Allostatic load and preterm birth

Preterm birth is a universal health problem that is one of the largest unmet medical needs contributing to the global burden of disease. Adding to its complexity is that there are no means to predict who is at risk when pregnancy begins or when women will actually deliver. Until these problems are addressed, there will be no interventions to reduce the risk because those who should be treated will not be known. Considerable evidence now exists that chronic life, generational or accumulated stress is a risk factor for preterm delivery in animal models and in women. This wear and tear on the body and mind is called allostatic load. This review explores the evidence that chronic stress contributes to preterm birth and other adverse pregnancy outcomes in animal and human studies. It explores how allostatic load can be used to, firstly, model stress and preterm birth in animal models and, secondly, how it can be used to develop a predictive model to assess relative risk among women in early pregnancy. Once care providers know who is in the highest risk group, interventions can be developed and applied to mitigate their risk

Different levels of cardiometabolic indicators in multiple vs. singleton children

Background We aimed to compare cardiometabolic indicators in singletons and multiples at age 7 and explore the birthweight mediation effect. Methods We studied 5431 singletons and 103 sets of multiples from Generation XXI birth cohort. Anthropometric measurements, body composition, and fasting blood samples were obtained. Age- and sex-specific z-scores were calculated (additionally height-specific for blood pressure). Adjusted regression coefficients and respective 95% confidence intervals [β (95%CI)] were computed using path analysis. Results Multiples had lower weight [− 0.419 (− 0.616;-0.223)], height [− 0.404 (− 0.594;-0.213)], BMI [− 0.470 (− 0.705;-0.234)], fat mass index [− 0.359 (− 0.565;-0.152)], waist circumference [− 0.342 (− 0.537;-0.147)], and waist-to-height ratio [− 0.165 (− 0.326;-0.003)] z-scores. These results were explained by the indirect effect via birthweight, which was also negative and significant for all the aforementioned cardiometabolic indicators, while no direct effect was present. There were also significant indirect effects regarding fat-free mass index, glucose, insulin, and blood pressure, though the total effects were not significant, due to the balance between direct and indirect effects. The only significant direct effect was regarding diastolic blood pressure [− 0.165 (− 0.302;-0.028)]. Conclusions At age 7, multiples presented better cardiometabolic indicators explained by lower weight at birth, except for the lower blood pressure which was independent of an effect via birthweight.Generation XXI was funded by Programa Operacional de Saúde – Saúde XXI, Quadro Comunitário de Apoio III and Administração Regional de Saúde Norte (Regional Department of Ministry of Health); FEDER through the Operational Programme Competitiveness and Internationalization and national funding from the Foundation for Science and Technology – FCT (Portuguese Ministry of Science, Technology and Higher Education) (POCI-01- 0145-FEDER-016837), under the project “PathMOB: Risco cardiometabólico na infância: desde o início da vida ao fim da infância” (Ref. FCT PTDC/DTP-EPI/3306/2014), the Unidade de Investigação em Epidemiologia - Instituto de Saúde Pública da Universidade do Porto (EPIUnit) (POCI-01-0145-FEDER-006862; Ref. UID/DTP/04750/2013), and ACS holds a FCT Investigator contract IF/01060/2015; and by the project DOCnet (NORTE-01-0145-FEDER-000003), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF) and by European Commission [project reference FP7-ENV-2013-603946]

From laboratory bench to benchmark: technology transfer in laboratory medicine

Background: Life Sciences research, enhancing the occurrence of innovation, is able to impact clinical decision-making, both at diagnosis and therapy. Indeed, starting from the knowledge of specific needs and of technical-scientific demands, researchers can conceive and experiment innovative solutions. Despite these strengths, transferring research to the market in Life Sciences shows considerable criticalities. The aim of this paper is to provide concrete evidences on the processes of technology transfer based on the exploitation of the results obtained by KronosDNAsrl, an academic spin-off focused on reproductive medicine. Methods: Different tools were used to evaluate the technical feasibility (validation of the results obtained with the prototype) and to manage the technology transfer process of One4Two®. Results: The different analyses we carried out showed the feasibility of the proposed solution. As a result, the One4Two® prototype has been developed and validated. Conclusions: Here, we provide a strength of evidences on how knowledge obtained by translational research on "bench" can be used to be transferred to the market on "benchmark" enabling innovation in Laboratory Medicine. In addition, the model described for One4Two® can be easily transferred to other products

