Systematic review and meta-analysis of peak wall stress and peak wall rupture index in ruptured and asymptomatic intact abdominal aortic aneurysms

BACKGROUND: Prior studies have suggested aortic peak wall stress (PWS) and peak wall rupture index (PWRI) can estimate the rupture risk of an abdominal aortic aneurysm (AAA), but whether these measurements have independent predictive ability over assessing AAA diameter alone is unclear. The aim of this systematic review was to compare PWS and PWRI in participants with ruptured and asymptomatic intact AAAs of similar diameter. METHODS AND RESULTS: Web of Science, Scopus, Medline, and The Cochrane Library were systematically searched to identify studies assessing PWS and PWRI in ruptured and asymptomatic intact AAAs of similar diameter. Random-effects meta-analyses were performed using inverse variance-weighted methods. Leave-one-out sensitivity analyses were conducted to assess the robustness of findings. Risk of bias was assessed using a modification of the Newcastle-Ottawa scale and standard quality assessment criteria for evaluating primary research papers. Seven case-control studies involving 309 participants were included. Meta-analyses suggested that PWRI (standardized mean difference, 0.42; 95% CI, 0.14–0.70; P=0.004) but not PWS (standardized mean difference, 0.13; 95% CI, −0.18 to 0.44; P=0.418) was greater in ruptured than intact AAAs. Sensitivity analyses suggested that the findings were not dependent on the inclusion of any single study. The included studies were assessed to have a medium to high risk of bias. CONCLUSIONS: Based on limited evidence, this study suggested that PWRI, but not PWS, is greater in ruptured than asymptomatic intact AAAs of similar maximum aortic diameter

Association between aortic peak wall stress and rupture index with abdominal aortic aneurysm–related events

Objective: The aim of this study was to assess whether aortic peak wall stress (PWS) and peak wall rupture index (PWRI) were associated with the risk of abdominal aortic aneurysm (AAA) rupture or repair (defined as AAA events) among participants with small AAAs. Methods: PWS and PWRI were estimated from computed tomography angiography (CTA) scans of 210 participants with small AAAs (≥ 30 and ≤ 50 mm) prospectively recruited between 2002 and 2016 from two existing databases. Participants were followed for a median of 2.0 (inter-quartile range 1.9, 2.8) years to record the incidence of AAA events. The associations between PWS and PWRI with AAA events were assessed using Cox proportional hazard analyses. The ability of PWS and PWRI to reclassify the risk of AAA events compared to the initial AAA diameter was examined using net reclassification index (NRI) and classification and regression tree (CART) analysis. Results: After adjusting for other risk factors, one standard deviation increase in PWS (hazard ratio, HR, 1.56, 95% confidence intervals, CI 1.19, 2.06; p = 0.001) and PWRI (HR 1.74, 95% CI 1.29, 2.34; p 0.562. PWRI, but not PWS, significantly improved the classification of risk of AAA events compared to the initial AAA diameter alone. Conclusion: PWS and PWRI predicted the risk of AAA events but only PWRI significantly improved the risk stratification compared to aortic diameter alone. Key Points: • Aortic diameter is an imperfect measure of abdominal aortic aneurysm (AAA) rupture risk. • This observational study of 210 participants found that peak wall stress (PWS) and peak wall rupture index (PWRI) predicted the risk of aortic rupture or AAA repair. • PWRI, but not PWS, significantly improved the risk stratification for AAA events compared to aortic diameter alone

Mutator Dynamics on a Smooth Evolutionary Landscape

We investigate a model of evolutionary dynamics on a smooth landscape which features a ``mutator'' allele whose effect is to increase the mutation rate. We show that the expected proportion of mutators far from equilibrium, when the fitness is steadily increasing in time, is governed solely by the transition rates into and out of the mutator state. This results is a much faster rate of fitness increase than would be the case without the mutator allele. Near the fitness equilibrium, however, the mutators are severely suppressed, due to the detrimental effects of a large mutation rate near the fitness maximum. We discuss the results of a recent experiment on natural selection of E. coli in the light of our model.Comment: 4 pages, 3 figure

