Citations

Prostate cancer is the most common malignancy of men in the United States. Small-cell carcinoma (SCC), which typically presents as an aggressive lung malignancy, is a rare diagnosis within the setting of prostate cancer pathology. Due to its limited prevalence, little information regarding the treatment and prognosis of this disease in large populations is available. To date our current knowledge base is largely limited to case reports and retrospective case reviews. The mainstay of treatment for this particular histology most often involves a multimodality approach utilizing chemotherapy in conjunction with radiation therapy, androgen deprivation therapy, or prostatectomy. Here we present the case of an elderly 89-year-old Caucasian male who was diagnosed with SCC of the prostate. Despite proceeding with a course of definitive radiotherapy, the patient experienced rapid progression of disease and ultimately elected to discontinue radiation therapy and receive hospice care.

A generalized ensemble model (gEnM) for document ranking is proposed in this paper. The gEnM linearly combines the document retrieval models and tries to retrieve relevant documents at high positions. In order to obtain the optimal linear combination of multiple document retrieval models or rankers, an optimization program is formulated by directly maximizing the mean average precision. Both supervised and unsupervised learning algorithms are presented to solve this program. For the supervised scheme, two approaches are considered based on the data setting, namely batch and online setting. In the batch setting, we propose a revised Newton?s algorithm, gEnM.BAT, by approximating the derivative and Hessian matrix. In the online setting, we advocate a stochastic gradient descent (SGD) based algorithm-gEnM.ON. As for the unsupervised scheme, an unsupervised ensemble model (UnsEnM) by iteratively co-learning from each constituent ranker is presented. Experimental study on benchmark data sets verifies the effectiveness of the proposed algorithms. Therefore, with appropriate algorithms, the gEnM is a viable option in diverse practical information retrieval applications.

AbstractBackgroundGastric motility is coordinated by bioelectrical slow waves, and gastric dysrhythmias are reported in motility disorders. High‐resolution (HR) mapping has advanced the accurate assessment of gastric dysrhythmias, offering promise as a diagnostic technique. However, HR mapping has been restricted to invasive surgical serosal access. This study investigates the feasibility of HR mapping from the gastric mucosal surface.MethodsExperiments were conducted in vivo in 14 weaner pigs. Reference serosal recordings were performed with flexible‐printed‐circuit (FPC) arrays (128‐192 electrodes). Mucosal recordings were performed by two methods: (i) FPC array aligned directly opposite the serosal array, and (ii) cardiac mapping catheter modified for gastric mucosal recordings. Slow‐wave propagation and morphology characteristics were quantified and compared between simultaneous serosal and mucosal recordings.Key ResultsSlow‐wave activity was consistently recorded from the mucosal surface from both electrode arrays. Mucosally recorded slow‐wave propagation was consistent with reference serosal activation pattern, frequency (P≥.3), and velocity (P≥.4). However, mucosally recorded slow‐wave morphology exhibited reduced amplitude (65‐72% reduced, P<.001) and wider downstroke width (18‐31% wider, P≤.02), compared to serosal data. Dysrhythmias were successfully mapped and classified from the mucosal surface, accorded with serosal data, and were consistent with known dysrhythmic mechanisms in the porcine model.Conclusions & InferencesHigh‐resolution gastric electrical mapping was achieved from the mucosal surface, and demonstrated consistent propagation characteristics with serosal data. However, mucosal signal morphology was attenuated, demonstrating necessity for optimized electrode designs and analytical algorithms. This study demonstrates feasibility of endoscopic HR mapping, providing a foundation for advancement of minimally invasive spatiotemporal gastric mapping as a clinical and scientific tool.

Abstract Background: Genome-wide association studies (GWAS) have identified multiple loci associated with epithelial ovarian cancer (EOC) susceptibility, but further progress requires integration of epidemiology and biology to illuminate true risk loci below genome-wide significance levels (P &lt; 5 × 10−8). Most risk SNPs lie within non–protein-encoding regions, and we hypothesize that long noncoding RNA (lncRNA) genes are enriched at EOC risk regions and represent biologically relevant functional targets. Methods: Using imputed GWAS data from about 18,000 invasive EOC cases and 34,000 controls of European ancestry, the GENCODE (v19) lncRNA database was used to annotate SNPs from 13,442 lncRNAs for permutation-based enrichment analysis. Tumor expression quantitative trait locus (eQTL) analysis was performed for sub-genome-wide regions (1 × 10−5 &gt; P &gt; 5 × 10−8) overlapping lncRNAs. Results: Of 5,294 EOC-associated SNPs (P &lt; 1.0 × 10−5), 1,464 (28%) mapped within 53 unique lncRNAs and an additional 3,484 (66%) SNPs were correlated (r2 &gt; 0.2) with SNPs within 115 lncRNAs. EOC-associated SNPs comprised 130 independent regions, of which 72 (55%) overlapped with lncRNAs, representing a significant enrichment (P = 5.0 × 10−4) that was more pronounced among a subset of 5,401 lncRNAs with active epigenetic regulation in normal ovarian tissue. EOC-associated lncRNAs and their putative promoters and transcription factors were enriched for biologically relevant pathways and eQTL analysis identified five novel putative risk regions with allele-specific effects on lncRNA gene expression. Conclusions: lncRNAs are significantly enriched at EOC risk regions, suggesting a mechanistic role for lncRNAs in driving predisposition to EOC. Impact: lncRNAs represent key candidates for integrative epidemiologic and functional studies. Further research on their biologic role in ovarian cancer is indicated. Cancer Epidemiol Biomarkers Prev; 26(1); 116–25. ©2016 AACR.

ABSTRACT Considerable data and media attention have highlighted a potential “crisis” in the treatment of osteoporosis. Specifically, despite the availability of several effective drugs to prevent fractures, many patients who need pharmacological therapy are either not being prescribed these medications or if prescribed a medication, are simply not taking it. Although there are many reasons for this “gap” in the treatment of osteoporosis, a major factor is physician and patient concerns over the risk of side effects, especially atypical femur fractures (AFFs) related to bisphosphonate (and perhaps other antiresorptive) drug therapy. In this perspective, we review the current state of undertreatment of patients at increased fracture risk and suggest possible short-, intermediate-, and long-term approaches to address patient concerns, specifically those related to AFF risk. We suggest improved patient and physician education on prodromal symptoms, extended femur scans using dual-energy X-ray absorptiometry (DXA) to monitor patients on antiresorptive treatment, better identification of high-risk patients perhaps using geometrical parameters from DXA and other risk factors, and more research on pharmacogenomics to identify risk markers. Although not the only impediment to appropriate treatment of osteoporosis, concern over AFFs remains a major issue and one that needs to be resolved for effective dissemination of existing treatments to reduce fracture risk. © 2016 American Society for Bone and Mineral Research.