Therapy with continuous epidural local anesthetic and methylprednisolone provides better control of chronic cervicobrachial pain compared with Single injection. These results are discussed with respect to the possible mechanism of action of the drugs and may relate to the physiopathologic mechanisms associated with neuronal plasticity that result in chronic pain.

We need to find ways of enhancing the potency of existing antibiotics, and, with this in mind, we begin with an unusual question: how low can antibiotic dosages be and yet bacterial clearance still be observed? Seeking to optimise the simultaneous use of two antibiotics, we use the minimal dose at which clearance is observed in an in vitro experimental model of antibiotic treatment as a criterion to distinguish the best and worst treatments of a bacterium, Escherichia coli. Our aim is to compare a combination treatment consisting of two synergistic antibiotics to so-called sequential treatments in which the choice of antibiotic to administer can change with each round of treatment. Using mathematical predictions validated by the E. coli treatment model, we show that clearance of the bacterium can be achieved using sequential treatments at antibiotic dosages so low that the equivalent two-drug combination treatments are ineffective. Seeking to treat the bacterium in testing circumstances, we purposefully study an E. coli strain that has a multidrug pump encoded in its chromosome that effluxes both antibiotics. Genomic amplifications that increase the number of pumps expressed per cell can cause the failure of high-dose combination treatments, yet, as we show, sequentially treated populations can still collapse. However, dual resistance due to the pump means that the antibiotics must be carefully deployed and not all sublethal sequential treatments succeed. A screen of 136 96-h-long sequential treatments determined five of these that could clear the bacterium at sublethal dosages in all replicate populations, even though none had done so by 24 h. These successes can be attributed to a collateral sensitivity whereby cross-resistance due to the duplicated pump proves insufficient to stop a reduction in E. coli growth rate following drug exchanges, a reduction that proves large enough for appropriately chosen drug switches to clear the bacterium.

Can we exploit our burgeoning understanding of molecular evolution to slow the progress of drug resistance? One role of an infection clinician is exactly that: to foresee trajectories to resistance during antibiotic treatment and to hinder that evolutionary course. But can this be done at a hospital-wide scale? Clinicians and theoreticians tried to when they proposed two conflicting behavioral strategies that are expected to curb resistance evolution in the clinic, these are known as “antibiotic cycling” and “antibiotic mixing.” However, the accumulated data from clinical trials, now approaching 4 million patient days of treatment, is too variable for cycling or mixing to be deemed successful. The former implements the restriction and prioritization of different antibiotics at different times in hospitals in a manner said to “cycle” between them. In antibiotic mixing, appropriate antibiotics are allocated to patients but randomly. Mixing results in no correlation, in time or across patients, in the drugs used for treatment which is why theorists saw this as an optimal behavioral strategy. So while cycling and mixing were proposed as ways of controlling evolution, we show there is good reason why clinical datasets cannot choose between them: by re-examining the theoretical literature we show prior support for the theoretical optimality of mixing was misplaced. Our analysis is consistent with a pattern emerging in data: neither cycling or mixing is a priori better than the other at mitigating selection for antibiotic resistance in the clinic. Key words: antibiotic cycling, antibiotic mixing, optimal control, stochastic models.

Evolutionary trajectories are constrained by tradeoffs when mutations that benefit one life history trait incur fitness costs in other traits. As resistance to tetracycline antibiotics by increased efflux can be associated with a 10%, or more, increase in length of the Escherichia coli chromosome, we sought costs of resistance associated with doxycycline. However, it was difficult to identify any because E.coli's growth rate (r), carrying capacity (K) and drug efflux rate increased during evolutionary experiments where E.coli was exposed to doxycycline. Moreover, these improvements remained following drug withdrawal. We sought mechanisms for this seemingly unconstrained adaptation particularly as these traits ought to tradeoff according to rK selection theory. Using prokaryote and eukaryote microbes, including clinical pathogens, we therefore show r and K can tradeoff, but need not, because of 'rK trade-ups'. r and K only tradeoff in sufficiently carbon-rich environments where growth is inefficient.We then used E. coli ribosomal RNA (rrn) knockouts to determine specific mutations, namely changes in rrn operon copy number, than can simultaneously maximise r and K. The optimal genome has fewer operons, and therefore fewer functional ribosomes, than the ancestral strain. It is, therefore, unsurprising for r-adaptation in the presence of a ribosome-inhibiting antibiotic, doxycycline, to also increase population size. Although E. coli can evolve to grow faster and to larger population sizes in the presence of antibiotics when compared to their absence, we found two costs to this improvement: an elongated lag phase and the loss of stress protection genes. 1 IntroductionTradeoffs lie at the heart of a cross-kingdom research effort that seeks to explain how biodiversity is generated and maintained. [1][2][3][4][5] Two traits engage in an evolutionary tradeoff when beneficial mutations for one trait are deleterious for the other, and vice versa, and many theories agree 2,[6][7][8][9][10][11] that genetic polymorphisms are maintained when tradeoffs have an appropriate geometry. Less clear, however, are the physical, chemical and physiological forces that create tradeoffs in the first place 12 and tradeoffs needed for the theories to work can be difficult to isolate in practise. [13][14][15][16][17][18] It is essential for medicine that we understand tradeoffs. The term 'superbug' refers to a pathogenic microorganism that resists treatment by antibiotics with no apparent cost, or tradeoff, in terms of its pathogenicity. An evolutionary route to superbug status is thought to occur when a pathogen first adopts costly drug resistance mutations, a process that sees resistance traded against proliferation rate in antibiotic-free environments. Thereafter, other mutations compensate for those costs, yielding strains that are both drug resistant and capable of rapid proliferation. 19, 20 Tradeoffs are, however, sometimes observed in pathogens. A genomic study of a clinical pathogen using several antibiotic classes 21 showed res...

Anammox (anaerobic ammonium oxidation) coupled to nitrite reduction is an important step in the nitrogen cycle and has been recognized as an important sink for fixed nitrogen in the ocean. Still little is known about the genomic blueprint of different anammox species. In the present article, we discuss the important genes of anammox metabolism in Candidatus 'Brocadia fulgida' that were retrieved via a metagenomic approach.

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