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A variety of mitigation strategies have been employed against the Covid-19 pandemic. Social distancing is still one of the main methods to reduce spread, but it entails a high toll on personal freedom and economic life. Alternative mitigation strategies that do not come with the same problems but are effective at preventing disease spread are therefore needed. Repetitive mass-testing using PCR assays for viral RNA has been suggested, but as a stand-alone strategy this would be prohibitively resource intensive. Here, we suggest a strategy that aims at targeting the limited resources available for viral RNA testing to subgroups that are more likely than the average population to yield a positive test result. Importantly, these pre-selected subgroups include symptom-free people. By testing everyone in these subgroups, in addition to symptomatic cases, large fractions of pre- and asymptomatic people can be identified, which is only possible by testing-based mitigation. We call this strategy smart testing (ST). In principle, pre-selected subgroups can be found in different ways, but for the purpose of this study we analyze a pre-selection procedure based on cheap and fast virus antigen tests. We quantify the potential reduction of the epidemic reproduction number by such a two-stage ST strategy. In addition to a scenario where such a strategy is available to the whole population, we analyze local applications, e.g. in a country, company, or school, where the tested subgroups are also in exchange with the untested population. Our results suggest that a two-stage ST strategy can be effective to curb pandemic spread, at costs that are clearly outweighed by the economic benefit. It is technically and logistically feasible to employ such a strategy, and our model predicts that it is even effective when applied only within local groups. We therefore recommend adding two-stage ST to the portfolio of available mitigation strategies, which allow easing social distancing measures without compromising public health.

We propose a computational simulation framework for describing cancer-therapeutic transport in the lung. A discrete vascular graph model (VGM) is coupled to a double-continuum model (DCM) to determine the amount of administered therapeutic agent that will reach the cancer cells. An alveolar cell carcinoma is considered. The processes in the bigger blood vessels (arteries, arterioles, venules and veins) are described by the VGM. The processes in the alveolar capillaries and the surrounding tissue are represented by a continuum approach for porous media. The system of equations of the coupled discrete/continuum model contains terms that account for degradation processes of the therapeutic agent, the reduction of the number of drug molecules by the lymphatic system and the interaction of the drug with the tissue cells. The functionality of the coupled discrete/continuum model is demonstrated in example simulations using simplified pulmonary vascular networks, which are designed to show-off the capabilities of the model rather than being physiologically accurate.

BACKGROUND: Type III secretion systems (TTSS) are employed by numerous pathogenic and symbiotic bacteria to inject a cocktail of different "effector proteins" into host cells. These effectors subvert host cell signaling to establish symbiosis or disease. METHODOLOGY/PRINCIPAL FINDINGS: We have studied the injection of SipA and SptP, two effector proteins of the invasion-associated Salmonella type III secretion system (TTSS-1). SipA and SptP trigger different host cell responses. SipA contributes to triggering actin rearrangements and invasion while SptP reverses the actin rearrangements after the invasion has been completed. Nevertheless, SipA and SptP were both pre-formed and stored in the bacterial cytosol before host cell encounter. By time lapse microscopy, we observed that SipA was injected earlier than SptP. Computer modeling revealed that two assumptions were sufficient to explain this injection hierarchy: a large number of SipA and SptP molecules compete for transport via a limiting number of TTSS; and the TTSS recognize SipA more efficiently than SptP. CONCLUSIONS/SIGNIFICANCE: This novel mechanism of hierarchical effector protein injection may serve to avoid functional interference between SipA and SptP. An injection hierarchy of this type may be of general importance, allowing bacteria to precisely time the host cell manipulation by type III effectors.