Electrochemical immunoassay for the prostate specific antigen using a reduced graphene oxide functionalized with a high molecular-weight silk peptide

High molecular-weight silk peptide (SP) was used to functionalize the surface of nanosheets of reduced graphene oxide (rGO). The SP-rGO nanocomposite was then mixed with mouse anti-human prostate specific antigen monoclonal antibody (anti-PSA) and coated onto a glassy carbon electrode to fabricate an immunosensor. By using the hexacyanoferrate redox system as electroactive probe, the immunosensor was characterized by voltammetry and electrochemical impedance spectroscopy. The peak current, measured at the potential of 0.24 V (vs. SCE), is distinctly reduced after binding prostate specific antigen (PSA). Response (measured by differential pulse voltammetry) is linearly related to PSA concentration in the range from 0.1 to 5.0 ng · mL⁻¹ and from 5.0 to 80.0 ng∙mL⁻¹, and the detection limit is 53 pg∙mL⁻¹ (at an SNR of 3). The immunosensor was successfully applied to the determination of PSA in clinical serum samples, and the results were found to agree well with those obtained with an enzyme-linked immunosorbent assay.

Nanosheets of reduced graphene oxide were functionalized with silk peptide and used to immobilize anti-PSA to fabricate an immunosensor for PSA.

     

An Interval Iteration Based Multilevel Thresholding Algorithm for Brain MR Image Segmentation

In this paper, we propose an interval iteration multilevel thresholding method (IIMT). This approach is based on the Otsu method but iteratively searches for sub-regions of the image to achieve segmentation, rather than processing the full image as a whole region. Then, a novel multilevel thresholding framework based on IIMT for brain MR image segmentation is proposed. In this framework, the original image is first decomposed using a hybrid L₁ − L₀ layer decomposition method to obtain the base layer. Second, we use IIMT to segment both the original image and its base layer. Finally, the two segmentation results are integrated by a fusion scheme to obtain a more refined and accurate segmentation result. Experimental results showed that our proposed algorithm is effective, and outperforms the standard Otsu-based and other optimization-based segmentation methods.