Spheroids-on-a-chip: Recent advances and design considerations in microfluidic platforms for spheroid formation and culture

© 2018 Elsevier B.V. A cell spheroid is a three-dimensional (3D) aggregation of cells. Synthetic, in-vitro spheroids provide similar metabolism, proliferation, and species concentration gradients to those found in-vivo. For instance, cancer cell spheroids have been demonstrated to mimic in-vivo tumor microenvironments, and are thus suitable for in-vitro drug screening. The first part of this paper discusses the latest microfluidic designs for spheroid formation and culture, comparing their strategies and efficacy. The most recent microfluidic techniques for spheroid formation utilize emulsion, microwells, U-shaped microstructures, or digital microfluidics. The engineering aspects underpinning spheroid formation in these microfluidic devices are therefore considered. In the second part of this paper, design considerations for microfluidic spheroid formation chips and microfluidic spheroid culture chips (μSFCs and μSCCs) are evaluated with regard to key parameters affecting spheroid formation, including shear stress, spheroid diameter, culture medium delivery and flow rate. This review is intended to benefit the microfluidics community by contributing to improved design and engineering of microfluidic chips capable of forming and/or culturing three-dimensional cell spheroids

Variability in transmissibility of the 2009 H1N1 pandemic in Canadian communities

Abstract Background The prevalence and severity of the 2009 H1N1 pandemic appeared to vary significantly across populations and geographic regions. We sought to investigate the variability in transmissibility of H1N1 pandemic in different health regions (including urban centres and remote, isolated communities) in the province of Manitoba, Canada. Methods The Richards model was used to fit to the daily number of laboratory-confirmed cases and estimate transmissibility (referred to as the basic reproduction number, R0), doubling times, and turning points of outbreaks in both spring and fall waves of the H1N1 pandemic in several health regions. Results We observed considerable variation in R0 estimates ranging from 1.55 to 2.24, with confidence intervals ranging from 1.45 to 2.88, for an average generation time of 2.9 days, and shorter doubling times in some remote and isolated communities compared to urban centres, suggesting a more rapid spread of disease in these communities during the first wave. For the second wave, R e , the effective reproduction number, is estimated to be lower for remote and isolated communities; however, outbreaks appear to have been driven by somewhat higher transmissibility in urban centres. Conclusions There was considerable geographic variation in transmissibility of the 2009 pandemic outbreaks. While highlighting the importance of estimating R0 for informing health responses, the findings indicate that projecting the transmissibility for large-scale epidemics may not faithfully characterize the early spread of disease in remote and isolated communities

Post-exposure prophylaxis during pandemic outbreaks

<p>Abstract</p> <p>Background</p> <p>With the rise of the second pandemic wave of the novel influenza A (H1N1) virus in the current season in the Northern Hemisphere, pandemic plans are being carefully re-evaluated, particularly for the strategic use of antiviral drugs. The recent emergence of oseltamivir-resistant in treated H1N1 patients has raised concerns about the prudent use of neuraminidase inhibitors for both treatment of ill individuals and post-exposure prophylaxis of close contacts.</p> <p>Methods</p> <p>We extended an established population dynamical model of pandemic influenza with treatment to include post-exposure prophylaxis of close contacts. Using parameter estimates published in the literature, we simulated the model to evaluate the combined effect of treatment and prophylaxis in minimizing morbidity and mortality of pandemic infections in the context of transmissible drug resistance.</p> <p>Results</p> <p>We demonstrated that, when transmissible resistant strains are present, post-exposure prophylaxis can promote the spread of resistance, especially when combined with aggressive treatment. For a given treatment level, there is an optimal coverage of prophylaxis that minimizes the total number of infections (final size) and this coverage decreases as a higher proportion of infected individuals are treated. We found that, when treatment is maintained at intermediate levels, limited post-exposure prophylaxis provides an optimal strategy for reducing the final size of the pandemic while minimizing the total number of deaths. We tested our results by performing a sensitivity analysis over a range of key model parameters and observed that the incidence of infection depends strongly on the transmission fitness of resistant strains.</p> <p>Conclusion</p> <p>Our findings suggest that, in the presence of transmissible drug resistance, strategies that prioritize the treatment of only ill individuals, rather than the prophylaxis of those suspected of being exposed, are most effective in reducing the morbidity and mortality of the pandemic. The impact of post-exposure prophylaxis depends critically on the treatment level and the transmissibility of resistant strains and, therefore, enhanced surveillance and clinical monitoring for resistant mutants constitutes a key component of any comprehensive plan for antiviral drug use during an influenza pandemic.</p

