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Institution

University of Groningen

EducationGroningen, Groningen, Netherlands
About: University of Groningen is a education organization based out in Groningen, Groningen, Netherlands. It is known for research contribution in the topics: Population & Poison control. The organization has 36346 authors who have published 69116 publications receiving 2940370 citations. The organization is also known as: Rijksuniversiteit Groningen & RUG.


Papers
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Journal ArticleDOI
TL;DR: An overview of some of the more popular CG models used in biomolecular applications to date, focusing on models that retain chemical specificity, are provided.
Abstract: Computational modeling of biological systems is challenging because of the multitude of spatial and temporal scales involved. Replacing atomistic detail with lower resolution, coarse grained (CG), beads has opened the way to simulate large-scale biomolecular processes on time scales inaccessible to all-atom models. We provide an overview of some of the more popular CG models used in biomolecular applications to date, focusing on models that retain chemical specificity. A few state-of-the-art examples of protein folding, membrane protein gating and self-assembly, DNA hybridization, and modeling of carbohydrate fibers are used to illustrate the power and diversity of current CG modeling.

464 citations

Journal ArticleDOI
TL;DR: Brigatinib yielded substantial whole-body and intracranial responses as well as robust progression-free survival; 180 mg (with lead-in) showed consistently better efficacy than 90 mg, with acceptable safety.
Abstract: PurposeMost crizotinib-treated patients with anaplastic lymphoma kinase gene (ALK)–rearranged non–small-cell lung cancer (ALK-positive NSCLC) eventually experience disease progression. We evaluated two regimens of brigatinib, an investigational next-generation ALK inhibitor, in crizotinib-refractory ALK-positive NSCLC.Patients and MethodsPatients were stratified by brain metastases and best response to crizotinib. They were randomly assigned (1:1) to oral brigatinib 90 mg once daily (arm A) or 180 mg once daily with a 7-day lead-in at 90 mg (180 mg once daily [with lead-in]; arm B). Investigator-assessed confirmed objective response rate (ORR) was the primary end point.ResultsOf 222 patients enrolled (arm A: n = 112, 109 treated; arm B: n = 110, 110 treated), 154 (69%) had baseline brain metastases and 164 of 222 (74%) had received prior chemotherapy. With 8.0-month median follow-up, investigator-assessed confirmed ORR was 45% (97.5% CI, 34% to 56%) in arm A and 54% (97.5% CI, 43% to 65%) in arm B. Invest...

463 citations

Journal ArticleDOI
TL;DR: In this paper, the authors described the collective nuclear states by symmetric couplings of proton and neutron pairs, represented by s- and d-bosons respectively, and the multiplet structure of the combined system was given by representations of the SU (6) × SU (2) group, the Arima-Iachello interacting bosons corresponding to the fully symmetric ones.

463 citations

Journal ArticleDOI
TL;DR: This review highlights the different traditional and emerging tools and strategies applied to identify subsets of metabolites detected in untargeted metabolomic studies applying various mass spectrometry platforms.
Abstract: Metabolomics has advanced significantly in the past 10 years with important developments related to hardware, software and methodologies and an increasing complexity of applications. In discovery-based investigations, applying untargeted analytical methods, thousands of metabolites can be detected with no or limited prior knowledge of the metabolite composition of samples. In these cases, metabolite identification is required following data acquisition and processing. Currently, the process of metabolite identification in untargeted metabolomic studies is a significant bottleneck in deriving biological knowledge from metabolomic studies. In this review we highlight the different traditional and emerging tools and strategies applied to identify subsets of metabolites detected in untargeted metabolomic studies applying various mass spectrometry platforms. We indicate the workflows which are routinely applied and highlight the current limitations which need to be overcome to provide efficient, accurate and robust identification of metabolites in untargeted metabolomic studies. These workflows apply to the identification of metabolites, for which the structure can be assigned based on entries in databases, and for those which are not yet stored in databases and which require a de novo structure elucidation.

463 citations

Journal ArticleDOI
TL;DR: Social media use by patients was found to affect the healthcare professional and patient relationship, by leading to more equal communication between the patient and healthcare professional, increased switching of doctors, harmonious relationships, and suboptimal interaction.
Abstract: Background Since the emergence of social media in 2004, a growing percentage of patients use this technology for health related reasons. To reflect on the alleged beneficial and potentially harmful effects of social media use by patients, the aim of this paper is to provide an overview of the extant literature on the effects of social media use for health related reasons on patients and their relationship with healthcare professionals. Methods We conducted a systematic literature review on empirical research regarding the effects of social media use by patients for health related reasons. The papers we included met the following selection criteria: (1) published in a peer-reviewed journal, (2) written in English, (3) full text available to the researcher, (4) contain primary empirical data, (5) the users of social media are patients, (6) the effects of patients using social media are clearly stated, (7) satisfy established quality criteria. Results Initially, a total of 1,743 articles were identified from which 22 were included in the study. From these articles six categories of patients’ use of social media were identified, namely: emotional, information, esteem, network support, social comparison and emotional expression. The types of use were found to lead to seven identified types of effects on patients, namely improved self-management and control, enhanced psychological well-being, and enhanced subjective well-being, diminished subjective well-being, addiction to social media, loss of privacy, and being targeted for promotion. Social media use by patients was found to affect the healthcare professional and patient relationship, by leading to more equal communication between the patient and healthcare professional, increased switching of doctors, harmonious relationships, and suboptimal interaction between the patient and healthcare professional. Conclusions Our review provides insights into the emerging utilization of social media in healthcare. In particular, it identifies types of use by patients as well as the effects of such use, which may differ between patients and doctors. Accordingly, our results framework and propositions can serve to guide future research, and they also have practical implications for healthcare providers and policy makers.

462 citations


Authors

Showing all 36692 results

NameH-indexPapersCitations
Ronald C. Kessler2741332328983
Nicholas J. Wareham2121657204896
André G. Uitterlinden1991229156747
Lei Jiang1702244135205
Brenda W.J.H. Penninx1701139119082
Richard H. Friend1691182140032
Panos Deloukas162410154018
Jerome I. Rotter1561071116296
Christopher M. Dobson1501008105475
Dirk Inzé14964774468
Scott T. Weiss147102574742
Dieter Lutz13967167414
Wilmar B. Schaufeli13751395718
Cisca Wijmenga13666886572
Arnold B. Bakker135506103778
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023166
2022543
20214,487
20203,990
20193,283
20182,836