Regional patient transfer patterns matter for the spread of hospital-acquired pathogens.

Pathogens typically responsible for hospital-acquired infections (HAIs) constitute a major threat to healthcare systems worldwide. They spread via hospital (or hospital-community) networks by readmissions or patient transfers. Therefore, knowledge of these networks is essential to develop and test strategies to mitigate and control the HAI spread. Until now, no methods for comparing healthcare networks across different systems were proposed. Based on healthcare insurance data from four German federal states (Bavaria, Lower Saxony, Saxony and Thuringia), we constructed hospital networks and compared them in a systematic approach regarding population, hospital characteristics, and patient transfer patterns. Direct patient transfers between hospitals had only a limited impact on HAI spread. Whereas, with low colonization clearance rates, readmissions to the same hospitals posed the biggest transmission risk of all inter-hospital transfers. We then generated hospital-community networks, in which patients either stay in communities or in hospitals. We found that network characteristics affect the final prevalence and the time to reach it. However, depending on the characteristics of the pathogen (colonization clearance rate and transmission rate or even the relationship between transmission rate in hospitals and in the community), the studied networks performed differently. The differences were not large, but justify further studies.

Transmission of drug-resistant bacteria in a hospital-community model stratified by patient risk.

A susceptible-infectious-susceptible (SIS) model for simulating healthcare-acquired infection spread within a hospital and associated community is proposed. The model accounts for the stratification of in-patients into two susceptibility-based risk groups. The model is formulated as a system of first-order ordinary differential equations (ODEs) with appropriate initial conditions. The mathematical analysis of this system is demonstrated. It is shown that the system has unique global solutions, which are bounded and non-negative. The basic reproduction number ([Formula: see text]) for the considered model is derived. The existence and the stability of the stationary solutions are analysed. The disease-free stationary solution is always present and is globally asymptotically stable for [Formula: see text], while for [Formula: see text] it is unstable. The presence of an endemic stationary solution depends on the model parameters and when it exists, it is globally asymptotically stable. The endemic state encompasses both risk groups. The endemic state within only one group only is not possible. In addition, for [Formula: see text] a forward bifurcation takes place. Numerical simulations, based on the anonymised insurance data, are also presented to illustrate theoretical results.

Diversity and inclusion for rodents: how animal ethics committees can help improve translation.

Translation failure occurs when a treatment shown to be safe and effective in one type of population does not produce the same result in another. We are currently in a crisis involving the translatability of preclinical studies to human populations. Animal trials are no better than a coin toss at predicting the safety and efficacy of drugs in human trials, and the high failure rate of drugs entering human trials suggests that most of the suffering of laboratory animals is futile, creating no commensurate benefit for human patients. Here, I argue that animal ethics committees have a role to play in getting us out of this crisis. Inadequate representation is a known contributor to translation failures and is a matter of both scientific and ethical concern. Ethical review committees have the authority to address it by reprioritising the values already enshrined in their guiding principles.

The effect of re-directed patient flow in combination with targeted infection control measures on the spread of multi-drug-resistant Enterobacteriaceae in the German health-care system: a mathematical modelling approach.

OBJECTIVE: The introduction of multi-drug-resistant Enterobacteriaceae (MDR-E) by colonized patients transferred from high-prevalence countries has led to several large outbreaks of MDR-E in low-prevalence countries, with the risk of propagated spread to the community. The goal of this study was to derive a strategy to counteract the spread of MDR-E at the regional health-care network level. METHODS: We used a hybrid ordinary differential equation and network model built based on German health insurance data to evaluate whether the re-direction of patient flow in combination with targeted infection control measures can counteract the spread of MDR-E in the German health-care system. We applied pragmatic re-direction strategies focusing on a reduced choice of hospitals for subsequent stays after initial hospitalization but not manipulating direct transfers because these are most likely determined by medical needs. RESULTS: The re-direction strategies alone did not reduce the system-wide spread of MDR-E (system-wide prevalence of MDR-E is 18.7% vs. 25.7%/29.9%). In contrast, targeted hospital-based infection control measures restricted to institutions with the highest institutional basic reproduction numbers in the network were identified as an effective tool for reducing system-wide prevalence (system-wide prevalence of MDR-E is 18.7% vs. 9.3%). If these measures were applied to the top one-third of hospitals, the system-wide prevalence could be reduced by approximately 80% (system-wide prevalence of 18.7% vs. 3.5% for one-third of patients subjected to interventions). A combination of this hospital-based intervention and patient re-direction strategies could not improve the effectiveness of the hospital-based approach (system-wide prevalence of MDR-E is 9.3% vs. 14.2%/14.3%). CONCLUSIONS: The pragmatic patient re-direction strategies were not capable of restricting the spread of MDR-E in a simulation of the German health-care system; in contrast, hospital-based interventions focusing on institutions identified based on network transmission patterns seem to be a promising approach for sustainable reduction of the spread of MDR-E through the German population.

