University of Pittsburgh

About: University of Pittsburgh is a education organization based out in Pittsburgh, Pennsylvania, United States. It is known for research contribution in the topics: Population & Transplantation. The organization has 87042 authors who have published 201012 publications receiving 9656783 citations. The organization is also known as: Pitt & Western University of Pennsylvania.

Effect of Tumor-Treating Fields Plus Maintenance Temozolomide vs Maintenance Temozolomide Alone on Survival in Patients With Glioblastoma: A Randomized Clinical Trial

Abstract: Importance Tumor-treating fields (TTFields) is an antimitotic treatment modality that interferes with glioblastoma cell division and organelle assembly by delivering low-intensity alternating electric fields to the tumor. Objective To investigate whether TTFields improves progression-free and overall survival of patients with glioblastoma, a fatal disease that commonly recurs at the initial tumor site or in the central nervous system. Design, Setting, and Participants In this randomized, open-label trial, 695 patients with glioblastoma whose tumor was resected or biopsied and had completed concomitant radiochemotherapy (median time from diagnosis to randomization, 3.8 months) were enrolled at 83 centers (July 2009-2014) and followed up through December 2016. A preliminary report from this trial was published in 2015; this report describes the final analysis. Interventions Patients were randomized 2:1 to TTFields plus maintenance temozolomide chemotherapy (n = 466) or temozolomide alone (n = 229). The TTFields, consisting of low-intensity, 200 kHz frequency, alternating electric fields, was delivered (≥ 18 hours/d) via 4 transducer arrays on the shaved scalp and connected to a portable device. Temozolomide was administered to both groups (150-200 mg/m2) for 5 days per 28-day cycle (6-12 cycles). Main Outcomes and Measures Progression-free survival (tested at α = .046). The secondary end point was overall survival (tested hierarchically at α = .048). Analyses were performed for the intent-to-treat population. Adverse events were compared by group. Results Of the 695 randomized patients (median age, 56 years; IQR, 48-63; 473 men [68%]), 637 (92%) completed the trial. Median progression-free survival from randomization was 6.7 months in the TTFields-temozolomide group and 4.0 months in the temozolomide-alone group (HR, 0.63; 95% CI, 0.52-0.76;P Conclusions and Relevance In the final analysis of this randomized clinical trial of patients with glioblastoma who had received standard radiochemotherapy, the addition of TTFields to maintenance temozolomide chemotherapy vs maintenance temozolomide alone, resulted in statistically significant improvement in progression-free survival and overall survival. These results are consistent with the previous interim analysis. Trial Registration clinicaltrials.gov Identifier:NCT00916409

The international position on laparoscopic liver surgery: The Louisville Statement, 2008.

Abstract: Objective:To summarize the current world position on laparoscopic liver surgery.Summary Background Data:Multiple series have reported on the safety and efficacy of laparoscopic liver surgery. Small and medium sized procedures have become commonplace in many centers, while major laparoscopic liver re

Diagnosis and treatment of polycystic ovary syndrome: an Endocrine Society clinical practice guideline.

Abstract: Objective: The aim was to formulate practice guidelines for the diagnosis and treatment of polycystic ovary syndrome (PCOS). Participants: An Endocrine Society-appointed Task Force of experts, a methodologist, and a medical writer developed the guideline. Evidence: This evidence-based guideline was developed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system to describe both the strength of recommendations and the quality of evidence. Consensus Process: One group meeting, several conference calls, and e-mail communications enabled consensus. Committees and members of The Endocrine Society and the European Society of Endocrinology reviewed and commented on preliminary drafts of these guidelines. Two systematic reviews were conducted to summarize supporting evidence. Conclusions: We suggest using the Rotterdam criteria for diagnosing PCOS (presence of two of the following criteria: androgen excess, ovulatory dysfunction, or polycystic ovaries). Establishing a diagno...

Indication of Electron Neutrino Appearance from an Accelerator-produced Off-axis Muon Neutrino Beam

Abstract: The T2K experiment observes indications of nu(mu) -> nu(mu) e appearance in data accumulated with 1.43 x 10(20) protons on target. Six events pass all selection criteria at the far detector. In a three-flavor neutrino oscillation scenario with |Delta m(23)(2)| = 2.4 x 10(-3) eV(2), sin(2)2 theta(23) = 1 and sin(2)2 theta(13) = 0, the expected number of such events is 1.5 +/- 0.3(syst). Under this hypothesis, the probability to observe six or more candidate events is 7 x 10(-3), equivalent to 2.5 sigma significance. At 90% C.L., the data are consistent with 0.03(0.04) < sin(2)2 theta(13) < 0.28(0.34) for delta(CP) = 0 and a normal (inverted) hierarchy.

Deep Ordinal Regression Network for Monocular Depth Estimation

Abstract: Monocular depth estimation, which plays a crucial role in understanding 3D scene geometry, is an ill-posed problem. Recent methods have gained significant improvement by exploring image-level information and hierarchical features from deep convolutional neural networks (DCNNs). These methods model depth estimation as a regression problem and train the regression networks by minimizing mean squared error, which suffers from slow convergence and unsatisfactory local solutions. Besides, existing depth estimation networks employ repeated spatial pooling operations, resulting in undesirable low-resolution feature maps. To obtain high-resolution depth maps, skip-connections or multilayer deconvolution networks are required, which complicates network training and consumes much more computations. To eliminate or at least largely reduce these problems, we introduce a spacing-increasing discretization (SID) strategy to discretize depth and recast depth network learning as an ordinal regression problem. By training the network using an ordinary regression loss, our method achieves much higher accuracy and faster convergence in synch. Furthermore, we adopt a multi-scale network structure which avoids unnecessary spatial pooling and captures multi-scale information in parallel. The proposed deep ordinal regression network (DORN) achieves state-of-the-art results on three challenging benchmarks, i.e., KITTI [16], Make3D [49], and NYU Depth v2 [41], and outperforms existing methods by a large margin.