Sleep, Dietary Habits, Smoking Status and Physical Activity among Jordanian Nurses

Background: Healthy lifestyle is important in promoting health and reducing risk of chronic diseases. Nurses’ lifestyle could be affected negatively by working night shifts or always rotating shifts, long working hours, and high exposure to work-related stress.  Objectives: This study aims to assess nurses’ lifestyle and factors associated with it.  Methods: A cross-sectional design with an online survey was used in this study. The sample included 203 Jordanian nurses from four hospitals. Sleep quality was assessed using The Pittsburgh Sleep Quality Index, while dietary habits was assessed using the Rapid Eating Assessment for Participants-Shortened Version. Physical activity was assessed using The International Physical Activity Questionnaires.  Results: Nurses’ mean age was 32.7± 21.78 years and on average they have 8.27±5.63 years of experience. Approximately, 25% of nurses were tobacco smokers. The majority of nurses reported poor sleep quality (n = 174, 85.5%). Approximately, 58% of nurses were overweight or obese and 41.9% of nurses had poor dietary habits. Only 39.5% of nurses reported moderate or high levels of activity.  Conclusion: Jordanian nurses’ lifestyle showed poor quality in most aspects.  Implications to nursing: Nurses should be aware of the importance of adopting a healthier lifestyle to prevent possible complications. Nurse leaders should consider the health status of nurses and prevent illnesses by encouraging a healthier lifestyle of nurses

Evolutionary psychology: A how-to guide

Researchers in the social and behavioral sciences are increasingly using evolutionary insights to test novel hypotheses about human psychology. Because evolutionary perspectives are relatively new to psychology and most researchers do not receive formal training in this endeavor, there remains ambiguity about "best practices" for implementing evolutionary principles. This article provides researchers with a practical guide for using evolutionary perspectives in their research programs and for avoiding common pitfalls in doing so. We outline essential elements of an evolutionarily informed research program at 3 central phases: (a) generating testable hypotheses, (b) testing empirical predictions, and (c) interpreting results. We elaborate key conceptual tools, including task analysis, psychological mechanisms, design features, universality, and cost-benefit analysis. Researchers can use these tools to generate hypotheses about universal psychological mechanisms, social and cultural inputs that amplify or attenuate the activation of these mechanisms, and cross-culturally variable behavior that these mechanisms can produce. We hope that this guide inspires theoretically and methodologically rigorous research that more cogently integrates knowledge from the psychological and life sciences. © 2017 American Psychological Association

Robust Biophysical Parameter Estimation with a Neural Network Enhanced Hamiltonian Markov Chain Monte Carlo Sampler

Probabilistic parameter estimation in model fitting runs the gamut from maximum likelihood or maximum a posteriori point estimates from optimization to Markov Chain Monte Carlo (MCMC) sampling. The latter, while more computationally intensive, generally provides a better characterization of the underlying parameter distribution than that of point estimates. However, in order to efficiently explore distributions, MCMC methods ideally require generating uncorrelated samples while also preserving reasonable acceptance probabilities; this becomes particularly important in problematic regions of parameter space. In this paper, we extend a recently proposed Hamiltonian MCMC sampler parametrized by neural networks (L2HMC) by modifying the loss function to jointly optimize the distance between samples and the acceptance probability such that it is stable and efficient. We apply this enhanced sampler to parameter estimation in a recently proposed MRI model, the multi-echo spherical mean technique. We show that it generally outperforms the state of the art Hamiltonian No-U-Turn (NUTS) sampler, L2HMC, and a least squares fitting in terms of accuracy and precision, also enabling the generation of more informative parameter posterior distributions. This illustrates the potential of machine learning enhanced samplers for improving probabilistic parameter estimation for medical imaging applications

Advances in prevention and therapy of neonatal dairy calf diarrhoea : a systematical review with emphasis on colostrum management and fluid therapy

Neonatal calf diarrhoea remains the most common cause of morbidity and mortality in preweaned dairy calves worldwide. This complex disease can be triggered by both infectious and non-infectious causes. The four most important enteropathogens leading to neonatal dairy calf diarrhoea are Escherichia coli, rota-and coronavirus, and Cryptosporidium parvum. Besides treating diarrhoeic neonatal dairy calves, the veterinarian is the most obvious person to advise the dairy farmer on prevention and treatment of this disease. This review deals with prevention and treatment of neonatal dairy calf diarrhoea focusing on the importance of a good colostrum management and a correct fluid therapy