Clarifying the Ethics and Oversight of Chimeric Research.

This article is the lead piece in a special report that presents the results of a bioethical investigation into chimeric research, which involves the insertion of human cells into nonhuman animals and nonhuman animal embryos, including into their brains. Rapid scientific developments in this field may advance knowledge and could lead to new therapies for humans. They also reveal the conceptual, ethical, and procedural limitations of existing ethics guidance for human-nonhuman chimeric research. Led by bioethics researchers working closely with an interdisciplinary work group, the investigation focused on generating conceptual clarity and identifying improvements to governance approaches, with the goal of helping scholars, funders, scientists, institutional leaders, and oversight bodies (embryonic stem cell research oversight [ESCRO] committees and institutional animal care and use committees [IACUCs]) deliver principled and trustworthy oversight of this area of science. The article, which focuses on human-nonhuman animal chimeric research that is stem cell based, identifies key ethical issues in and offers ten recommendations regarding the ethics and oversight of this research. Turning from bioethics' previous focus on human-centered questions about the ethics of "humanization" and this research's potential impact on concepts like human dignity, this article emphasizes the importance of nonhuman animal welfare concerns in chimeric research and argues for less-siloed governance and oversight and more-comprehensive public communication.

Application of Inspiratory Muscle Training to Improve Physical Tolerance in Older Patients with Ischemic Heart Failure.

Regardless of the management regime for heart failure (HF), there is strong evidence supporting the early implementation of exercise-based cardiac rehabilitation (CR). Respiratory therapy is considered to be an integral part of such secondary prevention protocols. The aim of the study was to evaluate the effect of inspiratory muscle training (IMT) on exercise tolerance and the functional parameters of the respiratory system in patients with heart failure involved in cardiac rehabilitation. The study included 90 patients with HF who took part in the second-stage 8-week cycle of cardiac rehabilitation (CR). They were randomly divided into three groups: Group I underwent CR and IMT; Group II only CR; and patients in Group III underwent only the IMT. Before and after the 8-week cycle, participants were assessed for exercise tolerance and the functional parameters of respiratory muscle strength. Significant statistical improvement concerned the majority of the hemodynamic parameters, lung function parameters, and respiratory muscle strength in the first group. Moreover, the enhancement in the exercise tolerance in the CR + IMT group was accompanied by a negligible change in the HRpeak. The results confirm that the addition of IMT to the standard rehabilitation process of patients with heart failure can increase the therapeutic effect while influencing some of the parameters measured by exercise electrocardiography and respiratory function.

Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections.

