BackgroundWith more than 940,000 new colorectal cancer cases worldwide each year, there is no better way for colorectal cancer routine screening. The aim of this study was to investigate whether the fatty acid binding to albumin is detectably and significantly altered in colorectal cancer patients when compared with healthy people, in order to find a better way for colorectal cancer diagnosis.MethodsOne hundred and forty-one patients operatively treated for colorectal cancer were included in the examination, and 180 healthy people were also enrolled as controls. Commercial 16-doxyl stearic acid was used as spin probe. Serum albumin was analyzed by electron paramagnetic resonance (EPR) with spin probe. Discriminant analysis was carried out using the measured EPR spectra by SPSS 20.0.ResultsOf the original grouped cases, 89.4% were correctly classified. Of the cross-validated grouped cases, 86.9% were correctly classified. Using Fisher linear discriminant analysis we were able to develop a mathematical model allowing for identification of colorectal cancer patients based on five values (both relative intensity and peak width) which are obtained from the EPR spectrum.ConclusionsCancer-associated alterations to albumin can be assessed by spin-label EPR. The potential applications for this diagnostic technique are significant and represent a cost-effective means for screening patients with cancer. Spin probe for diagnosis of colorectal cancer might be a useful tool and further studies should take place in order to investigate all stages of colorectal cancer patients.

For the low computational efficiency, the existence of false targets, blurred targets, and halo occluded targets of existing image fusion models, a novel fusion method of visible and infrared images using GE-WA model and VGG-19 network is proposed. First, Laplacian is used to decompose the visible and infrared images into basic images and detail content. Next, a Gaussian estimation function is constructed, and a basic fusion scheme using the GE-WA model is designed to obtain a basic fusion image that eliminates halo of visible image. Then, the pre-trained VGG-19 network and the multi-layer fusion strategy are used to extract the fusion of different depth features of the visible and infrared images, and also obtain the fused detail content with different depth features. Finally, the fusion image is reconstructed by the basic image and detail content after fusion. The experiments show that the comprehensive evaluation FQ of the proposed method is better than other comparison methods, and has better performance in the aspects of image fusion speed, halo elimination of visible image, and image fusion quality, which is more suitable for visible and infrared image fusion in complex environments.

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