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

Decision-making on numerous aspects of our daily lives is being outsourced to machine-learning algorithms and artificial intelligence (AI), motivated by speed and efficiency in the decision process. Machine learning (ML) approaches - one of the typologies of algorithms underpinning artificial intelligence - are typically developed as black boxes. The implication is that ML code scripts are rarely scrutinised; interpretability is usually sacrificed in favour of usability and effectiveness. Room for improvement in practices associated with programme development have also been flagged along other dimensions, including inter alia fairness, accuracy, accountability, and transparency. In this contribution, the production of guidelines and dedicated documents around these themes is discussed. The following applications of AI-driven decision making are outlined: a) Risk assessment in the criminal justice system, and b) autonomous vehicles, highlighting points of friction across ethical principles. Possible ways forward towards the implementation of governance on AI are finally examined

Adventitial SCA-1+ progenitor cell gene sequencing reveals the mechanisms of cell migration in response to hyperlipidemia

Adventitial progenitor cells, including SCA-1+ and mesenchymal stem cells, are believed to be important in vascular remodeling. It has been shown that SCA-1+ progenitor cells are involved in neointimal hyperplasia of vein grafts, but little is known concerning their involvement in hyperlipidemia-induced atherosclerosis. We employed single-cell sequencing technology on primary adventitial mouse SCA-1+ cells from wild-type and atherosclerotic-prone (ApoE-deficient) mice and found that a group of genes controlling cell migration and matrix protein degradation was highly altered. Adventitial progenitors from ApoE-deficient mice displayed an augmented migratory potential both in vitro and in vivo. This increased migratory ability was mimicked by lipid loading to SCA-1+ cells. Furthermore, we show that lipid loading increased miRNA-29b expression and induced sirtuin-1 and matrix metalloproteinase-9 levels to promote cell migration. These results provide direct evidence that blood cholesterol levels influence vascular progenitor cell function, which could be a potential target cell for treatment of vascular disease

Low back pain in general practice: cost-effectiveness of a minimal psychosocial intervention versus usual care

An intervention that can prevent low back pain (LBP) becoming chronic, may not only prevent great discomfort for patients, but also save substantial costs for the society. Psychosocial factors appear to be of importance in the transition of acute to chronic LBP. The aim of this study was to compare the cost-effectiveness of an intervention aimed at psychosocial factors to usual care in patients with (sub)acute LBP. The study design was an economic evaluation alongside a cluster-randomized controlled trial, conducted from a societal perspective with a follow-up of 1 year. Sixty general practitioners in 41 general practices recruited 314 patients with non-specific LBP of less than 12 weeks’ duration. General practitioners in the minimal intervention strategy (MIS) group explored and discussed psychosocial prognostic factors. Usual care (UC) was not protocolized. Clinical outcomes were functional disability (Roland–Morris Disability Questionnaire), perceived recovery and health-related quality of life (EuroQol). Cost data consisted of direct and indirect costs and were measured by patient cost diaries and general practitioner registration forms. Complete cost data were available for 80% of the patients. Differences in clinical outcomes between both the groups were small and not statistically significant. Differences in cost data were in favor of MIS. However, the complete case analysis and the sensitivity analyses with imputed cost data were inconsistent with regard to the statistical significance of this difference in cost data. This study presents conflicting points of view regarding the cost-effectiveness of MIS. We conclude that (Dutch) general practitioners, as yet, should not replace their usual care by this new intervention

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension

The SPIRIT 2013 (The Standard Protocol Items: Recommendations for Interventional Trials) statement aims to improve the completeness of clinical trial protocol reporting, by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there is a growing recognition that interventions involving artificial intelligence need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI extension is a new reporting guideline for clinical trials protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI. Both guidelines were developed using a staged consensus process, involving a literature review and expert consultation to generate 26 candidate items, which were consulted on by an international multi-stakeholder group in a 2-stage Delphi survey (103 stakeholders), agreed on in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items, which were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations around the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer-reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human–AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret, and critically appraise the design and risk of bias for a planned clinical trial