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Lysine 5,6-aminomutase is an adenosylcobalamin and pyridoxal-5 -phosphate-dependent enzyme that catalyzes a 1,2 rearrangement of the terminal amino group of DL-lysine and of L-␤-lysine. We have solved the x-ray structure of a substrate-free form of lysine-5,6-aminomutase from Clostridium sticklandii. In this structure, a Rossmann domain covalently binds pyridoxal-5 -phosphate by means of lysine 144 and positions it into the putative active site of a neighboring triosephosphate isomerase barrel domain, while simultaneously positioning the other cofactor, adenosylcobalamin, Ϸ25 Å from the active site. In this mode of pyridoxal-5 -phosphate binding, the cofactor acts as an anchor, tethering the separate polypeptide chain of the Rossmann domain to the triosephosphate isomerase barrel domain. Upon substrate binding and transaldimination of the lysine-144 linkage, the Rossmann domain would be free to rotate and bring adenosylcobalamin, pyridoxal-5 -phosphate, and substrate into proximity. Thus, the structure embodies a locking mechanism to keep the adenosylcobalamin out of the active site and prevent radical generation in the absence of substrate.A denosylcobalamin (AdoCbl; coenzyme B 12 ) is nature's biochemical radical reservoir, capable of catalyzing challenging chemical reactions by way of H atom abstraction and the generation of free-radical intermediates (1-3). AdoCbldependent isomerases catalyze 1,2 shifts between an H atom and a functional group such as -OH, -NH 3 ϩ , -(CO)S-coenzyme A, or other carbon-based groups. The catalytic power of AdoCbl lies in the homolytic cleavage of its weak (Ϸ30 kcal͞mol) organometallic C-Co bond, formed between an octahedral Co(III) center with five N coordinations and a 5Ј-deoxyadenosyl (Ado) axial ligand. C-Co bond homolysis results in the transient formation of cob(II)alamin and 5Ј-deoxyadenosyl radical (Ado • ). Ado • abstracts an H atom from the substrate, forming a substrate radical and 5Ј-deoxyadenosine (AdoH). To close the catalytic cycle, substrate reabstracts the H atom from AdoH, and recombination of cob(II)alamin and Ado • accompanies product formation. Amazingly, the enzymatic rate of C-Co bond homolysis is enhanced by a factor of Ϸ10 12 over nonenzymatic homolysis (4, 5). AdoCbl-dependent isomerases are often present in catabolic pathways and can serve to rearrange the substrate's carbon skeleton and͞or functional groups for further degradation. One such pathway that operates in several bacterial species is the fermentation of lysine to yield acetate. Interestingly, the lysine fermentation pathway contains two analogous enzymes: lysine 5,6-aminomutase (5,6-LAM), which is AdoCbl-dependent (6, 7), and lysine 2,3-aminomutase (2,3-LAM), which is an S-adenosylmethionine (AdoMet or SAM)-dependent ironsulfur enzyme (8-10). Both enzymes require pyridoxal 5Ј-phosphate (PLP) (8, 11) in addition to AdoCbl or AdoMet, and both catalyze a 1,2 amino group shift with concomitant H atom migration (Fig. 1A). In 5,6-LAM, AdoCbl is the source of the transient Ado • , whereas, in 2,3-L...

Microsimulation models are becoming increasingly common in the field of decision modeling for health. Because microsimulation models are computationally more demanding than traditional Markov cohort models, the use of computer programming languages in their development has become more common. R is a programming language that has gained recognition within the field of decision modeling. It has the capacity to perform microsimulation models more efficiently than software commonly used for decision modeling, incorporate statistical analyses within decision models, and produce more transparent models and reproducible results. However, no clear guidance for the implementation of microsimulation models in R exists. In this tutorial, we provide a step-by-step guide to build microsimulation models in R and illustrate the use of this guide on a simple, but transferable, hypothetical decision problem. We guide the reader through the necessary steps and provide generic R code that is flexible and can be adapted for other models. We also show how this code can be extended to address more complex model structures and provide an efficient microsimulation approach that relies on vectorization solutions.

As the complexity of health decision science applications increases, high-level programming languages are increasingly adopted for statistical analyses and numerical computations. These programming languages facilitate sophisticated modeling, model documentation, and analysis reproducibility. Among the high-level programming languages, the statistical programming framework R is gaining increased recognition. R is freely available, cross-platform compatible, and open source. A large community of users who have generated an extensive collection of well-documented packages and functions supports it. These functions facilitate applications of health decision science methodology as well as the visualization and communication of results. Although R's popularity is increasing among health decision scientists, methodological extensions of R in the field of decision analysis remain isolated. The purpose of this article is to provide an overview of existing R functionality that is applicable to the various stages of decision analysis, including model design, input parameter estimation, and analysis of model outputs.