In the year 2020, there were 105 different statutory insurance companies in Germany with heterogeneous regional coverage. Obtaining data from all insurance companies is challenging, so that it is likely that projects will have to rely on data not covering the whole population. Consequently, the study of epidemic spread in hospital referral networks using data-driven models may be biased. We studied this bias using data from three German regional insurance companies covering four federal states: AOK (historically "general local health insurance company", but currently only the abbreviation is used) Lower Saxony (in Federal State of Lower Saxony), AOK Bavaria (in Bavaria), and AOK PLUS (in Thuringia and Saxony). To understand how incomplete data influence network characteristics and related epidemic simulations, we created sampled datasets by randomly dropping a proportion of patients from the full datasets and replacing them with random copies of the remaining patients to obtain scale-up datasets to the original size. For the sampled and scale-up datasets, we calculated several commonly used network measures, and compared them to those derived from the original data. We found that the network measures (degree, strength and closeness) were rather sensitive to incompleteness. Infection prevalence as an outcome from the applied susceptible-infectious-susceptible (SIS) model was fairly robust against incompleteness. At incompleteness levels as high as 90% of the original datasets the prevalence estimation bias was below 5% in scale-up datasets. Consequently, a coverage as low as 10% of the local population of the federal state population was sufficient to maintain the relative bias in prevalence below 10% for a wide range of transmission parameters as encountered in clinical settings. Our findings are reassuring that despite incomplete coverage of the population, German health insurance data can be used to study effects of patient traffic between institutions on the spread of pathogens within healthcare networks.

Relevance of intra-hospital patient movements for the spread of healthcare-associated infections within hospitals - a mathematical modeling study.

The aim of this study is to analyze patient movement patterns between hospital departments to derive the underlying intra-hospital movement network, and to assess if movement patterns differ between patients at high or low risk of colonization. For that purpose, we analyzed patient electronic medical record data from five hospitals to extract information on risk stratification and patient intra-hospital movements. Movement patterns were visualized as networks, and network centrality measures were calculated. Next, using an agent-based model where agents represent patients and intra-hospital patient movements were explicitly modeled, we simulated the spread of multidrug resistant enterobacteriacae (MDR-E) inside a hospital. Risk stratification of patients according to certain ICD-10 codes revealed that length of stay, patient age, and mean number of movements per admission were higher in the high-risk groups. Movement networks in all hospitals displayed a high variability among departments concerning their network centrality and connectedness with a few highly connected departments and many weakly connected peripheral departments. Simulating the spread of a pathogen in one hospital network showed positive correlation between department prevalence and network centrality measures. This study highlights the importance of intra-hospital patient movements and their possible impact on pathogen spread. Targeting interventions to departments of higher (weighted) degree may help to control the spread of MDR-E. Moreover, when the colonization status of patients coming from different departments is unknown, a ranking system based on department centralities may be used to design more effective interventions that mitigate pathogen spread.

Research guidelines for embryoids.

Human embryo models formed from stem cells-known as embryoids-allow scientists to study the elusive first stages of human development without having to experiment on actual human embryos. But clear ethical guidelines for research involving embryoids are still lacking. Previously, a handful of researchers put forward new recommendations for embryoids, which they hope will be included in the next set of International Society for Stem Cell Research guidelines. Although these recommendations are an improvement over the default approach, they are nonetheless unworkable, because they rely on a poorly conceived notion of an embryoid's 'potential' to trigger stringent research regulations.

Rethinking the oversight conditions of human-animal chimera research.

New discoveries are improving the odds of human cells surviving in host animals, prompting regulatory and funding agencies to issue calls for additional layers of ethical oversight for certain types of human-animal chimeras. Of interest are research proposals involving chimeric animals with humanized brains. But what is motivating the demand for additional oversight? I locate two, not obviously compatible, motivations, each of which provides the justificatory basis for paying special attention to different sets of human-animal chimeras. Surprisingly, the sets of animals that actually get flagged for special scrutiny by research and funding guidelines do not correlate with either of the sets of animals that arise when we think about what is motivating additional oversight. What this shows is that existing research policies and funding guidelines are disconnected from their motivation: the rationale for flagging certain types of human-animal chimeras as requiring special oversight is ignored in execution.

Modelling pathogen spread in a healthcare network: Indirect patient movements.

