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- Mavaddat, N, et al.
(författare)
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Risk-reducing salpingo-oophorectomy, natural menopause, and breast cancer risk: an international prospective cohort of BRCA1 and BRCA2 mutation carriers
- 2020
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Ingår i: Breast cancer research : BCR. - : Springer Science and Business Media LLC. - 1465-542X. ; 22:1, s. 8-
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundThe effect of risk-reducing salpingo-oophorectomy (RRSO) on breast cancer risk forBRCA1andBRCA2mutation carriers is uncertain. Retrospective analyses have suggested a protective effect but may be substantially biased. Prospective studies have had limited power, particularly forBRCA2mutation carriers. Further, previous studies have not considered the effect of RRSO in the context of natural menopause.MethodsA multi-centre prospective cohort of 2272BRCA1and 1605BRCA2mutation carriers was followed for a mean of 5.4 and 4.9 years, respectively; 426 women developed incident breast cancer. RRSO was modelled as a time-dependent covariate in Cox regression, and its effect assessed in premenopausal and postmenopausal women.ResultsThere was no association between RRSO and breast cancer forBRCA1(HR = 1.23; 95% CI 0.94–1.61) orBRCA2(HR = 0.88; 95% CI 0.62–1.24) mutation carriers. ForBRCA2mutation carriers, HRs were 0.68 (95% CI 0.40–1.15) and 1.07 (95% CI 0.69–1.64) for RRSO carried out before or after age 45 years, respectively. The HR forBRCA2mutation carriers decreased with increasing time since RRSO (HR = 0.51; 95% CI 0.26–0.99 for 5 years or longer after RRSO). Estimates for premenopausal women were similar.ConclusionWe found no evidence that RRSO reduces breast cancer risk forBRCA1mutation carriers. A potentially beneficial effect forBRCA2mutation carriers was observed, particularly after 5 years following RRSO. These results may inform counselling and management of carriers with respect to RRSO.
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- Quintero, Luis, et al.
(författare)
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Excite-O-Meter : an Open-Source Unity Plugin to Analyze Heart Activity and Movement Trajectories in Custom VR Environments
- 2022
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Ingår i: 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). - Los Alamitos : IEEE. - 9781665484039 - 9781665484022 ; , s. 46-47
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Konferensbidrag (refereegranskat)abstract
- This article explains the new features of the Excite-O-Meter, an open-source tool that enables the collection of bodily data, real-time feature extraction, and post-session data visualization in any custom VR environment developed in Unity. Besides analyzing heart activity, the tool supports now multidimensional time series to study motion trajectories in VR. The paper presents the main functionalities and discusses the relevance of the tool for behavioral and psychophysiological research.
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- Velez Quintero, Luis Eduardo, et al.
(författare)
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Excite-O-Meter : Software Framework to Integrate Heart Activity in Virtual Reality
- 2021
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Ingår i: 2021 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). - : IEEE. - 9781665401586 ; , s. 357-366
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Konferensbidrag (refereegranskat)abstract
- Bodily signals can complement subjective and behavioral measures to analyze human factors, such as user engagement or stress, when interacting with virtual reality (VR) environments. Enabling widespread use of (also the real-time analysis) of bodily signals in VR applications could be a powerful method to design more user-centric, personalized VR experiences. However, technical and scientific challenges (e.g., cost of research-grade sensing devices, required coding skills, expert knowledge needed to interpret the data) complicate the integration of bodily data in existing interactive applications. This paper presents the design, development, and evaluation of an open-source software framework named Excite-O-Meter. It allows existing VR applications to integrate, record, analyze, and visualize bodily signals from wearable sensors, with the example of cardiac activity (heart rate and its variability) from the chest strap Polar H10. Survey responses from 58 potential users determined the design requirements for the framework. Two tests evaluated the framework and setup in terms of data acquisition/analysis and data quality. Finally, we present an example experiment that shows how our tool can be an easy-to-use and scientifically validated tool for researchers, hobbyists, or game designers to integrate bodily signals in VR applications.
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