Changing presentation of prostate cancer in a UK population--10 year trends in prostate cancer risk profiles in the East of England.

BACKGROUND: Prostate cancer incidence is rising in the United Kingdom but there is little data on whether the disease profile is changing. To address this, we interrogated a regional cancer registry for temporal changes in presenting disease characteristics. METHODS: Prostate cancers diagnosed from 2000 to 2010 in the Anglian Cancer Network (n=21,044) were analysed. Risk groups (localised disease) were assigned based on NICE criteria. Age standardised incidence rates (IRs) were compared between 2000-2005 and 2006-2010 and plotted for yearly trends. RESULTS: Over the decade, overall IR increased significantly (P<0.00001), whereas metastasis rates fell (P<0.0007). For localised disease, IR across all risk groups also increased but at different rates (P<0.00001). The most striking change was a three-fold increase in intermediate-risk cancers. Increased IR was evident across all PSA and stage ranges but with no upward PSA or stage shift. In contrast, IR of histological diagnosis of low-grade cancers fell over the decade, whereas intermediate and high-grade diagnosis increased significantly (P<0.00001). CONCLUSION: This study suggests evidence of a significant upward migration in intermediate and high-grade histological diagnosis over the decade. This is most likely to be due to a change in histological reporting of diagnostic prostate biopsies. On the basis of this data, increasing proportions of newly diagnosed cancers will be considered eligible for radical treatment, which will have an impact on health resource planning and provision

Cytomegalovirus-based vaccine expressing Ebola virus glycoprotein protects nonhuman primates from Ebola virus infection.

Ebolaviruses pose significant public health problems due to their high lethality, unpredictable emergence, and localization to the poorest areas of the world. In addition to implementation of standard public health control procedures, a number of experimental human vaccines are being explored as a further means for outbreak control. Recombinant cytomegalovirus (CMV)-based vectors are a novel vaccine platform that have been shown to induce substantial levels of durable, but primarily T-cell-biased responses against the encoded heterologous target antigen. Herein, we demonstrate the ability of rhesus CMV (RhCMV) expressing Ebola virus (EBOV) glycoprotein (GP) to provide protective immunity to rhesus macaques against lethal EBOV challenge. Surprisingly, vaccination was associated with high levels of GP-specific antibodies, but with no detectable GP-directed cellular immunity

Prediction of pathological stage in patients with prostate cancer: a neuro-fuzzy model

The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA) level, the biopsy most common tumor pattern (Primary Gleason pattern) and the second most common tumor pattern (Secondary Gleason pattern) in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD) or Extra-Prostatic Disease (ED) using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA) Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC) points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC), with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812). The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR = 0.032, TPR = 0.197, AUC = 0.582)