Inter-hospital patient transfers (direct transfers) between healthcare facilities have been shown to contribute to the spread of pathogens in a healthcare network. However, the impact of indirect transfers (patients re-admitted from the community to the same or different hospital) is not well studied. This work aims to study the contribution of indirect transfers to the spread of pathogens in a healthcare network. To address this aim, a hybrid network-deterministic model to simulate the spread of multiresistant pathogens in a healthcare system was developed for the region of Lower Saxony (Germany). The model accounts for both, direct and indirect transfers of patients. Intra-hospital pathogen transmission is governed by a SIS model expressed by a system of ordinary differential equations. Our results show that the proposed model reproduces the basic properties of healthcare-associated pathogen spread. They also show the importance of indirect transfers: restricting the pathogen spread to direct transfers only leads to 4.2% system wide prevalence. However, adding indirect transfers leads to an increase in the overall prevalence by a factor of 4 (18%). In addition, we demonstrated that the final prevalence in the individual healthcare facilities depends on average length of stay in a way described by a non-linear concave function. Moreover, we demonstrate that the network parameters of the model may be derived from administrative admission/discharge records. In particular, they are sufficient to obtain inter-hospital transfer probabilities, and to express the patients' transfers as a Markov process. Using the proposed model, we show that indirect transfers of patients are equally or even more important as direct transfers for the spread of pathogens in a healthcare network.

Avoiding the potentiality trap: thinking about the moral status of synthetic embryos.

Research ethics committees must sometimes deliberate about objects that do not fit nicely into any existing category. This is currently the case with the "gastruloid," which is a self-assembling blob of cells that resembles a human embryo. The resemblance makes it tempting to group it with other members of that kind, and thus to ask whether gastruloids really are embryos. But fitting an ambiguous object into an existing category with well-worn pathways in research ethics, like the embryo, is only a temporary fix. The bigger problem is that we no longer know what an embryo is. We haven't had a non-absurd definition of 'embryo' for several decades and without a well-defined comparison class, asking whether gastruloids belong to the morally relevant class of things we call embryos is to ask a question without an answer. What's the alternative? A better approach needs to avoid what I'll refer to as "the potentiality trap" and, instead, rely on the emergence of morally salient facts about gastruloids and other synthetic embryos.

Why is an Egg Donor a Genetic Parent, but not a Mitochondrial Donor?

As new ways of reproducing emerge, advancing technologies are forcing us to rethink our old ideas of what it means to be a genetic parent. Why should some processes qualify as "reproduction" and others should not? To illustrate the practical impact of providing a defensible account of reproduction, the author examines the case of mitochondrial donation (mtDNA) and calls for a philosophical argument to bring clarity to exactly what we mean by 'reproduction.'

A mathematical model describes the malignant transformation of low grade gliomas: Prognostic implications.

Gliomas are the most frequent type of primary brain tumours. Low grade gliomas (LGGs, WHO grade II gliomas) may grow very slowly for the long periods of time, however they inevitably cause death due to the phenomenon known as the malignant transformation. This refers to the transition of LGGs to more aggressive forms of high grade gliomas (HGGs, WHO grade III and IV gliomas). In this paper we propose a mathematical model describing the spatio-temporal transition of LGGs into HGGs. Our modelling approach is based on two cellular populations with transitions between them being driven by the tumour microenvironment transformation occurring when the tumour cell density grows beyond a critical level. We show that the proposed model describes real patient data well. We discuss the relationship between patient prognosis and model parameters. We approximate tumour radius and velocity before malignant transformation as well as estimate the onset of this process.

Preface.

