluation systems is desirable. With that in mind, this paper presents an automatic system which assesses the Grade and Roughness level of the speech according to the GRBAS perceptual scale. Two parameterization methods are used: one based on the classic Mel-Frequency Cepstral Coefficients, which has already been used successfully in previous works, and other derived from modulation spectra. For the latter, a new group of parameters has been proposed, named Modulation Spectra Morphological Parameters: MSC, DRB, LMR, MSH, MSW, CIL, PALA, and RALA. In methodology, PCA and LDA are employed to reduce the dimensionality of feature space, and GMM classifiers to evaluate the ability of the proposed features on distinguishing the different levels. Efficiencies of 81.6% and 84.7% are obtained for Grade and Roughness, respectively, using modulation spectra parameters, while MFCCs performed 80.5% and 77.7%. The obtained results suggest the usefulness of the proposed Modulation Spectra Morphological Parameters for automatic evaluation of Grade and Roughness in the speech.
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PMID:Modulation Spectra Morphological Parameters: A New Method to Assess Voice Pathologies according to the GRBAS Scale. 2655 56

A rapid and non-destructive methodology is proposed for the screening of edible vegetable oils according to conservation state expiration date employing near infrared (NIR) spectroscopy and chemometric tools. A total of fifty samples of soybean vegetable oil, of different brands andlots, were used in this study; these included thirty expired and twenty non-expired samples. The oil oxidation was measured by peroxide index. NIR spectra were employed in raw form and preprocessed by offset baseline correction and Savitzky-Golay derivative procedure, followed by PCA exploratory analysis, which showed that NIR spectra would be suitable for the classification task of soybean oil samples. The classification models were based in SPA-LDA (Linear Discriminant Analysis coupled with Successive Projection Algorithm) and PLS-DA (Discriminant Analysis by Partial Least Squares). The set of samples (50) was partitioned into two groups of training (35 samples: 15 non-expired and 20 expired) and test samples (15 samples 5 non-expired and 10 expired) using sample-selection approaches: (i) Kennard-Stone, (ii) Duplex, and (iii) Random, in order to evaluate the robustness of the models. The obtained results for the independent test set (in terms of correct classification rate) were 96% and 98% for SPA-LDA and PLS-DA, respectively, indicating that the NIR spectra can be used as an alternative to evaluate the degree of oxidation of soybean oil samples.
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PMID:Using near infrared spectroscopy to classify soybean oil according to expiration date. 2659 25

Surface-enhanced Raman spectroscopy (SERS) is an ultrasensitive vibrational fingerprinting technique widely used in analytical and biosensing applications. For intracellular sensing, typically gold nanoparticles (AuNPs) are employed as transducers to enhance the otherwise weak Raman spectroscopy signals. Thus, the signature patterns of the molecular nanoenvironment around intracellular unlabeled AuNPs can be monitored in a reporter-free manner by SERS. The challenge of selectively identifying molecular changes resulting from cellular processes in large and multidimensional data sets and the lack of simple tools for extracting this information has resulted in limited characterization of fundamental cellular processes by SERS. Here, this shortcoming in analysis of SERS data sets is tackled by developing a suitable methodology of reference-based PCA-LDA (principal component analysis-linear discriminant analysis). This method is validated and exemplarily used to extract spectral features characteristic of the endocytic compartment inside cells. The voluntary uptake through vesicular endocytosis is widely used for the internalization of AuNPs into cells, but the characterization of the individual stages of this pathway has not been carried out. Herein, we use reporter-free SERS to identify and visualize the stages of endocytosis of AuNPs in cells and map the molecular changes via the adaptation and advantageous use of chemometric methods in combination with tailored sample preparation. Thus, our study demonstrates the capabilities of reporter-free SERS for intracellular analysis and its ability to provide a way of characterizing intracellular composition. The developed analytical approach is generic and enables the application of reporter-free SERS to identify unknown components in different biological matrices and materials.
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PMID:Characterization and Visualization of Vesicles in the Endo-Lysosomal Pathway with Surface-Enhanced Raman Spectroscopy and Chemometrics. 2664 52

