Towards a global partnership model in interprofessional education for cross-sector problem-solving.

OBJECTIVES: A partnership model in interprofessional education (IPE) is important in promoting a sense of global citizenship while preparing students for cross-sector problem-solving. However, the literature remains scant in providing useful guidance for the development of an IPE programme co-implemented by external partners. In this pioneering study, we describe the processes of forging global partnerships in co-implementing IPE and evaluate the programme in light of the preliminary data available. METHODS: This study is generally quantitative. We collected data from a total of 747 health and social care students from four higher education institutions. We utilized a descriptive narrative format and a quantitative design to present our experiences of running IPE with external partners and performed independent t-tests and analysis of variance to examine pretest and posttest mean differences in students' data. RESULTS: We identified factors in establishing a cross-institutional IPE programme. These factors include complementarity of expertise, mutual benefits, internet connectivity, interactivity of design, and time difference. We found significant pretest-posttest differences in students' readiness for interprofessional learning (teamwork and collaboration, positive professional identity, roles, and responsibilities). We also found a significant decrease in students' social interaction anxiety after the IPE simulation. CONCLUSIONS: The narrative of our experiences described in this manuscript could be considered by higher education institutions seeking to forge meaningful external partnerships in their effort to establish interprofessional global health education.

Leapfrog dynamics in phage-bacteria coevolution revealed by joint analysis of cross-infection phenotypes and whole genome sequencing.

Viruses and their hosts can undergo coevolutionary arms races where hosts evolve increased resistance and viruses evolve counter-resistance. Given these arms race dynamics (ARD), both players are predicted to evolve along a single trajectory as more recently evolved genotypes replace their predecessors. By coupling phenotypic and genomic analyses of coevolving populations of bacteriophage λ and Escherichia coli, we find conflicting evidence for ARD. Virus-host infection phenotypes fit the ARD model, yet genomic analyses revealed fluctuating selection dynamics. Rather than coevolution unfolding along a single trajectory, cryptic genetic variation emerges and is maintained at low frequency for generations until it eventually supplants dominant lineages. These observations suggest a hybrid 'leapfrog' dynamic, revealing weaknesses in the predictive power of standard coevolutionary models. The findings shed light on the mechanisms that structure coevolving ecological networks and reveal the limits of using phenotypic or genomic data alone to differentiate coevolutionary dynamics.

Optimizing the Timing and Composition of Therapeutic Phage Cocktails: A Control-Theoretic Approach.

Viruses that infect bacteria, i.e., bacteriophage or 'phage,' are increasingly considered as treatment options for the control and clearance of bacterial infections, particularly as compassionate use therapy for multi-drug-resistant infections. In practice, clinical use of phage often involves the application of multiple therapeutic phage, either together or sequentially. However, the selection and timing of therapeutic phage delivery remains largely ad hoc. In this study, we evaluate principles underlying why careful application of multiple phage (i.e., a 'cocktail') might lead to therapeutic success in contrast to the failure of single-strain phage therapy to control an infection. First, we use a nonlinear dynamics model of within-host interactions to show that a combination of fast intra-host phage decay, evolution of phage resistance amongst bacteria, and/or compromised immune response might limit the effectiveness of single-strain phage therapy. To resolve these problems, we combine dynamical modeling of phage, bacteria, and host immune cell populations with control-theoretic principles (via optimal control theory) to devise evolutionarily robust phage cocktails and delivery schedules to control the bacterial populations. Our numerical results suggest that optimal administration of single-strain phage therapy may be sufficient for curative outcomes in immunocompetent patients, but may fail in immunodeficient hosts due to phage resistance. We show that optimized treatment with a two-phage cocktail that includes a counter-resistant phage can restore therapeutic efficacy in immunodeficient hosts.

Modeling the synergistic elimination of bacteria by phage and the innate immune system.

Phage therapy has been viewed as a potential treatment for bacterial infections for over a century. Yet, the year 2016 marks one of the first phase I/II human trials of a phage therapeutic - to treat burn wound patients in Europe. The slow progress in realizing clinical therapeutics is matched by a similar dearth in principled understanding of phage therapy. Theoretical models and in vitro experiments find that combining phage and bacteria often leads to coexistence of both phage and bacteria or phage elimination altogether. Both outcomes stand in contrast to the stated goals of phage therapy. A potential resolution to the gap between models, experiments, and therapeutic use of phage is the hypothesis that the combined effect of phage and host immune system can synergistically eliminate bacterial pathogens. Here, we propose a phage therapy model that considers the nonlinear dynamics arising from interactions between bacteria, phage and the host innate immune system. The model builds upon earlier efforts by incorporating a maximum capacity of the immune response and density-dependent immune evasion by bacteria. We analytically identify a synergistic regime in this model in which phage and the innate immune response jointly contribute to the elimination of the target bacteria. Crucially, we find that in this synergistic regime, neither phage alone nor the innate immune system alone can eliminate the bacteria. We confirm these findings using numerical simulations in biologically plausible scenarios. We utilize our numerical simulations to explore the synergistic effect and its significance for guiding the use of phage therapy in clinically relevant applications.