This volume was inspired by the topics presented at the international conference "Micro and Macro Systems in Life Sciences" which was held on Jun 8-12, 2015 in Bedlewo, Poland. System biology is an approach which tries to understand how micro systems, at the molecular and cellular levels, affect macro systems such as organs, tissue and populations. Thus it is not surprising that a major theme of this volume evolves around cancer and its treatment. Articles on this topic include models for tumor induced angiogenesis, without and with delays, metastatic niche of the bone marrow, drug resistance and metronomic chemotherapy, and virotherapy of glioma. Methods range from dynamical systems to optimal control. Another well represented topic of this volume is mathematical modeling in epidemiology. Mathematical approaches to modeling and control of more specific diseases like malaria, Ebola or human papillomavirus are discussed as well as a more general approaches to the SEIR, and even more general class of models in epidemiology, by using the tools of optimal control and optimization. The volume also brings up challenges in mathematical modeling of other diseases such as tuberculosis. Partial differential equations combined with numerical approaches are becoming important tools in modeling not only tumor growth and treatment, but also other diseases, such as fibrosis of the liver, and atherosclerosis and its associated blood flow dynamics, and our volume presents a state of the art approach on these topics. Understanding mathematics behind the cell motion, appearance of the special patterns in various cell populations, and age structured mutations are among topics addressed inour volume. A spatio-temporal models of synthetic genetic oscillators brings the analysis to the gene level which is the focus of much of current biological research. Mathematics can help biologists to explain the collective behavior of bacterial, a topic that is also presented here. Finally some more across the discipline topics are being addresses, which can appear as a challenge in studying problems in systems biology on all, macro, meso and micro levels. They include numerical approaches to stochastic wave equation arising in modeling Brownian motion, discrete velocity models, many particle approximations as well as very important aspect on the connection between discrete measurement and the construction of the models for various phenomena, particularly the one involving delays. With the variety of biological topics and their mathematical approaches we very much hope that the reader of the Mathematical Biosciences and Engineering will find this volume interesting and inspirational for their own research.

Deterministic and Stochastic Study for a Microscopic Angiogenesis Model: Applications to the Lewis Lung Carcinoma.

Angiogenesis modelling is an important tool to understand the underlying mechanisms yielding tumour growth. Nevertheless, there is usually a gap between models and experimental data. We propose a model based on the intrinsic microscopic reactions defining the angiogenesis process to link experimental data with previous macroscopic models. The microscopic characterisation can describe the macroscopic behaviour of the tumour, which stability analysis reveals a set of predicted tumour states involving different morphologies. Additionally, the microscopic description also gives a framework to study the intrinsic stochasticity of the reactive system through the resulting Langevin equation. To follow the goal of the paper, we use available experimental information on the Lewis lung carcinoma to infer meaningful parameters for the model that are able to describe the different stages of the tumour growth. Finally we explore the predictive capabilities of the fitted model by showing that fluctuations are determinant for the survival of the tumour during the first week and that available treatments can give raise to new stable tumour dormant states with a reduced vascular network.

A matter of timing: identifying significant multi-dose radiotherapy improvements by numerical simulation and genetic algorithm search.

Multi-dose radiotherapy protocols (fraction dose and timing) currently used in the clinic are the product of human selection based on habit, received wisdom, physician experience and intra-day patient timetabling. However, due to combinatorial considerations, the potential treatment protocol space for a given total dose or treatment length is enormous, even for relatively coarse search; well beyond the capacity of traditional in-vitro methods. In constrast, high fidelity numerical simulation of tumor development is well suited to the challenge. Building on our previous single-dose numerical simulation model of EMT6/Ro spheroids, a multi-dose irradiation response module is added and calibrated to the effective dose arising from 18 independent multi-dose treatment programs available in the experimental literature. With the developed model a constrained, non-linear, search for better performing cadidate protocols is conducted within the vicinity of two benchmarks by genetic algorithm (GA) techniques. After evaluating less than 0.01% of the potential benchmark protocol space, candidate protocols were identified by the GA which conferred an average of 9.4% (max benefit 16.5%) and 7.1% (13.3%) improvement (reduction) on tumour cell count compared to the two benchmarks, respectively. Noticing that a convergent phenomenon of the top performing protocols was their temporal synchronicity, a further series of numerical experiments was conducted with periodic time-gap protocols (10 h to 23 h), leading to the discovery that the performance of the GA search candidates could be replicated by 17-18 h periodic candidates. Further dynamic irradiation-response cell-phase analysis revealed that such periodicity cohered with latent EMT6/Ro cell-phase temporal patterning. Taken together, this study provides powerful evidence towards the hypothesis that even simple inter-fraction timing variations for a given fractional dose program may present a facile, and highly cost-effecitive means of significantly improving clinical efficacy.