Wellness Lessons From Transportation Companies, Research Report WP 11-01

The purpose of this report is to describe wellness programs and offer two suggestions for improving how they are delivered to commercial drivers and operators. It is not a large sample empirical study from which generalizations can be made. Rather, the Mineta Transportation Institute commissioned brief case studies of transportation companies to show what several organizations have done. Stress, nicotine use, sleep apnea, obesity and lack of information are significant barriers to wellness in commercial drivers/operators. Many wellness programs ask the individual driver/operator to lose weight; exercise more; and monitor blood pressure, glucose, cholesterol and other such indicators of health. However, little is done to change the environment or adopt structural interventions such as forbidding nicotine use, as is possible in 20 states. Other structural interventions include those possible at the levels of the company and community, including access to healthy food rather than the junk food drivers often can find on the road. At the societal level, more public transit that gets people walking and out of their cars, cities designed for people to walk and cycle in rather than drive from work to a sprawling suburb, and encouraging food manufacturers to make healthy food (rather than a toxic mix of sodium, fat and sugar to boost one’s craving for a particular food) are just a few measures that could improve the health and well being of the public. The Union Pacific Corporation (rail transportation), and Con-way Freight (trucking) are included because they were willing to share information and are large publicly traded companies. The Utah Transit Authority (UTA) is included because other transit authorities recommended it to the authors, as it has a long history in wellness as part of local government and it too chose to participate. Two issues are discussed: the first is the importance of using the mitigation of erectile dysfunction in the promotion of wellness programs to commercial drivers/operators and the second issue is to urge employers to consider banning tobacco use, both on and off the job, where legal

Health Fetishism Among The Nacirema: A Fugue On Jenny Reardon’s The Postgenomic Condition: Ethics, Justice, and Knowledge After The Genome (Chicago University Press, 2017) And Isabelle Stengers’ Another Science Is Possible: A Manifesto For Slow Science (Polity Press, 2018)

Personalized medicine has become a goal of genomics and of health policy makers. This article reviews two recent books that are highly critical of this approach, finding their arguments very thoughtful and important. According to Stengers, biology’s rush to become a science of genome sequences has made it part of the “speculative economy of promise.” Reardon claims that the postgenomic condition is the attempt to find meaning in all the troves of data that have been generated. The current paper attempts to extend these arguments by showing that scientific alternatives such as ecological developmental biology and the tissue organization field theory of cancer provide evidence demonstrating that genomic data alone is not sufficient to explain the origins of common disease. What does need to be explained is the intransience of medical scientists to recognize other explanatory models beside the “-omics” approaches based on computational algorithms. To this end, various notions of commodity and religious fetishism are used. This is not to say that there is no place for Big Data and genomics. Rather, these methodologies should have a definite place among others. These books suggest that Big Data genomics is like the cancer it is supposed to conquer. It has expanded unregulated and threatens to kill the body in which it arose

Moving Domain Computational Fluid Dynamics to Interface with an Embryonic Model of Cardiac Morphogenesis

Peristaltic contraction of the embryonic heart tube produces time- and spatial-varying wall shear stress (WSS) and pressure gradients (∇P) across the atrioventricular (AV) canal. Zebrafish (Danio rerio) are a genetically tractable system to investigate cardiac morphogenesis. The use of Tg(fli1a:EGFP)y1 transgenic embryos allowed for delineation and two-dimensional reconstruction of the endocardium. This time-varying wall motion was then prescribed in a two-dimensional moving domain computational fluid dynamics (CFD) model, providing new insights into spatial and temporal variations in WSS and ∇P during cardiac development. The CFD simulations were validated with particle image velocimetry (PIV) across the atrioventricular (AV) canal, revealing an increase in both velocities and heart rates, but a decrease in the duration of atrial systole from early to later stages. At 20-30 hours post fertilization (hpf), simulation results revealed bidirectional WSS across the AV canal in the heart tube in response to peristaltic motion of the wall. At 40-50 hpf, the tube structure undergoes cardiac looping, accompanied by a nearly 3-fold increase in WSS magnitude. At 110-120 hpf, distinct AV valve, atrium, ventricle, and bulbus arteriosus form, accompanied by incremental increases in both WSS magnitude and ∇P, but a decrease in bi-directional flow. Laminar flow develops across the AV canal at 20-30 hpf, and persists at 110-120 hpf. Reynolds numbers at the AV canal increase from 0.07±0.03 at 20-30 hpf to 0.23±0.07 at 110-120 hpf (p< 0.05, n=6), whereas Womersley numbers remain relatively unchanged from 0.11 to 0.13. Our moving domain simulations highlights hemodynamic changes in relation to cardiac morphogenesis; thereby, providing a 2-D quantitative approach to complement imaging analysis. © 2013 Lee et al

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 204

This bibliography lists 140 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980

Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model

Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine

An application of genetic algorithms to chemotherapy treatment.

