Bioelectrical impedance vector analysis (BIVA) in sport and exercise: Systematic review and future perspectives

Background Bioelectrical impedance vector analysis (BIVA) is a general concept that includes all methodologies used in the analysis of the bioelectrical vector, whereas the "classic" BIVA is a patented methodology included among these methods of analysis. Once this was clarified, the systematic review of the literature provides a deeper insight into the scope and range of application of BIVA in sport and exercise. Objective The main goal of this work was to systematically review the sources on the applications of BIVA in sport and exercise and to examine its usefulness and suitability as a technique for the evaluation of body composition, hydration status, and other physiological and clinical relevant characteristics, ultimately to trace future perspectives in this growing area, including a proposal for a research agenda. Methods Systematic literature searches in PubMed, SPORTDiscus and Scopus databases up to July, 2017 were conducted on any empirical investigations using phase-sensitive bioimpedance instruments to perform BIVA within exercise and sport contexts. The search included healthy sedentary individuals, physically active subjects and athletes. Result Nineteen eligible papers were included and classified as sixteen original articles and three scientific conference communications. Three studies analysed short-term variations in the hydration status evoked by exercise/training through whole-body measurements, eleven assessed whole-body body composition changes induced by long-term exercise, four compared athletic groups or populations using the whole-body assessment, and two analysed bioelectrical patterns of athletic injuries or muscle damage through localised bioimpedance measurements. Conclusions BIVA is a relatively new technique that has potential in sport and exercise, especially for the assessment of soft-tissue injury. On the other hand, the current tolerance ellipses of “classic” BIVA are not a valid method to identify dehydration in individual athletes and a new approach is needed. “Specific” BIVA, a method which proposes a correction of bioelectrical values for body geometry, emerges as the key to overcome “classic” BIVA limitations regarding the body composition assessment. Further research establishing standardised testing procedures and investigating the relationship between physiology and the bioelectrical signal in sport and exercise is needed. © 2018 Castizo-Olier et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Scopu

Dynamics of FitzHugh-Nagumo excitable systems with delayed coupling

Small lattices of $N$ nearest neighbor coupled excitable FitzHugh-Nagumo systems, with time-delayed coupling are studied, and compared with systems of FitzHugh-Nagumo oscillators with the same delayed coupling. Bifurcations of equilibria in N=2 case are studied analytically, and it is then numerically confirmed that the same bifurcations are relevant for the dynamics in the case $N>2$. Bifurcations found include inverse and direct Hopf and fold limit cycle bifurcations. Typical dynamics for different small time-lags and coupling intensities could be excitable with a single globally stable equilibrium, asymptotic oscillatory with symmetric limit cycle, bi-stable with stable equilibrium and a symmetric limit cycle, and again coherent oscillatory but non-symmetric and phase-shifted. For an intermediate range of time-lags inverse sub-critical Hopf and fold limit cycle bifurcations lead to the phenomenon of oscillator death. The phenomenon does not occur in the case of FitzHugh-Nagumo oscillators with the same type of coupling.Comment: accepted by Phys.Rev.

Real-world data of fulvestrant as first-line treatment of postmenopausal women with estrogen receptor-positive metastatic breast cancer

Goals of endocrine therapy for advanced breast cancer (ABC) include prolonging survival rates, maintaining the quality of life, and delaying the initiation of chemotherapy. We evaluated the effectiveness of fulvestrant as first-line in patients with estrogen receptor (ER)-positive ABC with relapse during or after adjuvant anti-estrogenic therapy in real-world settings. Retrospective, observational study involving 171 postmenopausal women with ER-positive ABC who received fulvestrant as first-line between January 2011 and May 2018 in Spanish hospitals. With a median follow-up of 31.4 months, the progression-free survival (PFS) with fulvestrant was 14.6 months. No differences were seen in the visceral metastatic (14.3 months) versus non-visceral (14.6 months) metastatic subgroup for PFS. Overall response rate and clinical benefit rate were 35.2% and 82.8%. Overall survival was 43.1 months. The duration of the clinical benefit was 19.2 months. Patients with ECOG performance status 0 at the start of treatment showed a significant greater clinical benefit rate and overall survival than with ECOG 1-2. Results in real-world settings are in concordance with randomized clinical trials. Fulvestrant continues to demonstrate clinical benefits in real-world settings and appears be well tolerated as first-line for the treatment of postmenopausal women with ER-positive ABC

rEHR: An R package for manipulating and analysing Electronic Health Record data

Research with structured Electronic Health Records (EHRs) is expanding as data becomes more accessible; analytic methods advance; and the scientific validity of such studies is increasingly accepted. However, data science methodology to enable the rapid searching/extraction, cleaning and analysis of these large, often complex, datasets is less well developed. In addition, commonly used software is inadequate, resulting in bottlenecks in research workflows and in obstacles to increased transparency and reproducibility of the research. Preparing a research-ready dataset from EHRs is a complex and time consuming task requiring substantial data science skills, even for simple designs. In addition, certain aspects of the workflow are computationally intensive, for example extraction of longitudinal data and matching controls to a large cohort, which may take days or even weeks to run using standard software. The rEHR package simplifies and accelerates the process of extracting ready-for-analysis datasets from EHR databases. It has a simple import function to a database backend that greatly accelerates data access times. A set of generic query functions allow users to extract data efficiently without needing detailed knowledge of SQL queries. Longitudinal data extractions can also be made in a single command, making use of parallel processing. The package also contains functions for cutting data by time-varying covariates, matching controls to cases, unit conversion and construction of clinical code lists. There are also functions to synthesise dummy EHR. The package has been tested with one for the largest primary care EHRs, the Clinical Practice Research Datalink (CPRD), but allows for a common interface to other EHRs. This simplified and accelerated work flow for EHR data extraction results in simpler, cleaner scripts that are more easily debugged, shared and reproduced

Association of different antiplatelet therapies with mortality after primary percutaneous coronary intervention.

