Proteomic profiling of the retinal dysplasia and degeneration chick retina

Purpose: In our previous paper we undertook proteomic analysis of the normal developing chick retina to identify proteins that were differentially expressed during retinal development. In the present paper we use the same proteomic approach to analyze the development and onset of degeneration in the retinal dysplasia and degeneration (rdd) chick. The pathology displayed by the rdd chick resembles that observed in some of the more severe forms of human retinitis pigmentosa.

Design of a salisbury screen absorber using frequency selective surfaces to improve bandwidth and angular stability performance

A new design method that greatly enhances the reflectivity bandwidth and angular stability beyond what is possible with a simple Salisbury screen is described. The performance improvement is obtained from a frequency selective surface (FSS) which is sandwiched between the outermost 377 Ω/square resistive sheet and the ground plane. This is designed to generate additional reflection nulls at two predetermined frequencies by selecting the size of the two unequal length printed dipoles in each unit cell. A multiband Salisbury screen is realised by adjusting the reflection phase of the FSS to position one null above and the other below the inherent absorption band of the structure. Alternatively by incorporating resistive elements midway on the dipoles, it is shown that the three absorption bands can be merged to create a structure with a −10 dB reflectivity bandwidth which is 52% larger and relatively insensitive to incident angle compared to a classical Salisbury screen having the same thickness. CST Microwave Studio was used to optimise the reflectivity performance and simulate the radar backscatter from the structure. The numerical results are shown to be in close agreement with bistatic measurements for incident angles up to 40° over the frequency range 5.4−18 GHz.

Comparison of porcine urine and bile as matrices to screen for the residues of two sulfonamides using a semi-automated enzyme immunoassay.

Porcine urine enzyme immunoassays for sulfamethazine and sulfadiazine have previously been employed as screening tests to predict the concentrations of the drugs in the corresponding tissues (kidneys), If a urine was found positive (> 800 ng ml(-1)) the corresponding kidney was then analysed by an enzyme immunoassay and, if found positive, a confirmatory analysis by HPLC was performed. Urine was chosen as the screening matrix since sulfonamides are mainly eliminated through this body fluid, However, after obtaining a number of false positive predictions, an investigation was carried out to assess the possibility of using an alternative body fluid which would act as a superior indicator of the presence of sulfonamides in porcine kidney, An initial study indicated that serum, plasma and bile could all be used as screening matrices. From these, bile was chosen as the preferred sample matrix and an extensive study followed to compare the efficiencies of sulfonamide positive bile and urine at predicting sulphonamide positive kidneys, Bile was found to be 17 times more efficient than urine at predicting a sulfamethazine positive kidney and 11 times more efficient at predicting a sulfadiazine positive kidney, With this enhanced performance of the initial screening test, the need for the costly and time consuming kidney enzyme immunoassay, prior to HPLC analysis, was eliminated

Novel breast cancer biomarkers identified by integrative proteomic and gene expression mapping

Proteomic and transcriptomic platforms both play important roles in cancer research, with differing strengths and limitations. Here, we describe a proteo-transcriptomic integrative strategy for discovering novel cancer biomarkers, combining the direct visualization of differentially expressed proteins with the high-throughput scale of gene expression profiling. Using breast cancer as a case example, we generated comprehensive two-dimensional electrophoresis (2DE)/mass spectrometry (MS) proteomic maps of cancer (MCF-7 and HCC-38) and control (CCD-1059Sk) cell lines, identifying 1724 expressed protein spots representing 484 different protein species. The differentially expressed cell-line proteins were then mapped to mRNA transcript databases of cancer cell lines and primary breast tumors to identify candidate biomarkers that were concordantly expressed at the gene expression level. Of the top nine selected biomarker candidates, we reidentified ANX1, a protein previously reported to be differentially expressed in breast cancers and normal tissues, and validated three other novel candidates, CRAB, 6PGL, and CAZ2, as differentially expressed proteins by immunohistochemistry on breast tissue microarrays. In total, close to half (4/9) of our protein biomarker candidates were successfully validated. Our study thus illustrates how the systematic integration of proteomic and transcriptomic data from both cell line and primary tissue samples can prove advantageous for accelerating cancer biomarker discovery.