A systematic review of the evidence for single stage and two stage revision of infected knee replacement

BACKGROUND: Periprosthetic infection about the knee is a devastating complication that may affect between 1% and 5% of knee replacement. With over 79 000 knee replacements being implanted each year in the UK, periprosthetic infection (PJI) is set to become an important burden of disease and cost to the healthcare economy. One of the important controversies in treatment of PJI is whether a single stage revision operation is superior to a two-stage procedure. This study sought to systematically evaluate the published evidence to determine which technique had lowest reinfection rates. METHODS: A systematic review of the literature was undertaken using the MEDLINE and EMBASE databases with the aim to identify existing studies that present the outcomes of each surgical technique. Reinfection rate was the primary outcome measure. Studies of specific subsets of patients such as resistant organisms were excluded. RESULTS: 63 studies were identified that met the inclusion criteria. The majority of which (58) were reports of two-stage revision. Reinfection rated varied between 0% and 41% in two-stage studies, and 0% and 11% in single stage studies. No clinical trials were identified and the majority of studies were observational studies. CONCLUSIONS: Evidence for both one-stage and two-stage revision is largely of low quality. The evidence basis for two-stage revision is significantly larger, and further work into direct comparison between the two techniques should be undertaken as a priority

Molecular Longitudinal Tracking of Mycobacterium abscessus spp. during Chronic Infection of the Human Lung

<div><p>The <i>Mycobacterium abscessus</i> complex is an emerging cause of chronic pulmonary infection in patients with underlying lung disease. The <i>M. abscessus</i> complex is regarded as an environmental pathogen but its molecular adaptation to the human lung during long-term infection is poorly understood. Here we carried out a longitudinal molecular epidemiological analysis of 178 <i>M. abscessus</i> spp. isolates obtained from 10 cystic fibrosis (CF) and 2 non CF patients over a 13 year period. Multi-locus sequence and molecular typing analysis revealed that 11 of 12 patients were persistently colonized with the same genotype during the course of the infection while replacement of a <i>M. abscessus sensu stricto</i> strain with a <i>Mycobacterium massiliense</i> strain was observed for a single patient. Of note, several patients including a pair of siblings were colonized with closely-related strains consistent with intra-familial transmission or a common infection reservoir. In general, a switch from smooth to rough colony morphology was observed during the course of long-term infection, which in some cases correlated with an increasing severity of clinical symptoms. To examine evolution during long-term infection of the CF lung we compared the genome sequences of 6 sequential isolates of <i>Mycobacterium bolletii</i> obtained from a single patient over an 11 year period, revealing a heterogeneous clonal infecting population with mutations in regulators controlling the expression of virulence factors and complex lipids. Taken together, these data provide new insights into the epidemiology of <i>M. abscessus</i> spp. during long-term infection of the CF lung, and the molecular transition from saprophytic organism to human pathogen.</p></div

Markovian Dynamics on Complex Reaction Networks

Complex networks, comprised of individual elements that interact with each other through reaction channels, are ubiquitous across many scientific and engineering disciplines. Examples include biochemical, pharmacokinetic, epidemiological, ecological, social, neural, and multi-agent networks. A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underling population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions, the computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples.Comment: 52 pages, 11 figures, for freely available MATLAB software, see http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.htm

Tikhonov adaptively regularized gamma variate fitting to assess plasma clearance of inert renal markers

The Tk-GV model fits Gamma Variates (GV) to data by Tikhonov regularization (Tk) with shrinkage constant, λ, chosen to minimize the relative error in plasma clearance, CL (ml/min). Using 169Yb-DTPA and 99mTc-DTPA (n = 46, 8–9 samples, 5–240 min) bolus-dilution curves, results were obtained for fit methods: (1) Ordinary Least Squares (OLS) one and two exponential term (E1 and E2), (2) OLS-GV and (3) Tk-GV. Four tests examined the fit results for: (1) physicality of ranges of model parameters, (2) effects on parameter values when different data subsets are fit, (3) characterization of residuals, and (4) extrapolative error and agreement with published correction factors. Test 1 showed physical Tk-GV results, where OLS-GV fits sometimes-produced nonphysical CL. Test 2 showed the Tk-GV model produced good results with 4 or more samples drawn between 10 and 240 min. Test 3 showed that E1 and E2 failed goodness-of-fit testing whereas GV fits for t > 20 min were acceptably good. Test 4 showed CLTk-GV clearance values agreed with published CL corrections with the general result that CLE1 > CLE2 > CLTk-GV and finally that CLTk-GV were considerably more robust, precise and accurate than CLE2, and should replace the use of CLE2 for these renal markers

Non Mycobacterial Virulence Genes in the Genome of the Emerging Pathogen Mycobacterium abscessus

Mycobacterium abscessus is an emerging rapidly growing mycobacterium (RGM) causing a pseudotuberculous lung disease to which patients with cystic fibrosis (CF) are particularly susceptible. We report here its complete genome sequence. The genome of M. abscessus (CIP 104536T) consists of a 5,067,172-bp circular chromosome including 4920 predicted coding sequences (CDS), an 81-kb full-length prophage and 5 IS elements, and a 23-kb mercury resistance plasmid almost identical to pMM23 from Mycobacterium marinum. The chromosome encodes many virulence proteins and virulence protein families absent or present in only small numbers in the model RGM species Mycobacterium smegmatis. Many of these proteins are encoded by genes belonging to a “mycobacterial” gene pool (e.g. PE and PPE proteins, MCE and YrbE proteins, lipoprotein LpqH precursors). However, many others (e.g. phospholipase C, MgtC, MsrA, ABC Fe(3+) transporter) appear to have been horizontally acquired from distantly related environmental bacteria with a high G+C content, mostly actinobacteria (e.g. Rhodococcus sp., Streptomyces sp.) and pseudomonads. We also identified several metabolic regions acquired from actinobacteria and pseudomonads (relating to phenazine biosynthesis, homogentisate catabolism, phenylacetic acid degradation, DNA degradation) not present in the M. smegmatis genome. Many of the “non mycobacterial” factors detected in M. abscessus are also present in two of the pathogens most frequently isolated from CF patients, Pseudomonas aeruginosa and Burkholderia cepacia. This study elucidates the genetic basis of the unique pathogenicity of M. abscessus among RGM, and raises the question of similar mechanisms of pathogenicity shared by unrelated organisms in CF patients