Inferring strain-level mutational drivers of phage-bacteria interaction phenotypes.

The enormous diversity of bacteriophages and their bacterial hosts presents a significant challenge to predict which phages infect a focal set of bacteria. Infection is largely determined by complementary -and largely uncharacterized- genetics of adsorption, injection, and cell take-over. Here we present a machine learning (ML) approach to predict phage-bacteria interactions trained on genome sequences of and phenotypic interactions amongst 51 Escherichia coli strains and 45 phage λ strains that coevolved in laboratory conditions for 37 days. Leveraging multiple inference strategies and without a priori knowledge of driver mutations, this framework predicts both who infects whom and the quantitative levels of infections across a suite of 2,295 potential interactions. The most effective ML approach inferred interaction phenotypes from independent contributions from phage and bacteria mutations, predicting phage host range with 86% mean classification accuracy while reducing the relative error in the estimated strength of the infection phenotype by 40%. Further, transparent feature selection in the predictive model revealed 18 of 176 phage λ and 6 of 18 E. coli mutations that have a significant influence on the outcome of phage-bacteria interactions, corroborating sites previously known to affect phage λ infections, as well as identifying mutations in genes of unknown function not previously shown to influence bacterial resistance. While the genetic variation studied was limited to a focal, coevolved phage-bacteria system, the method's success at recapitulating strain-level infection outcomes provides a path forward towards developing strategies for inferring interactions in non-model systems, including those of therapeutic significance.

Cardiac power output ratio: Novel survival predictor after percutaneous ventricular assist device in cardiogenic shock.

AIMS: Currently, there is limited data on prognostic indicators after insertion of percutaneous ventricular assist device (PVAD) in the treatment of cardiogenic shock (CS). This study evaluated the prognostic role of cardiac power output (CPO) ratio, defined as CPO at 24 h divided by early CPO (30 min to 2 h), in CS patients after PVAD. METHODS AND RESULTS: Consecutive CS patients from the QEH-PVAD Registry were followed up for survival at 90 days after PVAD. Among 121 consecutive patients, 98 underwent right heart catheterization after PVAD, with CPO ratio available in 68 patients. The CPO ratio and 24-h CPO, but not the early CPO post PVAD, were significantly associated with 90-day survival, with corresponding area under curve in ROC analysis of 0.816, 0.740, and 0.469, respectively. In multivariate analysis, only the CPO ratio and lactate level at 24 h remained as independent survival predictors. The CPO ratio was not associated with age, sex, and body size. Patients with lower CPO ratio had significantly lower coronary perfusion pressure, worse right heart indices, and higher pulmonary vascular resistance. A lower CPO ratio was also significantly associated with mechanical ventilation and higher creatine kinase levels in myocardial infarction patients. CONCLUSION: In post-PVAD patients, the CPO ratio outperformed the absolute CPO values and other haemodynamic metrics in predicting survival at 90 days. Such a proportional change of CPO over time, likely reflecting native heart function recovery, may help to guide management of CS patients post-PVAD.

Modeling shield immunity to reduce COVID-19 transmission in long-term care facilities.

PURPOSE: Nursing homes and long-term care facilities have experienced severe outbreaks and elevated mortality rates of COVID-19. When available, vaccination at-scale has helped drive a rapid reduction in severe cases. However, vaccination coverage remains incomplete among residents and staff, such that additional mitigation and prevention strategies are needed to reduce the ongoing risk of transmission. One such strategy is that of "shield immunity", in which immune individuals modulate their contact rates and shield uninfected individuals from potentially risky interactions. METHODS: Here, we adapt shield immunity principles to a network context, by using computational models to evaluate how restructured interactions between staff and residents affect SARS-CoV-2 epidemic dynamics. RESULTS: First, we identify a mitigation rewiring strategy that reassigns immune healthcare workers to infected residents, significantly reducing outbreak sizes given weekly testing and rewiring (48% reduction in the outbreak size). Second, we identify a preventative prewiring strategy in which susceptible healthcare workers are assigned to immunized residents. This preventative strategy reduces the risk and size of an outbreak via the inadvertent introduction of an infectious healthcare worker in a partially immunized population (44% reduction in the epidemic size). These mitigation levels derived from network-based interventions are similar to those derived from isolating infectious healthcare workers. CONCLUSIONS: This modeling-based assessment of shield immunity provides further support for leveraging infection and immune status in network-based interventions to control and prevent the spread of COVID-19.

