31 research outputs found

    Nanoscale Metallic Iron for Environmental Remediation: Prospects and Limitations

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    The amendment of the subsurface with nanoscale metallic iron particles (nano-Fe0) has been discussed in the literature as an efficient in situ technology for groundwater remediation. However, the introduction of this technology was controversial and its efficiency has never been univocally established. This unsatisfying situation has motivated this communication whose objective was a comprehensive discussion of the intrinsic reactivity of nano-Fe0 based on the contemporary knowledge on the mechanism of contaminant removal by Fe0 and a mathematical model. It is showed that due to limitations of the mass transfer of nano-Fe0 to contaminants, available concepts cannot explain the success of nano-Fe0 injection for in situ groundwater remediation. It is recommended to test the possibility of introducing nano-Fe0 to initiate the formation of roll-fronts which propagation would induce the reductive transformation of both dissolved and adsorbed contaminants. Within a roll-front, FeII from nano-Fe0 is the reducing agent for contaminants. FeII is recycled by biotic or abiotic FeIII reduction. While the roll-front concept could explain the success of already implemented reaction zones, more research is needed for a science-based recommendation of nano- Fe0 for subsurface treatment by roll-front

    Steering Evolution with Sequential Therapy to Prevent the Emergence of Bacterial Antibiotic Resistance

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    The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Here, we consider a simple model of evolution in asexually reproducing populations which considers adaptation as a biased random walk on a fitness landscape. This model associates the global properties of the fitness landscape with the algebraic properties of a Markov chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Using this formalism, we analyze 15 empirical fitness landscapes of E. coli under selection by different β-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be either hindered or promoted by different sequences of drug application. Specifically, we demonstrate that the majority, approximately 70%, of sequential drug treatments with 2–4 drugs promote resistance to the final antibiotic. Further, we derive optimal drug application sequences with which we can probabilistically ‘steer’ the population through genotype space to avoid the emergence of resistance. This suggests a new strategy in the war against antibiotic–resistant organisms: drug sequencing to shepherd evolution through genotype space to states from which resistance cannot emerge and by which to maximize the chance of successful therapy
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