OBJECTIVES: Prasugrel and ticagrelor both reduce ischaemic endpoints in high-risk acute coronary syndromes, compared with clopidogrel. However, comparative outcomes of these two newer drugs in the context of primary percutaneous coronary intervention (PCI) for ST-elevation myocardial infarction (STEMI) remains unclear. We sought to examine this question using the British Cardiovascular Interventional Society national database in patients undergoing primary PCI for STEMI. METHODS: Data from January 2007 to December 2014 were used to compare use of P2Y12 antiplatelet drugs in primary PCI in >89 000 patients. Statistical modelling, involving propensity matching, multivariate logistic regression (MLR) and proportional hazards modelling, was used to study the association of different antiplatelet drug use with all-cause mortality. RESULTS: In our main MLR analysis, prasugrel was associated with significantly lower mortality than clopidogrel at both 30 days (OR 0.87, 95% CI 0.78 to 0.97, P=0.014) and 1 year (OR 0.89, 95% CI 0.82 to 0.97, P=0.011) post PCI. Ticagrelor was not associated with any significant differences in mortality compared with clopidogrel at either 30 days (OR 1.07, 95% CI 0.95 to 1.21, P=0.237) or 1 year (OR 1.058, 95% CI 0.96 to 1.16, P=0.247). Finally, ticagrelor was associated with significantly higher mortality than prasugrel at both time points (30 days OR 1.22, 95% CI 1.03 to 1.44, P=0.020; 1 year OR 1.19 95% CI 1.04 to 1.35, P=0.01). CONCLUSIONS: In a cohort of over 89 000 patients undergoing primary PCI for STEMI in the UK, prasugrel is associated with a lower 30-day and 1-year mortality than clopidogrel and ticagrelor. Given that an adequately powered comparative randomised trial is unlikely to be performed, these data may have implications for routine care

Enhanced survival prediction using explainable artificial intelligence in heart transplantation.

The most limiting factor in heart transplantation is the lack of donor organs. With enhanced prediction of outcome, it may be possible to increase the life-years from the organs that become available. Applications of machine learning to tabular data, typical of clinical decision support, pose the practical question of interpretation, which has technical and potential ethical implications. In particular, there is an issue of principle about the predictability of complex data and whether this is inherent in the data or strongly dependent on the choice of machine learning model, leading to the so-called accuracy-interpretability trade-off. We model 1-year mortality in heart transplantation data with a self-explaining neural network, which is benchmarked against a deep learning model on the same development data, in an external validation study with two data sets: (1) UNOS transplants in 2017-2018 (n = 4750) for which the self-explaining and deep learning models are comparable in their AUROC 0.628 [0.602,0.654] cf. 0.635 [0.609,0.662] and (2) Scandinavian transplants during 1997-2018 (n = 2293), showing good calibration with AUROCs of 0.626 [0.588,0.665] and 0.634 [0.570, 0.698], respectively, with and without missing data (n = 982). This shows that for tabular data, predictive models can be transparent and capture important nonlinearities, retaining full predictive performance

Développement d'un outil informatique opérationnel d'aide à la décision et de modélisation des pollutions en Méditerranée

National audienc

Multi-task learning with a natural metric for quantitative structure activity relationship learning

The goal of Quantitative Structure Activity Relationship (QSAR) Learning is to learn a function that, given the structure of a small molecule (a potential drug), outputs the predicted activity of the compound. We employed multi-task learning (MTL) to exploit commonalities in drug targets and assays. We used datasets containing curated records about the activity of speci c compounds on drug targets provided by ChEMBL. Totally, 1091 assays have been analysed. As a baseline, a single task learning approach that trains random forest to predict drug activity for each drug target individually was considered. We then carried out feature-based and instance-based MTL to predict drug activities. We introduced a natural metric of evolutionary distance between drug targets as a measure of tasks relatedness. Instance-based MTL signi cantly outperformed both, feature-based MTL and the base learner, on 741 drug targets out of 1091. Feature-based MTL won on 179 occasions and the base learner performed best on 171 drug targets. We conclude that MTL QSAR is improved by incorporating the evolutionary distance between targets. These results indicate that QSAR learning can be performed effectively, even if little data is available for speci c drug targets, by leveraging what is known about similar drug targets

Macrocyclic colibactin induces DNA double-strand breaks via copper-mediated oxidative cleavage.

Colibactin is an assumed human gut bacterial genotoxin, whose biosynthesis is linked to the clb genomic island that has a widespread distribution in pathogenic and commensal human enterobacteria. Colibactin-producing gut microbes promote colon tumour formation and enhance the progression of colorectal cancer via cellular senescence and death induced by DNA double-strand breaks (DSBs); however, the chemical basis that contributes to the pathogenesis at the molecular level has not been fully characterized. Here, we report the discovery of colibactin-645, a macrocyclic colibactin metabolite that recapitulates the previously assumed genotoxicity and cytotoxicity. Colibactin-645 shows strong DNA DSB activity in vitro and in human cell cultures via a unique copper-mediated oxidative mechanism. We also delineate a complete biosynthetic model for colibactin-645, which highlights a unique fate of the aminomalonate-building monomer in forming the C-terminal 5-hydroxy-4-oxazolecarboxylic acid moiety through the activities of both the polyketide synthase ClbO and the amidase ClbL. This work thus provides a molecular basis for colibactin's DNA DSB activity and facilitates further mechanistic study of colibactin-related colorectal cancer incidence and prevention