Modelers' Perception of Mathematical Modeling in Epidemiology: A Web-Based Survey

International audienceBackground: Mathematical modeling in epidemiology (MME) is being used increasingly. However, there are many uncertainties in terms of definitions, uses and quality features of MME. Methodology/Principal Findings: To delineate the current status of these models, a 10-item questionnaire on MME was devised. Proposed via an anonymous internet-based survey, the questionnaire was completed by 189 scientists who had published in the domain of MME. A small minority (18%) of respondents claimed to have in mind a concise definition of MME. Some techniques were identified by the researchers as characterizing MME (e.g. Markov models), while others–at the same level of sophistication in terms of mathematics–were not (e.g. Cox regression). The researchers' opinions were also contrasted about the potential applications of MME, perceived as higly relevant for providing insight into complex mechanisms and less relevant for identifying causal factors. The quality criteria were those of good science and were not related to the size and the nature of the public health problems addressed. Conclusions/Significance: This study shows that perceptions on the nature, uses and quality criteria of MME are contrasted, even among the very community of published authors in this domain. Nevertheless, MME is an emerging discipline in epidemiology and this study underlines that it is associated with specific areas of application and methods. The development of this discipline is likely to deserve a framework providing recommendations and guidance at various steps of the studies, from design to report

How to Minimize the Attack Rate during Multiple Influenza Outbreaks in a Heterogeneous Population

<div><h3>Background</h3><p>If repeated interventions against multiple outbreaks are not feasible, there is an optimal level of control during the first outbreak. Any control measures above that optimal level will lead to an outcome that may be as sub-optimal as that achieved by an intervention that is too weak. We studied this scenario in more detail.</p> <h3>Method</h3><p>An age-stratified ordinary-differential-equation model was constructed to study infectious disease outbreaks and control in a population made up of two groups, adults and children. The model was parameterized using influenza as an example. This model was used to simulate two consecutive outbreaks of the same infectious disease, with an intervention applied only during the first outbreak, and to study how cumulative attack rates were influenced by population composition, strength of inter-group transmission, and different ways of triggering and implementing the interventions. We assumed that recovered individuals are fully immune and the intervention does not confer immunity.</p> <h3>Results/Conclusion</h3><p>The optimal intervention depended on coupling between the two population sub-groups, the length, strength and timing of the intervention, and the population composition. Population heterogeneity affected intervention strategies only for very low cross-transmission between groups. At more realistic values, coupling between the groups led to synchronization of outbreaks and therefore intervention strategies that were optimal in reducing the attack rates for each subgroup and the population overall coincided. For a sustained intervention of low efficacy, early intervention was found to be best, while at high efficacies, a delayed start was better. For short interventions, a delayed start was always advantageous, independent of the intervention efficacy. For most scenarios, starting the intervention after a certain cumulative proportion of children were infected seemed more robust in achieving close to optimal outcomes compared to a strategy that used a specified duration after an outbreak’s beginning as the trigger.</p> </div

Influenza Pandemic Waves under Various Mitigation Strategies with 2009 H1N1 as a Case Study