A new model for non-typeable Haemophilus influenzae middle ear infection in the Junbo mutant mouse

Acute otitis media, inflammation of the middle ear, is the most common bacterial infection in children and, as a consequence, is the most common reason for antimicrobial prescription to this age group. There is currently no effective vaccine for the principal pathogen involved, non-typeable Haemophilus influenzae (NTHi). The most frequently used and widely accepted experimental animal model of middle ear infection is in chinchillas, but mice and gerbils have also been used. We have established a robust model of middle ear infection by NTHi in the Junbo mouse, a mutant mouse line that spontaneously develops chronic middle ear inflammation in specific pathogen-free conditions. The heterozygote Junbo mouse (Jbo/+) bears a mutation in a gene (Evi1, also known as Mecom) that plays a role in host innate immune regulation; pre-existing middle ear inflammation promotes NTHi middle ear infection. A single intranasal inoculation with NTHi produces high rates (up to 90%) of middle ear infection and bacterial titres (104-105 colony-forming units/µl) in bulla fluids. Bacteria are cleared from the majority of middle ears between day 21 and 35 post-inoculation but remain in approximately 20% of middle ears at least up to day 56 post-infection. The expression of Toll-like receptor-dependent response cytokine genes is elevated in the middle ear of the Jbo/+ mouse following NTHi infection. The translational potential of the Junbo model for studying antimicrobial intervention regimens was shown using a 3 day course of azithromycin to clear NTHi infection, and its potential use in vaccine development studies was shown by demonstrating protection in mice immunized with killed homologous, but not heterologous, NTHi bacteria

High-accuracy numerical models of Brownian thermal noise in thin mirror coatings

Brownian coating thermal noise in detector test masses is limiting the sensitivity of current gravitational-wave detectors on Earth. Therefore, accurate numerical models can inform the ongoing effort to minimize Brownian coating thermal noise in current and future gravitational-wave detectors. Such numerical models typically require significant computational resources and time, and often involve closed-source commercial codes. In contrast, open-source codes give complete visibility and control of the simulated physics and enable direct assessment of the numerical accuracy. In this article, we use the open-source SpECTRE numerical-relativity code and adopt a novel discontinuous Galerkin numerical method to model Brownian coating thermal noise. We demonstrate that SpECTRE achieves significantly higher accuracy than a previous approach at a fraction of the computational cost. Furthermore, we numerically model Brownian coating thermal noise in multiple sub-wavelength crystalline coating layers for the first time. Our new numerical method has the potential to enable fast exploration of realistic mirror configurations, and hence to guide the search for optimal mirror geometries, beam shapes and coating materials for gravitational-wave detectors

Adding Gravitational Memory to Waveform Catalogs using BMS Balance Laws

Accurate models of gravitational waves from merging binary black holes are crucial for detectors to measure events and extract new science. One important feature that is currently missing from the Simulating eXtreme Spacetimes (SXS) Collaboration's catalog of waveforms for merging black holes, and other waveform catalogs, is the gravitational memory effect: a persistent, physical change to spacetime that is induced by the passage of transient radiation. We find, however, that by exploiting the Bondi-Metzner-Sachs (BMS) balance laws, which come from the extended BMS transformations, we can correct the strain waveforms in the SXS catalog to include the missing displacement memory. Our results show that these corrected waveforms satisfy the BMS balance laws to a much higher degree of accuracy. Furthermore, we find that these corrected strain waveforms coincide especially well with the waveforms obtained from Cauchy-characteristic extraction (CCE) that already exhibit memory effects. These corrected strain waveforms also evade the transient junk effects that are currently present in CCE waveforms. Lastly, we make our code for computing these contributions to the BMS balance laws and memory publicly available as a part of the python package $\texttt{sxs}$, thus enabling anyone to evaluate the expected memory effects and violation of the BMS balance laws