Application of FT-IR spectroscopy on breast cancer serum analysis.

Breast cancer is regarded as the most malignant tumor among women throughout the world. Therefore, early detection and proper diagnostic methods have been known to help save women's lives. Fourier Transform Infrared (FT-IR) spectroscopy, coupled with PCA-LDA analysis, is a new technique to investigate the characteristics of serum in breast cancer. In this study, 43 breast cancer and 43 healthy serum samples were collected, and the FT-IR spectra were recorded for each one. Then, PCA analysis and linear discriminant analysis (LDA) were used to analyze the spectral data. The results showed that there were differences between the spectra of the two groups. Discriminating wavenumbers were associated with several spectral differences over the 950-1200cm-1(sugar), 1190-1350cm-1 (collagen), 1475-1710cm-1 (protein), 1710-1760cm-1 (ester), 2800-3000cm-1 (stretching motions of -CH2 & -CH3), and 3090-3700cm-1 (NH stretching) regions. PCA-LDA performance on serum IR could recognize changes between the control and the breast cancer cases. The diagnostic accuracy, sensitivity, and specificity of PCA-LDA analysis for 3000-3600cm-1 (NH stretching) were found to be 83%, 84%, 74% for the control and 80%, 76%, 72% for the breast cancer cases, respectively. The results showed that the major spectral differences between the two groups were related to the differences in protein conformation in serum samples. It can be concluded that FT-IR spectroscopy, together with multivariate data analysis, is able to discriminate between breast cancer and healthy serum samples.

The assessment of three-dimensional foot pronation using a principal component analysis method in the stance phase of running.

Foot pronation has not been quantified dynamically in three planes of movement in an in-vivo study. The aims of this study were to determine foot pronation through using Principal Component Analysis (PCA) method and to compare it among barefoot, shod and shod with 6° lateral wedge during the stance phase of running. In this method, three-dimension of foot movements were measured and each of these components represents a percentage of foot pronation. These components were derived based on eigenvalues and vectors of covariance matrix of primary variables. The first (PC1), second (PC2) and third (PC3) components explained about (82.5%, 79.1%), (14%, 15.8%) and (3.5%, 5.1%) the foot pronation for barefoot and shod conditions, respectively. These components were mutually independent and the components set had the same information as the primary variables. Foot pronation index and eversion angles were calculated and compared among barefoot, shod and shod with wedge insole (6° lateral wedge insole) conditions in the four phases of stance. Statistical analysis showed that there was no foot conditions effect for foot eversion in four phases (p=0.72), while this effect was significant for PC1 (p=0.001). This finding shows that PC1 index could discriminate footwear effect among each phase of stance. Specifically, pronation was reduced in shoe condition as compared to barefoot condition (p=0.02) from 5 to 50% of stance phase. It has been suggested that the PCA method provides more accurate criteria for investigating effects of footwear interventions on simultaneous three-dimensional foot motion.

Statistical significance of threading scores.

We present a general method for assessing threading score significance. The threading score of a protein sequence, thread onto a given structure, should be compared with the threading score distribution of a random amino-acid sequence, of the same length, thread on the same structure; small p-values point significantly high scores. We claim that, due to general protein contact map properties, this reference distribution is a Weibull extreme value distribution whose parameters depend on the threading method, the structure, the length of the query and the random sequence simulation model used. These parameters can be estimated off-line with simulated sequence samples, for different sequence lengths. They can further be interpolated at the exact length of a query, enabling the quick computation of the p-value.