A hybrid feature subset selection algorithm for analysis of high correlation proteomic data.

Pathological changes within an organ can be reflected as proteomic patterns in biological fluids such as plasma, serum, and urine. The surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF MS) has been used to generate proteomic profiles from biological fluids. Mass spectrometry yields redundant noisy data that the most data points are irrelevant features for differentiating between cancer and normal cases. In this paper, we have proposed a hybrid feature subset selection algorithm based on maximum-discrimination and minimum-correlation coupled with peak scoring criteria. Our algorithm has been applied to two independent SELDI-TOF MS datasets of ovarian cancer obtained from the NCI-FDA clinical proteomics databank. The proposed algorithm has used to extract a set of proteins as potential biomarkers in each dataset. We applied the linear discriminate analysis to identify the important biomarkers. The selected biomarkers have been able to successfully diagnose the ovarian cancer patients from the noncancer control group with an accuracy of 100%, a sensitivity of 100%, and a specificity of 100% in the two datasets. The hybrid algorithm has the advantage that increases reproducibility of selected biomarkers and able to find a small set of proteins with high discrimination power.

Automatic evaluation of pressure sore status by combining information obtained from high-frequency ultrasound and digital photography.

In this study, the different phases of pressure sore generation and healing are investigated through a combined analysis of high-frequency ultrasound (20 MHz) images and digital color photographs. Pressure sores were artificially induced in guinea pigs, and the injured regions were monitored for 21 days (data were obtained on days 3, 7, 14, and 21). Several statistical features of the images were extracted, relating to both the altering pattern of tissue and its superficial appearance. The features were grouped into five independent categories, and each category was used to train a neural network whose outputs were the four days. The outputs of the five classifiers were then fused using a fuzzy integral to provide the final decision. We demonstrate that the suggested method provides a better decision regarding tissue status than using either imaging technique separately. This new approach may be a viable tool for detecting the phases of pressure sore generation and healing in clinical settings.

Determination of time-dependent protoporphyrin IX concentration for photodynamic therapy dosimetry in a mice colon tumor model using fluorescence spectroscopy.

Photodynamic therapy (PDT) is an effective treatment method for various types of invasive tumors. The efficiency of PDT treatment depends, to a great extent, on optimal dosimetry of light, the photosensitizer used, and on tissue oxygenation. Fluorescence spectroscopy can be employed for measurement of drug concentration in target tissue and can provide a basis for in vivo evaluation of treatment efficiency. We have developed an integrated system that can be used to determine photosensitizer concentration in vivo based on fluorescence measurements. In our study, we performed fluorescence measurements on colon tumors of Balb/c mice in which CT26 cells were injected subcutaneously in the right flank. 5-Aminolevulinic acid (ALA)-induced protoporphyrin IX (PpIX) was used as the photosensitizer. ALA was administered intraperitoneally at a dose of 200 mg/kg and PpIX fluorescence profiles were followed up to 34 h after ALA administration. Maximum fluorescence intensity was found 8 h after ALA administration. Also, we determined the relationship between PpIX concentration in colon tumor tissue of Balb/c mice and its fluorescence intensity at the peak of the spectrum (635 nm). This was used to determine the PpIX content in the target tissue as a function of time after ALA administration.