Detection of amino-terminal extracellular domain of somatostatin receptor 2 by specific monoclonal antibodies and quantification of receptor density in medulloblastoma.

Somatostatin receptor 2 (SSTR2) is expressed by most medulloblastomas (MEDs). We isolated monoclonal antibodies (MAbs) to the 12-mer (33)QTEPYYDLTSNA(44), which resides in the extracellular domain of the SSTR2 amino terminus, screened the peptide-bound MAbs by fluorescence microassay on D341 and D283 MED cells, and demonstrated homogeneous cell-surface binding, indicating that all cells expressed cell surface-detectable epitopes. Five radiolabeled MAbs were tested for immunoreactive fraction (IRF), affinity (KA) (Scatchard analysis vs. D341 MED cells), and internalization by MED cells. One IgG(3) MAb exhibited a 50-100% IRF, but low KA. Four IgG(2a) MAbs had 46-94% IRFs and modest KAs versus intact cells (0.21-1.2 x 10(8) M(-1)). Following binding of radiolabeled MAbs to D341 MED at 4 degrees C, no significant internalization was observed, which is consistent with results obtained in the absence of ligand. However, all MAbs exhibited long-term association with the cells; binding at 37 degrees C after 2 h was 65-66%, and after 24 h, 52-64%. In tests with MAbs C10 and H5, the number of cell surface receptors per cell, estimated by Scatchard and quantitative FACS analyses, was 3.9 x 10(4) for the "glial" phenotype DAOY MED cell line and 0.6-8.8 x 10(5) for four neuronal phenotype MED cell lines. Our results indicate a potential immunotherapeutic application for these MAbs.

Sensitive detection of H2S using gold nanoparticle decorated single-walled carbon nanotubes.

Herein, we demonstrate that highly sensitive conductometric gas nanosensors for H(2)S can be synthesized by electrodepositing gold nanoparticles on single-walled carbon nanotube (SWNT) networks. Adjusting the electrodeposition conditions allowed for tuning of the size and number of gold nanoparticles deposited. The best H(2)S sensing performance was obtained with discrete gold nanodeposits rather than continuous nanowires. The gas nanosensors could sense H(2)S in air at room temperature with a 3 ppb limit of detection. The sensors were reversible, and increasing the bias voltage reduced the sensor recovery time, probably by local Joule heating. The sensing mechanism is believed to be based on the modulation of the conduction path across the nanotubes emanating from the modulation of electron exchange between the gold and carbon nanotube defect sites when exposed to H(2)S.

A host transcriptional signature for presymptomatic detection of infection in humans exposed to influenza H1N1 or H3N2.

There is great potential for host-based gene expression analysis to impact the early diagnosis of infectious diseases. In particular, the influenza pandemic of 2009 highlighted the challenges and limitations of traditional pathogen-based testing for suspected upper respiratory viral infection. We inoculated human volunteers with either influenza A (A/Brisbane/59/2007 (H1N1) or A/Wisconsin/67/2005 (H3N2)), and assayed the peripheral blood transcriptome every 8 hours for 7 days. Of 41 inoculated volunteers, 18 (44%) developed symptomatic infection. Using unbiased sparse latent factor regression analysis, we generated a gene signature (or factor) for symptomatic influenza capable of detecting 94% of infected cases. This gene signature is detectable as early as 29 hours post-exposure and achieves maximal accuracy on average 43 hours (p = 0.003, H1N1) and 38 hours (p-value = 0.005, H3N2) before peak clinical symptoms. In order to test the relevance of these findings in naturally acquired disease, a composite influenza A signature built from these challenge studies was applied to Emergency Department patients where it discriminates between swine-origin influenza A/H1N1 (2009) infected and non-infected individuals with 92% accuracy. The host genomic response to Influenza infection is robust and may provide the means for detection before typical clinical symptoms are apparent.

Structured Illumination Microscopy and a Quantitative Image Analysis for the Detection of Positive Margins in a Pre-Clinical Genetically Engineered Mouse Model of Sarcoma.

Intraoperative assessment of surgical margins is critical to ensuring residual tumor does not remain in a patient. Previously, we developed a fluorescence structured illumination microscope (SIM) system with a single-shot field of view (FOV) of 2.1 × 1.6 mm (3.4 mm2) and sub-cellular resolution (4.4 μm). The goal of this study was to test the utility of this technology for the detection of residual disease in a genetically engineered mouse model of sarcoma. Primary soft tissue sarcomas were generated in the hindlimb and after the tumor was surgically removed, the relevant margin was stained with acridine orange (AO), a vital stain that brightly stains cell nuclei and fibrous tissues. The tissues were imaged with the SIM system with the primary goal of visualizing fluorescent features from tumor nuclei. Given the heterogeneity of the background tissue (presence of adipose tissue and muscle), an algorithm known as maximally stable extremal regions (MSER) was optimized and applied to the images to specifically segment nuclear features. A logistic regression model was used to classify a tissue site as positive or negative by calculating area fraction and shape of the segmented features that were present and the resulting receiver operator curve (ROC) was generated by varying the probability threshold. Based on the ROC curves, the model was able to classify tumor and normal tissue with 77% sensitivity and 81% specificity (Youden's index). For an unbiased measure of the model performance, it was applied to a separate validation dataset that resulted in 73% sensitivity and 80% specificity. When this approach was applied to representative whole margins, for a tumor probability threshold of 50%, only 1.2% of all regions from the negative margin exceeded this threshold, while over 14.8% of all regions from the positive margin exceeded this threshold.