Effect of steam explosion of oil palm frond and empty fruit bunch on nutrient composition and ruminal fermentation characteristics.

In this study, we evaluated the effect of steam explosion of oil palm frond (OPF) and oil palm empty fruit bunch (EFB) on nutrient composition and ruminal fermentation characteristics in vitro. The results showed that steam explosion decreased NDF (P < 0.01), ADF (P < 0.01), and hemicellulose content (P < 0.01) in OPF and EFB. Steam explosion improved the effective energy value of OPF and EFB. In vitro fermentation results revealed that 72-h gas production capacity of OPF and EFB increased by 12.60and 85.06% (P < 0.01), respectively, after steam explosion. Steam explosion had a tendency to improve the concentration of total volatile fatty acids (TVFA) (P = 0.082). In conclusion, steam explosion of OPF and EFB reduced NDF, ADF, and hemicellulose content and increased gas production and TVFA concentration.

Key native fluorophores analysis of human breast cancer tissues using Gram-Schmidt subspace method.

The native fluorescence (NFL) spectra of human cancerous and normal breast tissues were excited by a selected wavelength of 300 nm to investigate the efficacy of two key fluorophores: tryptophan and reduced nicotinamide adenine dinucleotide (NADH), as cancer biomarkers. The basis spectra of these key fluorophores' subspaces spanned by the corresponding emission spectra are obtained by the Gram-Schmidt method. A support vector machine (SVM) classifier is trained in the subspace to evaluate the sensitivity, specificity, and accuracy. This research demonstrates that the NFL spectroscopy measurements are effective to detect changes of fluorophores compositions in tissues due to the development of cancer.

Native fluorescence spectra of human cancerous and normal breast tissues analyzed with non-negative constraint methods.

The native fluorescence spectra of human cancerous and normal breast tissues were investigated using the selected excitation wavelength of 340 nm to excite key building block molecules, such as reduced nicotinamide adenine dinucleotide (NADH), collagen, and flavin. The measured emission spectra were analyzed using a non-negative constraint method, namely multivariate curve resolution with alternating least-squares (MCR-ALS). The results indicate that the biochemical changes of tissue can be exposed by native fluorescence spectra analysis. The MCR-ALS-extracted components corresponding to the key fluorophores in breast tissue, such as collagen, NADH, and flavin, show differences of relative contents of fluorophores in cancerous and normal breast tissues. This research demonstrates that the native fluorescence spectroscopy measurements are effective for detecting changes of fluorophores composition in tissues due to the development of cancer. Native fluorescence spectroscopy analyzed by MCR-ALS may have the potential to be a new armamentarium.

Stokes shift spectroscopy highlights differences of cancerous and normal human tissues.

The Stokes shift spectroscopy (S3) offers a simpler and better way to recognize spectral fingerprints of fluorophores in complex mixtures. The efficiency of S3 for cancer detection in human tissue was investigated systematically. The alterations of Stokes shift spectra (S3) between cancerous and normal tissues are due to the changes of key fluorophores, e.g., tryptophan and collagen, and can be highlighted using optimized wavelength shift interval. To our knowledge, this is the first time to explicitly disclose how and why S3 is superior in comparison with other conventional spectroscopic techniques.

DNA and protein changes caused by disease in human breast tissues probed by the Kubelka-Munk spectral functional.

Malignant, fibroadenoma, normal and adipose breast tissues were studied using diffuse reflectance spectroscopy. The absorption spectra of the breast tissues were extracted from the diffuse reflectance spectra using the Kubelka-Munk function (K-M function). The spectral features of the K-M function were identified and compared with those of the absorption spectra. The spectral features of the K-M function were assigned to DNA, protein, beta-carotene and hemoglobin (oxygenated and deoxygenated) molecules in the breast tissue. The amplitudes of the K-M function averaged from 275 to 285 nm and from 255 to 265 nm and were found to be different for malignant, fibroadenoma and normal tissues. These differences were attributed to changes in proteins and DNA. A set of critical parameters was determined for separating malignant tissues from fibroadenoma and normal tissues. This approach should hold for other tissue types such as cervix, uterus and colon.

Stokes shift spectroscopic analysis of multifluorophores for human cancer detection in breast and prostate tissues.

Stokes shift spectroscopy (S3) offers a novel and simpler way to rapidly recognize spectral fingerprints of multiple fluorophores in complex media such as in tissue. This spectroscopic technique can be used as an effective approach to detect cancer in tissue. The alterations of the measured S3 spectra between cancerous and normal tissues were observed in human breast and prostate samples. In order to obtain the optimal Stokes shift interval, Δλi, for the purpose of breast/prostate cancer detection using S3, the S3 spectra of a mixed aqueous solution of tryptophan, nicotinamide adenine dinucleotide, and flavin were measured with different Δλi values. The experimental results analyzed using nonnegative least square method show that there is a reduced contribution from collagen and an increased contribution from tryptophan to the S3 signal of the cancerous tissue as compared with those of the normal tissue. This study indicates that the changes of relative contents of tryptophan and collagen in tissue shown by the S3 spectra may present potential native biomarkers for breast and prostate cancer detection. S3 has the potential to be a new armamentarium.

Biomarkers spectral subspace for cancer detection.

A novel approach to cancer detection in biomarkers spectral subspace (BSS) is proposed. The basis spectra of the subspace spanned by fluorescence spectra of biomarkers are obtained by the Gram-Schmidt method. A support vector machine classifier (SVM) is trained in the subspace. The spectrum of a sample tissue is projected onto and is classified in the subspace. In addition to sensitivity and specificity, the metrics of positive predictivity, Score1, maximum Score1, and accuracy (AC) are employed for performance evaluation. The proposed BSS using SVM is applied to breast cancer detection using four biomarkers: collagen, NADH, flavin, and elastin, with 340-nm excitation. It is found that the BSS SVM outperforms the approach based on multivariate curve resolution (MCR) using SVM and achieves the best performance of principal component analysis (PCA) using SVM among all combinations of PCs. The descent order of efficacy of the four biomarkers in the breast cancer detection of this experiment is collagen, NADH, elastin, and flavin. The advantage of BSS is twofold. First, all diagnostically useful information of biomarkers for cancer detection is retained while dimensionality of data is significantly reduced to obviate the curse of dimensionality. Second, the efficacy of biomarkers in cancer detection can be determined.