The cientificWorldJOURNAL Clinical Study Fasting Blood Glucose and Lipid Profile Alterations following Twelve-Month Androgen Deprivation Therapy in Men with Prostate Cancer

properly cited. Purpose. In this retrospective study, we aimed to investigate the effects of androgen deprivation therapy (ADT) on blood glucose and blood cholesterol levels over a 12-month period. Materials and Methods. Between January 2010 and June 2012, the data of 44 patients with prostate cancer who were receiving ADT were collected from a hospital database. Patients with additional malignancy or diabetes and those who had been prescribed and were currently taking cholesterol-lowering medication were excluded from the study. Data (including fasting blood glucose levels and a cholesterol profile) were collected and analysed statistically. A P value <0.05 was considered statistically significant. Results. Twelve months after the initiation of ADT, fasting blood glucose (FBG), total cholesterol (TC), low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, and triglyceride (TG) levels changed. FBG, TC, LDL cholesterol, and TG increased significantly (P = 0.009, 0.000, 0.000, and 0.000, resp.), while HDL cholesterol decreased (P = 0.000). Conclusion. ADT may increase FBG, TC, LDL cholesterol, and TG but decrease HDL cholesterol by the end of a year of treatment. Therefore, close followup may be needed as a consequence of one-year ADT regarding metabolic alterations

Inherent Grading Characteristics of Individual Pathologists Contribute to Clinically and Prognostically Relevant Interobserver Discordance Concerning Broders' Grading of Penile Squamous Cell Carcinomas

Introduction: The aim of our study was to evaluate the significance of transurethral resection of the prostate (TURP) to detect prostate cancer (PCa). A comparison was performed of the TURP specimens of patients undergoing high-intensity focused ultrasound (HIFU) with the core biopsies. Materials and Methods: TURP before undergoing HIFU therapy was performed in 106 patients without neoadjuvant treatment. The resected tissue was subjected to histopathological evaluation and compared to the histological results of transrectal prostate biopsy. Results: Cancer was detected in the resected tissue of 69 patients (65%). A positive correlation of the amount of resected tissue and detection of PCa could be demonstrated in a multivariate analysis. Conclusions: With a rate of 65% PCa detected by TURP, our data provide evidence that TURP might be suitable to detect PCa in a small group of selected patients with continuously rising PSA levels and several negative biopsies. On the other hand, these data underline/reinforce the necessity to treat the whole gland using modern treatment modalities such as HIFU and cryotherapy

Incorporating rich background knowledge for gene named entity classification and recognition

<p>Abstract</p> <p>Background</p> <p>Gene named entity classification and recognition are crucial preliminary steps of text mining in biomedical literature. Machine learning based methods have been used in this area with great success. In most state-of-the-art systems, elaborately designed lexical features, such as words, n-grams, and morphology patterns, have played a central part. However, this type of feature tends to cause extreme sparseness in feature space. As a result, out-of-vocabulary (OOV) terms in the training data are not modeled well due to lack of information.</p> <p>Results</p> <p>We propose a general framework for gene named entity representation, called feature coupling generalization (FCG). The basic idea is to generate higher level features using term frequency and co-occurrence information of highly indicative features in huge amount of unlabeled data. We examine its performance in a named entity classification task, which is designed to remove non-gene entries in a large dictionary derived from online resources. The results show that new features generated by FCG outperform lexical features by 5.97 F-score and 10.85 for OOV terms. Also in this framework each extension yields significant improvements and the sparse lexical features can be transformed into both a lower dimensional and more informative representation. A forward maximum match method based on the refined dictionary produces an F-score of 86.2 on BioCreative 2 GM test set. Then we combined the dictionary with a conditional random field (CRF) based gene mention tagger, achieving an F-score of 89.05, which improves the performance of the CRF-based tagger by 4.46 with little impact on the efficiency of the recognition system. A demo of the NER system is available at <url>http://202.118.75.18:8080/bioner</url>.</p

European Association of Urology Guidelines Office Rapid Reaction Group: An Organisation-wide Collaborative Effort to Adapt the European Association of Urology Guidelines Recommendations to the Coronavirus Disease 2019 Era

The coronavirus disease 2019 (COVID-19) pandemic is unlike anything seen before by modern science-based medicine. As a scientific society, the European Association of Urology, via the guidelines, section offices, and the European Urology family of journals, we believe that it is important that we try to support urologists in this difficult situation. We aim to do this by providing tools that can facilitate decision making with the goal to minimise the impact and risks for both patients and health professionals delivering urological care, whenever possible, although it is clear that it is not always possible to mitigate them entirely. We hope that these revised recommendations will fill an important urological practice void and assist urologist surgeons across the globe as they do their very best to deal with the crisis of our generation

Overview of the ID, EPI and REL tasks of BioNLP Shared Task 2011

We present the preparation, resources, results and analysis of three tasks of the BioNLP Shared Task 2011: the main tasks on Infectious Diseases (ID) and Epigenetics and Post-translational Modifications (EPI), and the supporting task on Entity Relations (REL). The two main tasks represent extensions of the event extraction model introduced in the BioNLP Shared Task 2009 (ST'09) to two new areas of biomedical scientific literature, each motivated by the needs of specific biocuration tasks. The ID task concerns the molecular mechanisms of infection, virulence and resistance, focusing in particular on the functions of a class of signaling systems that are ubiquitous in bacteria. The EPI task is dedicated to the extraction of statements regarding chemical modifications of DNA and proteins, with particular emphasis on changes relating to the epigenetic control of gene expression. By contrast to these two application-oriented main tasks, the REL task seeks to support extraction in general by separating challenges relating to part-of relations into a subproblem that can be addressed by independent systems. Seven groups participated in each of the two main tasks and four groups in the supporting task. The participating systems indicated advances in the capability of event extraction methods and demonstrated generalization in many aspects: from abstracts to full texts, from previously considered subdomains to new ones, and from the ST'09 extraction targets to other entities and events. The highest performance achieved in the supporting task REL, 58% F-score, is broadly comparable with levels reported for other relation extraction tasks. For the ID task, the highest-performing system achieved 56% F-score, comparable to the state-of-the-art performance at the established ST'09 task. In the EPI task, the best result was 53% F-score for the full set of extraction targets and 69% F-score for a reduced set of core extraction targets, approaching a level of performance sufficient for user-facing applications. In this study, we extend on previously reported results and perform further analyses of the outputs of the participating systems. We place specific emphasis on aspects of system performance relating to real-world applicability, considering alternate evaluation metrics and performing additional manual analysis of system outputs. We further demonstrate that the strengths of extraction systems can be combined to improve on the performance achieved by any system in isolation. The manually annotated corpora, supporting resources, and evaluation tools for all tasks are available from http://www.bionlp-st.org and the tasks continue as open challenges for all interested parties