Sequential adjuvant chemotherapy and radiotherapy in endometrial cancer--results from two randomised studies.

INTRODUCTION: Endometrial cancer patients with high grade tumours, deep myometrial invasion, or advanced stage disease have a poor prognosis. Randomized studies have demonstrated prevention of loco-regional relapses with radiotherapy with no effect on overall survival. The possible additive effect of chemotherapy remains unclear. Two randomized clinical trials (NSGO-EC-9501/EORTC-55991 and MaNGO ILIADE-III) were undertaken to clarify if sequential combination of chemotherapy and radiotherapy improves progression-free survival in high-risk endometrial cancer. The two studies were pooled. METHODS: Patients (n=540; 534 evaluable) with operated endometrial cancer FIGO stage I-III with no residual tumour and prognostic factors implying high-risk were randomly allocated to adjuvant radiotherapy with or without sequential chemotherapy. RESULTS: In the NSGO/EORTC study, combined modality treatment was associated with a 36 % reduction in the risk for relapse or death (HR 0.64, 95 % CI 0.41-0.99; P=0.04); two-sided tests were used. The result from the MaNGO-study pointed in the same direction (HR 0.61), but was not significant. In combined analysis, the estimate of risk for relapse or death was similar but with narrower confidence limits (HR 0.63, CI 0.44-0.89; P=0.009). Neither study showed significant differences in overall survival. In combined analysis, overall survival approached statistical significance (HR 0.69, CI 0.46-1.03; P = 0.07) and cancer-specific survival was significant (HR 0.55, CI 0.35-0.88; p=0.01). CONCLUSION: Addition of adjuvant chemotherapy to radiation improves progression-free survival in operated endometrial cancer patients with no residual tumour and high risk profile. A remaining question for future studies is if addition of radiotherapy to chemotherapy improves the results

Molecular Dynamics Simulation Study and Hybrid Pharmacophore Model Development in Human LTA4H Inhibitor Design

Human leukotriene A4 hydrolase (hLTA4H) is a bi-functional enzyme catalyzes the hydrolase and aminopeptidase functions upon the fatty acid and peptide substrates, respectively, utilizing the same but overlapping binding site. Particularly the hydrolase function of this enzyme catalyzes the rate-limiting step of the leukotriene (LT) cascade that converts the LTA4 to LTB4. This product is a potent pro-inflammatory activator of inflammatory responses and thus blocking this conversion provides a valuable means to design anti-inflammatory agents. Four structurally very similar chemical compounds with highly different inhibitory profile towards the hydrolase function of hLTA4H were selected from the literature. Molecular dynamics (MD) simulations of the complexes of hLTA4H with these inhibitors were performed and the results have provided valuable information explaining the reasons for the differences in their biological activities. Binding mode analysis revealed that the additional thiophene moiety of most active inhibitor helps the pyrrolidine moiety to interact the most important R563 and K565 residues. The hLTA4H complexes with the most active compound and substrate were utilized in the development of hybrid pharmacophore models. These developed pharmacophore models were used in screening chemical databases in order to identify lead candidates to design potent hLTA4H inhibitors. Final evaluation based on molecular docking and electronic parameters has identified three compounds of diverse chemical scaffolds as potential leads to be used in novel and potent hLTA4H inhibitor design

Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design

This paper reviews past and ongoing efforts in using high-throughput ab-inito calculations in combination with machine learning models for materials design. The primary focus is on bulk materials, i.e., materials with fixed, ordered, crystal structures, although the methods naturally extend into more complicated configurations. Efficient and robust computational methods, computational power, and reliable methods for automated database-driven high-throughput computation are combined to produce high-quality data sets. This data can be used to train machine learning models for predicting the stability of bulk materials and their properties. The underlying computational methods and the tools for automated calculations are discussed in some detail. Various machine learning models and, in particular, descriptors for general use in materials design are also covered.Comment: 19 pages, 2 figure

Understanding sports-HCI by going jogging at CHI

More and more technologies are emerging that aim to support sports activities, for example there are jogging apps, cycling computers and quadcopters for sportspeople to videorecord their actions. These new technologies appear to become more and more popular, yet interaction design knowledge how to support the associated exertion experiences is still limited. In order to bring practitioners and academics interested in sports-HCI together and examine the topic "in the wild", we propose to go outside and jog around the CHI venue while using and discussing some of these new technologies. The goal is to investigate and shape the future of the field of sports-HCI

Short-term effect of external cardioversion on patients with pacemakers and atrial fibrillation/flutter

Background: Previous case reports have demonstrated increased threshold and exit block due to external cardioversion (ECV).International guidelines from 2006 recommend that ECV is performed with anterior-posterior paddle orientation, that the anterior paddle is placed with minimum distance of 8 cm from the pulse generator, and that the cardiac device is interrogated before and after ECV. Currently, there are to our knowledge no larger studies that have examined the effect of ECV on pacemakers

Updated results from OCTAVIA (front-line bevacizumab, carboplatin and weekly paclitaxel therapy for ovarian cancer)

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