Autonomous motivation predicts students' engagement and disaffection in interprofessional education: Scale adaptation and application.

OBJECTIVES: Despite the popularity of interprofessional education, the empirical and theoretical development of its scholarship and science is just emerging. This may be caused in part by the non-availability of measures that can be used by researchers in this field. This study aimed to contribute to the psychological theorizing of interprofessional education by uncovering the psychometric properties of Perceived Locus of Causality adapted to Interprofessional Education (PLOC-IPE) in healthcare education and provide a comprehensive guide on how this can be used to advance the IPE research agenda. METHODS: Confirmatory factor analysis (quantitative design) was used to examine the acceptability of psychometric properties of PLOC-IPE. Data were collected through questionnaires administered at two different time points. The participants consisted of 345 students from Chinese Medicine, Clinical Psychology, Medicine, Nursing, Pharmacy, and Social Work from a university in Hong Kong. RESULTS: Based on confirmatory factor analysis, results of within-network construct validity showed good psychometric properties of PLOC-IPE while between-network validity indicated that the scale can predict IPE-related outcomes. Students' intrinsic motivation in IPE positively predicted emotional engagement and negatively predicted emotional disaffection, demonstrating the applicability of the newly validated PLOC-IPE. Amotivation was a negative predictor of emotional engagement and a positive predictor of emotional disaffection. CONCLUSIONS: Findings support the acceptability of PLOC when adapted to IPE. PLOC-IPE obtained acceptable psychometric properties as a measure of students' academic motivation in IPE. It is an adapted scale that can be used to understand self-determined motivation in the context of IPE in health and social care education. A guide on how PLOC-IPE can be a means by which researchers can contribute to the advancement of scholarship of IPE was provided.

Quantitative Models of Phage-Antibiotic Combination Therapy.

The spread of multidrug-resistant (MDR) bacteria is a global public health crisis. Bacteriophage therapy (or "phage therapy") constitutes a potential alternative approach to treat MDR infections. However, the effective use of phage therapy may be limited when phage-resistant bacterial mutants evolve and proliferate during treatment. Here, we develop a nonlinear population dynamics model of combination therapy that accounts for the system-level interactions between bacteria, phage, and antibiotics for in vivo application given an immune response against bacteria. We simulate the combination therapy model for two strains of Pseudomonas aeruginosa, one which is phage sensitive (and antibiotic resistant) and one which is antibiotic sensitive (and phage resistant). We find that combination therapy outperforms either phage or antibiotic alone and that therapeutic effectiveness is enhanced given interaction with innate immune responses. Notably, therapeutic success can be achieved even at subinhibitory concentrations of antibiotics, e.g., ciprofloxacin. These in silico findings provide further support to the nascent application of combination therapy to treat MDR bacterial infections, while highlighting the role of innate immunity in shaping therapeutic outcomes.IMPORTANCE This work develops and analyzes a novel model of phage-antibiotic combination therapy, specifically adapted to an in vivo context. The objective is to explore the underlying basis for clinical application of combination therapy utilizing bacteriophage that target antibiotic efflux pumps in Pseudomonas aeruginosa In doing so, the paper addresses three key questions. How robust is combination therapy to variation in the resistance profiles of pathogens? What is the role of immune responses in shaping therapeutic outcomes? What levels of phage and antibiotics are necessary for curative success? As we show, combination therapy outperforms either phage or antibiotic alone, and therapeutic effectiveness is enhanced given interaction with innate immune responses. Notably, therapeutic success can be achieved even at subinhibitory concentrations of antibiotic. These in silico findings provide further support to the nascent application of combination therapy to treat MDR bacterial infections, while highlighting the role of system-level feedbacks in shaping therapeutic outcomes.

Intervention Serology and Interaction Substitution: Modeling the Role of 'Shield Immunity' in Reducing COVID-19 Epidemic Spread.

The COVID-19 pandemic has precipitated a global crisis, with more than 690,000 confirmed cases and more than 33,000 confirmed deaths globally as of March 30, 2020 [1-4]. At present two central public health control strategies have emerged: mitigation and suppression (e.g, [5]). Both strategies focus on reducing new infections by reducing interactions (and both raise questions of sustainability and long-term tactics). Complementary to those approaches, here we develop and analyze an epidemiological intervention model that leverages serological tests [6, 7] to identify and deploy recovered individuals as focal points for sustaining safer interactions via interaction substitution, i.e., to develop what we term 'shield immunity' at the population scale. Recovered individuals, in the present context, represent those who have developed protective, antibodies to SARS-CoV-2 and are no longer shedding virus [8]. The objective of a shield immunity strategy is to help sustain the interactions necessary for the functioning of essential goods and services (including but not limited to tending to the elderly [9], hospital care, schools, and food supply) while decreasing the probability of transmission during such essential interactions. We show that a shield immunity approach may significantly reduce the length and reduce the overall burden of an outbreak, and can work synergistically with social distancing. The present model highlights the value of serological testing as part of intervention strategies, in addition to its well recognized roles in estimating prevalence [10, 11] and in the potential development of plasma-based therapies [12-15].