Background Macular edema is the most common cause of visual loss among patients with diabetes. Objective To determine the cost-effectiveness of different treatments of diabetic macular edema (DME). Design Markov model. Data Sources Published literature and expert opinion. Target Population Patients with clinically significant DME. Time Horizon Lifetime. Perspective Societal. Intervention Laser treatment, intraocular injections of triamcinolone or a vascular endothelial growth factor (VEGF) inhibitor, or a combination of both. Outcome Measures Discounted costs, gains in quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs). Results of Base-Case Analysis All treatments except laser monotherapy substantially reduced costs, and all treatments except triamcinolone monotherapy increased QALYs. Laser treatment plus a VEGF inhibitor achieved the greatest benefit, gaining 0.56 QALYs at a cost of $6975 for an ICER of $12 410 per QALY compared with laser treatment plus triamcinolone. Monotherapy with a VEGF inhibitor achieved similar outcomes to combination therapy with laser treatment plus a VEGF inhibitor. Laser monotherapy and triamcinolone monotherapy were less effective and more costly than combination therapy. Results of Sensitivity Analysis VEGF inhibitor monotherapy was sometimes preferred over laser treatment plus a VEGF inhibitor, depending on the reduction in quality of life with loss of visual acuity. When the VEGF inhibitor bevacizumab was as effective as ranibizumab, it was preferable to because of its lower cost. Limitation Long-term outcome data for treated and untreated diseases are limited. Conclusion The most effective treatment of DME is VEGF inhibitor injections with or without laser treatment. This therapy compares favorably with cost-effective interventions for other conditions. Primary Funding Source Agency for Healthcare Research and Quality.

Movement of livestock between premises is one of the foremost factors contributing to the spread of infectious diseases of livestock. In part to address this issue, the origin and destination for all cattle movements in Uruguay are registered by law. This information has great potential to be used in assessing the risk of disease spread in the Uruguayan cattle population. Here, we analyze cattle movements from 2008 to 2013 using network analysis in order to understand the flows of animals in the Uruguayan cattle industry and to identify targets for surveillance and control measures. Cattle movements were represented as seasonal and annual networks in which farms represented nodes and nodes were linked based on the frequency and quantity of cattle moved. At the farm level, the distribution of the number of unique farms each farm is connected to through outgoing and incoming movements, as well as the number of animals moved, was highly right-skewed; the majority of farms had few to no contacts, whereas the 10% most highly connected farms accounted for 72-83% of animals moved annually. This extreme level of heterogeneity in movement patterns indicates that some farms may be disproportionately important for pathogen spread. Different production types exhibited characteristic patterns of farm-level connectivity, with some types, such a dairies, showing consistently higher levels of centrality. In addition, the observed networks were characterized by lower levels of connectivity and higher levels of heterogeneity than random networks of the same size and density, both of which have major implications for disease dynamics and control strategies. This represents the first in-depth analysis of farm-level livestock movements within South America, and highlights the importance of collecting livestock movement data in order to understand the vulnerability of livestock trade networks to invasion by infectious diseases.

Background In 2018, the Infectious Diseases Society of America (IDSA) published guidelines for diagnosis and treatment of Clostridioides (formerly Clostridium) difficile infection (CDI). However, there is little guidance regarding which treatments are cost-effective. Methods We used a Markov model to simulate a cohort of patients presenting with an initial CDI diagnosis. We used the model to estimate the costs, effectiveness, and cost-effectiveness of different CDI treatment regimens recommended in the recently published 2018 IDSA guidelines. The model includes stratification by the severity of the initial infection, and subsequent likelihood of cure, recurrence, mortality, and outcomes of subsequent recurrences. Data sources were taken from IDSA guidelines and published literature on treatment outcomes. Outcome measures were discounted quality-adjusted life-years (QALYs), costs, and incremental cost-effectiveness ratios (ICERs). Results Use of fidaxomicin for nonsevere initial CDI, vancomycin for severe CDI, fidaxomicin for first recurrence, and fecal microbiota transplantation (FMT) for subsequent recurrence (strategy 44) cost an additional $478 for 0.009 QALYs gained per CDI patient, resulting in an ICER of $31 751 per QALY, below the willingness-to-pay threshold of $100 000/QALY. This is the optimal, cost-effective CDI treatment strategy. Conclusions Metronidazole is suboptimal for nonsevere CDI as it is less beneficial than alternative strategies. The preferred treatment regimen is fidaxomicin for nonsevere CDI, vancomycin for severe CDI, fidaxomicin for first recurrence, and FMT for subsequent recurrence. The most effective treatments, with highest cure rates, are also cost-effective due to averted mortality, utility loss, and costs of rehospitalization and/or further treatments for recurrent CDI.