The present work investigates methods for optimising cancer chemotherapy within the bounds of clinical acceptability and making this optimisation easily accessible to oncologists. Clinical oncologists wish to be able to improve existing treatment regimens in a systematic, effective and reliable way. In order to satisfy these requirements a novel approach to chemotherapy optimisation has been developed, which utilises Genetic Algorithms in an intelligent search process for good chemotherapy treatments. The following chapters consequently address various issues related to this approach. Chapter 1 gives some biomedical background to the problem of cancer and its treatment. The complexity of the cancer phenomenon, as well as the multi-variable and multi-constrained nature of chemotherapy treatment, strongly support the use of mathematical modelling for predicting and controlling the development of cancer. Some existing mathematical models, which describe the proliferation process of cancerous cells and the effect of anti-cancer drugs on this process, are presented in Chapter 2. Having mentioned the control of cancer development, the relevance of optimisation and optimal control theory becomes evident for achieving the optimal treatment outcome subject to the constraints of cancer chemotherapy. A survey of traditional optimisation methods applicable to the problem under investigation is given in Chapter 3 with the conclusion that the constraints imposed on cancer chemotherapy and general non-linearity of the optimisation functionals associated with the objectives of cancer treatment often make these methods of optimisation ineffective. Contrariwise, Genetic Algorithms (GAs), featuring the methods of evolutionary search and optimisation, have recently demonstrated in many practical situations an ability to quickly discover useful solutions to highly-constrained, irregular and discontinuous problems that have been difficult to solve by traditional optimisation methods. Chapter 4 presents the essence of Genetic Algorithms, as well as their salient features and properties, and prepares the ground for the utilisation of Genetic Algorithms for optimising cancer chemotherapy treatment. The particulars of chemotherapy optimisation using Genetic Algorithms are given in Chapter 5 and Chapter 6, which present the original work of this thesis. In Chapter 5 the optimisation problem of single-drug chemotherapy is formulated as a search task and solved by several numerical methods. The results obtained from different optimisation methods are used to assess the quality of the GA solution and the effectiveness of Genetic Algorithms as a whole. Also, in Chapter 5 a new approach to tuning GA factors is developed, whereby the optimisation performance of Genetic Algorithms can be significantly improved. This approach is based on statistical inference about the significance of GA factors and on regression analysis of the GA performance. Being less computationally intensive compared to the existing methods of GA factor adjusting, the newly developed approach often gives better tuning results. Chapter 6 deals with the optimisation of multi-drug chemotherapy, which is a more practical and challenging problem. Its practicality can be explained by oncologists' preferences to administer anti-cancer drugs in various combinations in order to better cope with the occurrence of drug resistant cells. However, the imposition of strict toxicity constraints on combining various anticancer drugs together, makes the optimisation problem of multi-drug chemotherapy very difficult to solve, especially when complex treatment objectives are considered. Nevertheless, the experimental results of Chapter 6 demonstrate that this problem is tractable to Genetic Algorithms, which are capable of finding good chemotherapeutic regimens in different treatment situations. On the basis of these results a decision has been made to encapsulate Genetic Algorithms into an independent optimisation module and to embed this module into a more general and user-oriented environment - the Oncology Workbench. The particulars of this encapsulation and embedding are also given in Chapter 6. Finally, Chapter 7 concludes the present work by summarising the contributions made to the knowledge of the subject treated and by outlining the directions for further investigations. The main contributions are: (1) a novel application of the Genetic Algorithm technique in the field of cancer chemotherapy optimisation, (2) the development of a statistical method for tuning the values of GA factors, and (3) the development of a robust and versatile optimisation utility for a clinically usable decision support system. The latter contribution of this thesis creates an opportunity to widen the application domain of Genetic Algorithms within the field of drug treatments and to allow more clinicians to benefit from utilising the GA optimisation