Modeling shield immunity to reduce COVID-19 epidemic spread.

The COVID-19 pandemic has precipitated a global crisis, with more than 1,430,000 confirmed cases and more than 85,000 confirmed deaths globally as of 9 April 20201-4. Mitigation and suppression of new infections have emerged as the two predominant public health control strategies5. Both strategies focus on reducing new infections by limiting human-to-human interactions, which could be both socially and economically unsustainable in the long term. We have developed and analyzed an epidemiological intervention model that leverages serological tests6,7 to identify and deploy recovered individuals8 as focal points for sustaining safer interactions via interaction substitution, developing what we term 'shield immunity' at the population scale. The objective of a shield immunity strategy is to help to sustain the interactions necessary for the functioning of essential goods and services9 while reducing the probability of transmission. Our shield immunity approach could substantively reduce the length and reduce the overall burden of the current outbreak, and can work synergistically with social distancing. The present model highlights the value of serological testing as part of intervention strategies, in addition to its well-recognized roles in estimating prevalence10,11 and in the potential development of plasma-based therapies12-15.

Not by (Good) Microbes Alone: Towards Immunocommensal Therapies.

Commensal bacteria have been identified as critical drivers of host resilience to pathogen invasion. The resulting 'competitive exclusion' of pathogens by commensals can arise via multiple mechanisms, including direct competition for sites of colonization, production of metabolic products that inhibit pathogen growth, and modulation of host immune responses (including differential targeting of pathogens). Nonetheless, suppression of pathogens through the combined action of commensals and host immunity is far from inevitable. Here, we utilize a simple, within-host ecosystem model to explore the microbiological and immunological conditions that govern the fate of pathogen colonization. Model analysis leads to the hypothesis that robust elimination of pathogens requires a synergy between host immune defense and commensal bacteria. That is, pathogens can proliferate and establish persistent infections if either the state of the microbiota or the host immune defense falls below critical levels. Leveraging these findings, we advocate for improved integration of nonlinear dynamic models in efforts to understand infection dynamics in an immunological context. Doing so may provide new opportunities to establish baseline indicators for healthy microbiomes and to develop improved therapeutics through targeted modification of feedback amongst commensals and between commensals and the immune system.

Synergy between the Host Immune System and Bacteriophage Is Essential for Successful Phage Therapy against an Acute Respiratory Pathogen.

The rise of multi-drug-resistant (MDR) bacteria has spurred renewed interest in the use of bacteriophages in therapy. However, mechanisms contributing to phage-mediated bacterial clearance in an animal host remain unclear. We investigated the effects of host immunity on the efficacy of phage therapy for acute pneumonia caused by MDR Pseudomonas aeruginosa in a mouse model. Comparing efficacies of phage-curative and prophylactic treatments in healthy immunocompetent, MyD88-deficient, lymphocyte-deficient, and neutrophil-depleted murine hosts revealed that neutrophil-phage synergy is essential for the resolution of pneumonia. Population modeling of in vivo results further showed that neutrophils are required to control both phage-sensitive and emergent phage-resistant variants to clear infection. This "immunophage synergy" contrasts with the paradigm that phage therapy success is largely due to bacterial permissiveness to phage killing. Lastly, therapeutic phages were not cleared by pulmonary immune effector cells and were immunologically well tolerated by lung tissues.

Conflicting attachment and the growth of bipartite networks.

Simple growth mechanisms have been proposed to explain the emergence of seemingly universal network structures. The widely studied model of preferential attachment assumes that new nodes are more likely to connect to highly connected nodes. Preferential attachment explains the emergence of scale-free degree distributions within complex networks. Yet it is incompatible with many network systems, particularly bipartite systems in which two distinct types of agents interact. For example, the addition of new links in a host-parasite system corresponds to the infection of hosts by parasites. Increasing connectivity is beneficial to a parasite and detrimental to a host. Therefore, the overall network connectivity is subject to conflicting pressures. Here we propose a stochastic network growth model of conflicting attachment, inspired by a particular kind of parasite-host interaction: that of viruses interacting with microbial hosts. The mechanism of network growth includes conflicting preferences to network density as well as costs involved in modifying the network connectivity according to these preferences. We find that the resulting networks exhibit realistic patterns commonly observed in empirical data, including the emergence of nestedness, modularity, and nested-modular structures that exhibit both properties. We study the role of conflicting interests in shaping network structure and assess opportunities to incorporate greater realism in linking growth process to pattern in systems governed by antagonistic and mutualistic interactions.