A significant feature of influenza pandemics is multiple waves of morbidity and mortality over a few months or years. The size of these successive waves depends on intervention strategies including antivirals and vaccination, as well as the effects of immunity gained from previous infection. However, the global vaccine manufacturing capacity is limited. Also, antiviral stockpiles are costly and thus, are limited to very few countries. The combined effect of antivirals and vaccination in successive waves of a pandemic has not been quantified. The effect of acquired immunity from vaccination and previous infection has also not been characterized. In times of a pandemic threat countries must consider the effects of a limited vaccine, limited antiviral use and the effects of prior immunity so as to adopt a pandemic strategy that will best aid the population. We developed a mathematical model describing the first and second waves of an influenza pandemic including drug therapy, vaccination and acquired immunity. The first wave model includes the use of antiviral drugs under different treatment profiles. In the second wave model the effects of antivirals, vaccination and immunity gained from the first wave are considered. The models are used to characterize the severity of infection in a population under different drug therapy and vaccination strategies, as well as school closure, so that public health policies regarding future influenza pandemics are better informed

Pertussis resurgence in Toronto, Canada: a population-based study including test-incidence feedback modeling

<p>Abstract</p> <p>Background</p> <p>Pertussis continues to challenge medical professionals; recently described increases in incidence may be due to age-cohort effects, vaccine effectiveness, or changes in testing patterns. Toronto, Canada has recently experienced increases in pertussis incidence, and provides an ideal jurisdiction for evaluating pertussis epidemiology due to centralized testing. We evaluated pertussis trends in Toronto using all available specimen data, which allowed us to control for changing testing patterns and practices.</p> <p>Methods</p> <p>Data included all pertussis culture and PCR test records for Greater Toronto from 1993 to 2007. We estimated incidence trends using Poisson regression models; complex relationships between disease incidence and test submission were explored with vector autoregressive models.</p> <p>Results</p> <p>From 1993 to 2007, 26988 specimens were submitted for testing; 2545 (9.4%) were positive. Pertussis incidence was 2 per 100,000 from 1993 to 2004 and increased to 10 per 100,000 from 2005-2007, with a concomitant 6-fold surge in test specimen submissions after the introduction of a new, more sensitive PCR assay. The relative change in incidence was less marked after adjustment for testing volumes. Bidirectional feedbacks between test positivity and test submissions were identified.</p> <p>Conclusions</p> <p>Toronto's recent surge in pertussis reflects a true increase in local disease activity; the apparent size of the outbreak has likely been magnified by increasing use of pertussis testing by clinicians, and by improved test sensitivity since 2005. These findings may be applicable to changes in pertussis epidemiology that have been noted elsewhere in North America.</p

Nutrition and lung cancer: a case control study in Iran

Background: Despite many prospective and retrospective studies about the association of dietary habit and lung cancer, the topic still remains controversial. So, this study aims to investigate the association of lung cancer with dietary factors. Method: In this study 242 lung cancer patients and their 484 matched controls on age, sex, and place of residence were enrolled between October 2002 to 2005. Trained physicians interviewed all participants with standardized questionnaires. The middle and upper third consumer groups were compared to the lower third according to the distribution in controls unless the linear trend was significant across exposure groups. Result: Conditional logistic regression was used to evaluate the association with lung cancer. In a multivariate analysis fruit (Ptrend < 0.0001), vegetable (P = 0.001) and sunflower oil (P = 0.006) remained as protective factors and rice (P = 0.008), bread (Ptrend = 0.04), liver (P = 0.004), butter (Ptrend = 0.04), white cheese (Ptrend < 0.0001), beef (Ptrend = 0.005), vegetable ghee (P < 0.0001) and, animal ghee (P = 0.015) remained as risk factors of lung cancer. Generally, we found positive trend between consumption of beef (P = 0.002), bread (P < 0.0001), and dairy products (P < 0.0001) with lung cancer. In contrast, only fruits were inversely related to lung cancer (P < 0.0001). Conclusion: It seems that vegetables, fruits, and sunflower oil could be protective factors and bread, rice, beef, liver, dairy products, vegetable ghee, and animal ghee found to be possible risk factors for the development of lung cancer in Iran