This study presents a systematized method for predicting water content, fat content and free acidity in olive fruits by on-line NIR Spectroscopy combined with chemometric techniques (PCA, LDA and PLSR). Three cultivar varieties of Olea europaea - Hojiblanca cv., Picual cv. and Arbequina cv. - were monitored. Five olive cultivation areas of Southern Spain (Andalucia) and Southern Portugal (Alentejo) were studied in 2011 and 2012. 465 olive samples were collected during the ripening process (non-mature olives) and compared with other 203 samples of mature olives collected at the final ripening stage. NIR spectra were measured directly in the olive fruits in the wavelength region from 1000 to 2300 nm in reflectance mode. The reference analyses were performed on the olive paste by oven drying for the moisture, by mini-Soxhlet extraction for the fat content and by acid titration of the oil extracted from the olive paste. Calibrations and predictive models were developed by Partial Least Square Regression (PLSR) previous Principal Component and Linear Discriminant analyses (PCA and LDA) were employed as exploratory and clean-up tools of data sets. The final models obtained for the total samples showed acceptable statistics of prediction with R(2)=0.88, RMSEV%=4.88 and RMSEP%=4.98 for water content, R(2)=0.76, RMSECV%=19.5 and RMSEP%=20.0 for fat content and R(2)=0.83, RMSECV%=36.8 and RMSEP%=38.8 for free acidity. Regression coefficients were better for only one maturity state (ripe period) than for olive fruit with different composition (ripening period). All models obtained were applied to predict LQPs on a new set of samples with satisfactory results, a good prediction potential of the models.
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PMID:Combining PLS regression with portable NIR spectroscopy to on-line monitor quality parameters in intact olives for determining optimal harvesting time. 2665 43

The aim of this study was to find any relation between soft facial tissue thickness (FSTT) and sex, age and asymmetry in the contemporary Czech population. The studied sample consisted of head CT scans of 102 adult Czech individuals between the ages of 21 and 83. Forty FSTTs were evaluated and analysed using PCA, Hotelling's T(2) test, LDA, the Kolmogorov-Smirnov test, MANOVA, the Kruskal-Wallis test and Wilcoxon's paired test. The greatest sexual dimorphism was detected in the lower part of the face, which had discriminant power almost the same as the entire faces (approximately 80%). On the other hand, a significant influence of aging was shown, mostly in the area of the upper face (In females, twice as many landmarks displayed a significant influence, compared with males). The influence of asymmetry was confirmed in seven bilateral landmarks, five of them favouring the right side.
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PMID:Facial soft tissue thicknesses in the present Czech Population. 2686 69

The present study evaluated the use of saliva surface-enhanced Raman spectroscopy (SERS) for the detection of non-invasive nasopharyngeal carcinoma (NPC). SERS measurements were taken from 62 saliva samples, of which 32 were from NPC patients and 30 from healthy volunteers. Notable biochemical Raman bands in the SERS spectra were tentatively assigned to various saliva components. The saliva SERS spectra obtained from the NPC patients and the healthy volunteers were also analyzed by multivariate statistical techniques based on principal component analysis and linear discriminant analysis (PCA-LDA). Significant differences were observed between the saliva SERS spectral intensities for NPC patients and healthy volunteers, particularly at 447, 496, 635, 729, 1134, 1270 and 1448 cm-1, which primarily contained signals associated with proteins, nucleic acids, fatty acids, glycogen and collagen. The classification results based on the PCA-LDA method provided a relatively high diagnostic sensitivity of 86.7%, specificity of 81.3% and diagnostic accuracy of 83.9% for NPC identification. The results from the present study demonstrate that saliva SERS analysis used in conjunction with PCA-LDA diagnostic algorithms possesses a promising clinical application for the non-invasive detection of NPC.
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PMID:Non-invasive detection of nasopharyngeal carcinoma using saliva surface-enhanced Raman spectroscopy. 2687 Mar


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