Enzymatically-degradable hydrogel coatings on titanium for bacterial infection inhibition and enhanced soft tissue compatibility via a self-adaptive strategy

ObjectiveTo investigate the rate of serious bacterial, viral or opportunistic infection in patients with rheumatoid arthritis (RA) starting tocilizumab (TCZ) versus tumour necrosis factor inhibitors (TNFi) or abatacept.MethodsUsing claims data from US Medicare from 2010 to 2015, and IMS and MarketScan from 2011 to 2015, we identified adults with RA who initiated TCZ or TNFi (primary comparator)/abatacept (secondary comparator) with prior use of ≥1 different biologic drug or tofacitinib. The primary outcome was hospitalised serious infection (SI), including bacterial, viral or opportunistic infection. To control for >70 confounders, TCZ initiators were propensity score (PS)-matched to TNFi or abatacept initiators. Database-specific HRs were combined by a meta-analysis.ResultsThe primary cohort included 16 074 TCZ PS-matched to 33 109 TNFi initiators. The risk of composite SI was not different between TCZ and TNFi initiators (combined HR 1.05, 95% CI 0.95 to 1.16). However, TCZ was associated with an increased risk of serious bacterial infection (HR 1.19, 95% CI 1.07 to 1.33), skin and soft tissue infections (HR 2.38, 95% CI 1.47 to 3.86), and diverticulitis (HR 2.34, 95% CI 1.64 to 3.34) versus TNFi. An increased risk of composite SI, serious bacterial infection, diverticulitis, pneumonia/upper respiratory tract infection and septicaemia/bacteraemia was observed in TCZ versus abatacept users.ConclusionsThis large multidatabase cohort study found no difference in composite SI risk in patients with RA initiating TCZ versus TNFi after failing ≥1 biologic drug or tofacitinib. However, the risk of serious bacterial infection, skin and soft tissue infections, and diverticulitis was higher in TCZ versus TNFi initiators. The risk of composite SI was higher in TCZ initiators versus abatacept.

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Bacterial colony foraging algorithm: Combining chemotaxis, cell-to-cell communication, and self-adaptive strategy

Information Sciences ◽

10.1016/j.ins.2014.02.161 ◽

2014 ◽

Vol 273 ◽

pp. 73-100 ◽

Cited By ~ 16

Author(s):

Hanning Chen ◽

Yunlong Zhu ◽

Kunyuan Hu ◽

Lianbo Ma

Keyword(s):

Cell Communication ◽

Adaptive Strategy ◽

Bacterial Colony ◽

Self Adaptive

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A Novel Mating Approach for Genetic Algorithms

Evolutionary Computation ◽

10.1162/evco_a_00067 ◽

2013 ◽

Vol 21 (2) ◽

pp. 197-229 ◽

Cited By ~ 14

Author(s):

Severino F. Galán ◽

Ole J. Mengshoel ◽

Rafael Pinter

Keyword(s):

Genetic Algorithms ◽

Random Mating ◽

Adaptive Strategy ◽

Mating Strategies ◽

Function Optimization ◽

The Novel ◽

Independent Manner ◽

Novel Approach ◽

Domain Independent ◽

Self Adaptive

Genetic algorithms typically use crossover, which relies on mating a set of selected parents. As part of crossover, random mating is often carried out. A novel approach to parent mating is presented in this work. Our novel approach can be applied in combination with a traditional similarity-based criterion to measure distance between individuals or with a fitness-based criterion. We introduce a parameter called the mating index that allows different mating strategies to be developed within a uniform framework: an exploitative strategy called best-first, an explorative strategy called best-last, and an adaptive strategy called self-adaptive. Self-adaptive mating is defined in the context of the novel algorithm, and aims to achieve a balance between exploitation and exploration in a domain-independent manner. The present work formally defines the novel mating approach, analyzes its behavior, and conducts an extensive experimental study to quantitatively determine its benefits. In the domain of real function optimization, the experiments show that, as the degree of multimodality of the function at hand grows, increasing the mating index improves performance. In the case of the self-adaptive mating strategy, the experiments give strong results for several case studies.

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