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Institution

Katholieke Universiteit Leuven

EducationLeuven, Belgium
About: Katholieke Universiteit Leuven is a education organization based out in Leuven, Belgium. It is known for research contribution in the topics: Population & Transplantation. The organization has 61109 authors who have published 176584 publications receiving 6210872 citations.


Papers
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Journal ArticleDOI
TL;DR: How the mitochondria has a key role in regulating the interplay between redox homeostasis and metabolism within tumor cells is described, and the potential therapeutic use of agents that directly or indirectly block metabolism is discussed.
Abstract: Tumor cells harbor genetic alterations that promote a continuous and elevated production of reactive oxygen species. Whereas such oxidative stress conditions would be harmful to normal cells, they facilitate tumor growth in multiple ways by causing DNA damage and genomic instability, and ultimately, by reprogramming cancer cell metabolism. This review outlines the metabolic-dependent mechanisms that tumors engage in when faced with oxidative stress conditions that are critical for cancer progression by producing redox cofactors. In particular, we describe how the mitochondria has a key role in regulating the interplay between redox homeostasis and metabolism within tumor cells. Last, we will discuss the potential therapeutic use of agents that directly or indirectly block metabolism.

822 citations

Journal ArticleDOI
TL;DR: Intestinal contents trigger postoperative recurrence of Crohn's disease in the terminal ileum proximal to the ileocolonic anastomosis in the first days after surgery.

819 citations

Journal ArticleDOI
TL;DR: In this article, a review of the knowledge of aerogel insulation in general and for building applications in particular is given, where the possibility of high transmittances in the solar spectrum is of high interest for the construction sector.

819 citations

Journal ArticleDOI
TL;DR: This phase 3 randomised controlled trial assessed whether dose intensification of doxorubicin with ifosfamide improves survival of patients with advanced soft-tissue sarcoma compared with doxorbicin alone.
Abstract: Summary Background Effective targeted treatment is unavailable for most sarcomas and doxorubicin and ifosfamide—which have been used to treat soft-tissue sarcoma for more than 30 years—still have an important role Whether doxorubicin alone or the combination of doxorubicin and ifosfamide should be used routinely is still controversial We assessed whether dose intensification of doxorubicin with ifosfamide improves survival of patients with advanced soft-tissue sarcoma compared with doxorubicin alone Methods We did this phase 3 randomised controlled trial (EORTC 62012) at 38 hospitals in ten countries We included patients with locally advanced, unresectable, or metastatic high-grade soft-tissue sarcoma, age 18–60 years with a WHO performance status of 0 or 1 They were randomly assigned (1:1) by the minimisation method to either doxorubicin (75 mg/m 2 by intravenous bolus on day 1 or 72 h continuous intravenous infusion) or intensified doxorubicin (75 mg/m 2 ; 25 mg/m 2 per day, days 1–3) plus ifosfamide (10 g/m 2 over 4 days with mesna and pegfilgrastim) as first-line treatment Randomisation was stratified by centre, performance status (0 vs 1), age ( vs ≥50 years), presence of liver metastases, and histopathological grade (2 vs 3) Patients were treated every 3 weeks till progression or unacceptable toxic effects for up to six cycles The primary endpoint was overall survival in the intention-to-treat population The trial is registered with ClinicalTrialsgov, number NCT00061984 Findings Between April 30, 2003, and May 25, 2010, 228 patients were randomly assigned to receive doxorubicin and 227 to receive doxorubicin and ifosfamide Median follow-up was 56 months (IQR 31–77) in the doxorubicin only group and 59 months (36–72) in the combination group There was no significant difference in overall survival between groups (median overall survival 12·8 months [95·5% CI 10·5–14·3] in the doxorubicin group vs 14·3 months [12·5–16·5] in the doxorubicin and ifosfamide group; hazard ratio [HR] 0·83 [95·5% CI 0·67–1·03]; stratified log-rank test p=0·076) Median progression-free survival was significantly higher for the doxorubicin and ifosfamide group (7·4 months [95% CI 6·6–8·3]) than for the doxorubicin group (4·6 months [2·9–5·6]; HR 0·74 [95% CI 0·60–0·90], stratified log-rank test p=0·003) More patients in the doxorubicin and ifosfamide group than in the doxorubicin group had an overall response (60 [26%] of 227 patients vs 31 [14%] of 228; p vs 40 [18%] of 223 patients), neutropenia (93 [42%] vs 83 [37%]), febrile neutropenia (103 (46%) vs 30 [13%]), anaemia (78 [35%] vs 10 [5%]), and thrombocytopenia (75 [33%]) vs one [ Interpretation Our results do not support the use of intensified doxorubicin and ifosfamide for palliation of advanced soft-tissue sarcoma unless the specific goal is tumour shrinkage These findings should help individualise the care of patients with this disease Funding Cancer Research UK, EORTC Charitable Trust, UK NHS, Canadian Cancer Society Research Institute, Amgen

819 citations

Proceedings Article
01 Jan 2016
TL;DR: In this article, the Dynamic Filter Network (DFN) is proposed, where filters are generated dynamically conditioned on an input, and a wide variety of filtering operation can be learned this way, including local spatial transformations, selective (de)blurring or adaptive feature extraction.
Abstract: In a traditional convolutional layer, the learned filters stay fixed after training. In contrast, we introduce a new framework, the Dynamic Filter Network, where filters are generated dynamically conditioned on an input. We show that this architecture is a powerful one, with increased flexibility thanks to its adaptive nature, yet without an excessive increase in the number of model parameters. A wide variety of filtering operation can be learned this way, including local spatial transformations, but also others like selective (de)blurring or adaptive feature extraction. Moreover, multiple such layers can be combined, e.g. in a recurrent architecture. We demonstrate the effectiveness of the dynamic filter network on the tasks of video and stereo prediction, and reach state-of-the-art performance on the moving MNIST dataset with a much smaller model. By visualizing the learned filters, we illustrate that the network has picked up flow information by only looking at unlabelled training data. This suggests that the network can be used to pretrain networks for various supervised tasks in an unsupervised way, like optical flow and depth estimation.

819 citations


Authors

Showing all 61602 results

NameH-indexPapersCitations
Eugene Braunwald2301711264576
Joseph L. Goldstein207556149527
Rakesh K. Jain2001467177727
Stefan Schreiber1781233138528
Masayuki Yamamoto1711576123028
Jun Wang1661093141621
David R. Jacobs1651262113892
Klaus Müllen1642125140748
Peter Carmeliet164844122918
Hua Zhang1631503116769
William J. Sandborn1621317108564
Elliott M. Antman161716179462
Tobin J. Marks1591621111604
Ian A. Wilson15897198221
Johan Auwerx15865395779
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023307
2022857
202111,007
202010,541
20199,719
20189,532