Rapid detection of deformed wing virus in honeybee using ultra-rapid qPCR and a DNA-chip.

Fast and accurate detection of viral RNA pathogens is important in apiculture. A polymerase chain reaction (PCR)-based detection method has been developed, which is simple, specific, and sensitive. In this study, we rapidly (in 1 min) synthesized cDNA from the RNA of deformed wing virus (DWV)-infected bees (Apis mellifera), and then, within 10 min, amplified the target cDNA by ultra-rapid qPCR. The PCR products were hybridized to a DNA-chip for confirmation of target gene specificity. The results of this study suggest that our method might be a useful tool for detecting DWV, as well as for the diagnosis of RNA virus-mediated diseases on-site.

Predicting nonsentinel lymph node metastasis using lymphoscintigraphy in patients with breast cancer.

UNLABELLED: Several models for predicting the likelihood of nonsentinel lymph node (NSLN) metastasis using histopathologic parameters in sentinel-positive breast cancer patients have been proposed. In this study, we established a new model that uses sentinel lymphoscintigraphic findings and histopathologic parameters as covariates and assessed its predictive performance. METHODS: The analysis included breast cancer patients (n = 301 women) who underwent sentinel lymphoscintigraphy (SLS) using (99m)Tc-labeled human serum albumin, had sentinel lymph node biopsy results positive for metastasis, and subsequently underwent complete axillary lymph node dissection. First, we devised a grading system relating SLS patterns to the risk of NSLN metastasis positivity. Second, we developed a multivariate logistic regression model for the prediction of NSLN metastasis using the SLS pattern and histopathologic parameters as covariates and compared its performance with that of the extensively validated Memorial Sloan-Kettering Cancer Center model using receiver-operating-characteristic curve analysis. RESULTS: The SLS visual grade was strongly correlated with the presence of NSLN metastases. A well-calibrated prediction model for NSLN metastasis was constructed using SLS grade and histopathologic findings. The mean area under the curve of our model was 0.812, which is significantly greater than that of the Memorial Sloan-Kettering Cancer Center model (P < 0.001). A nomogram was drawn to facilitate the application of our model. CONCLUSION: SLS can aid in predicting NSLN metastasis in patients with breast cancer. Our model performed better than did established prediction models.

[A case of cavernoma of portal vein associated with polycythemia vera].

Cavernoma of the portal vein is defined as a formation of venous channels within or around a previously thrombosed portal vein. We experienced a 50-year-old woman who presented a huge hepatic mass with right upper quadrant dull pain. Abdominal computed tomography showed a huge sponge-like hepatic mass with cavernous transformation of portal vein along the common bile duct and common hepatic duct. She had increased hemoglobin/hematocrit (15.7 g/dL/49.1%) and red blood cell mass (35 mL/kg). Platelet count was 450,000/microL and white blood cell count was 13,500/microL. Erythropoietin level was low normal range (10.2 mU/mL). Bone marrow biopsy showed a moderately hypercellular marrow and overall cellularity was about 80-90%. Megakaryocytes were slightly increased in number with abnormal clusterings Myelopoiesis and erythropoiesis were also slightly increased with moderate to severe fibrosis. She was diagnosed as polycythemia vera with cavernous transformation of portal vein. Repeated thrombosis occurred in the leg and the toe and was treated with angioplasty and thrombolytic therapy with phlebotomy.