Can physical joint simulators be used to anticipate clinical wear problems of new joint replacement implants prior to market release?

Medley, J. B. (2016). Can physical joint simulators be used to anticipate clinical wear problems of new joint replacement implants prior to market release? Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine, 230(5), 347–358. Copyright © 2016. Sage Publications. Reprinted by permission of SAGE Publications. https://doi.org/10.1177/0954411916643902One of the most important mandates of physical joint simulators is to provide test results that allow the implant manufacturer to anticipate and perhaps avoid clinical wear problems with their new products. This is best done before market release. This study gives four steps to follow in conducting such wear simulator testing. Two major examples involving hip wear simulators are discussed in which attempts had been made to predict clinical wear performance prior to market release. The second one, involving the DePuy ASR implant systems, is chosen for more extensive treatment by making it an illustrative example to explore whether wear simulator testing can anticipate clinical wear problems. It is concluded that hip wear simulator testing did provide data in the academic literature that indicated some risk of clinical wear problems prior to market release of the ASR implant systems. This supports the idea that physical joint simulators have an important role in the pre-market testing of new joint replacement implants

Determination of freedom-from-rabies for small Indian mongoose populations in the United States Virgin Islands, 2019–2020

Mongooses, a nonnative species, are a known reservoir of rabies virus in the Caribbean region. A cross-sectional study of mongooses at 41 field sites on the US Virgin Islands of St. Croix, St. John, and St. Thomas captured 312 mongooses (32% capture rate). We determined the absence of rabies virus by antigen testing and rabies virus exposure by antibody testing in mongoose populations on all three islands. USVI is the first Caribbean state to determine freedom-from-rabies for its mongoose populations with a scientifically-led robust cross-sectional study. Ongoing surveillance activities will determine if other domestic and wildlife populations in USVI are rabies-free

Biomass production of herbaceous energy crops in the United States: field trial results and yield potential maps from the multiyear regional feedstock partnership

Current knowledge of yield potential and best agronomic management practices for perennial bioenergy grasses is primarily derived from small-scale and short-term studies, yet these studies inform policy at the national scale. In an effort to learn more about how bioenergy grasses perform across multiple locations and years, the U.S. Department of Energy (US DOE)/Sun Grant Initiative Regional Feedstock Partnership was initiated in 2008. The objectives of the Feedstock Partnership were to (1) provide a wide range of information for feedstock selection (species choice) and management practice options for a variety of regions and (2) develop national maps of potential feedstock yield for each of the herbaceous species evaluated. The Feedstock Partnership expands our previous understanding of the bioenergy potential of switchgrass, Miscanthus, sorghum, energycane, and prairie mixtures on Conservation Reserve Program land by conducting long-term, replicated trials of each species at diverse environments in the U.S. Trials were initiated between 2008 and 2010 and completed between 2012 and 2015 depending on species. Field-scale plots were utilized for switchgrass and Conservation Reserve Program trials to use traditional agricultural machinery. This is important as we know that the smaller scale studies often overestimated yield potential of some of these species. Insufficient vegetative propagules of energycane and Miscanthus prohibited farm-scale trials of these species. The Feedstock Partnership studies also confirmed that environmental differences across years and across sites had a large impact on biomass production. Nitrogen application had variable effects across feedstocks, but some nitrogen fertilizer generally had a positive effect. National yield potential maps were developed using PRISM-ELM for each species in the Feedstock Partnership. This manuscript, with the accompanying supplemental data, will be useful in making decisions about feedstock selection as well as agronomic practices across a wide region of the country

Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study

Background: The risk of severe COVID-19 if an individual becomes infected is known to be higher in older individuals and those with underlying health conditions. Understanding the number of individuals at increased risk of severe COVID-19 and how this varies between countries should inform the design of possible strategies to shield or vaccinate those at highest risk. Methods: We estimated the number of individuals at increased risk of severe disease (defined as those with at least one condition listed as “at increased risk of severe COVID-19” in current guidelines) by age (5-year age groups), sex, and country for 188 countries using prevalence data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 and UN population estimates for 2020. The list of underlying conditions relevant to COVID-19 was determined by mapping the conditions listed in GBD 2017 to those listed in guidelines published by WHO and public health agencies in the UK and the USA. We analysed data from two large multimorbidity studies to determine appropriate adjustment factors for clustering and multimorbidity. To help interpretation of the degree of risk among those at increased risk, we also estimated the number of individuals at high risk (defined as those that would require hospital admission if infected) using age-specific infection–hospitalisation ratios for COVID-19 estimated for mainland China and making adjustments to reflect country-specific differences in the prevalence of underlying conditions and frailty. We assumed males were twice at likely as females to be at high risk. We also calculated the number of individuals without an underlying condition that could be considered at increased risk because of their age, using minimum ages from 50 to 70 years. We generated uncertainty intervals (UIs) for our estimates by running low and high scenarios using the lower and upper 95% confidence limits for country population size, disease prevalences, multimorbidity fractions, and infection–hospitalisation ratios, and plausible low and high estimates for the degree of clustering, informed by multimorbidity studies. Findings: We estimated that 1·7 billion (UI 1·0–2·4) people, comprising 22% (UI 15–28) of the global population, have at least one underlying condition that puts them at increased risk of severe COVID-19 if infected (ranging from <5% of those younger than 20 years to >66% of those aged 70 years or older). We estimated that 349 million (186–787) people (4% [3–9] of the global population) are at high risk of severe COVID-19 and would require hospital admission if infected (ranging from <1% of those younger than 20 years to approximately 20% of those aged 70 years or older). We estimated 6% (3–12) of males to be at high risk compared with 3% (2–7) of females. The share of the population at increased risk was highest in countries with older populations, African countries with high HIV/AIDS prevalence, and small island nations with high diabetes prevalence. Estimates of the number of individuals at increased risk were most sensitive to the prevalence of chronic kidney disease, diabetes, cardiovascular disease, and chronic respiratory disease. Interpretation: About one in five individuals worldwide could be at increased risk of severe COVID-19, should they become infected, due to underlying health conditions, but this risk varies considerably by age. Our estimates are uncertain, and focus on underlying conditions rather than other risk factors such as ethnicity, socioeconomic deprivation, and obesity, but provide a starting point for considering the number of individuals that might need to be shielded or vaccinated as the global pandemic unfolds. Funding: UK Department for International Development, Wellcome Trust, Health Data Research UK, Medical Research Council, and National Institute for Health Research

Studies of η\eta and η′\eta' production in pppp and ppPb collisions

The production of $\eta$ and $\eta'$ mesons is studied in proton-proton and proton-lead collisions collected with the LHCb detector. Proton-proton collisions are studied at center-of-mass energies of $5.02$ and $13~{\rm TeV}$, and proton-lead collisions are studied at a center-of-mass energy per nucleon of $8.16~{\rm TeV}$. The studies are performed in center-of-mass rapidity regions $2.5<y_{\rm c.m.}<3.5$ (forward rapidity) and $-4.0<y_{\rm c.m.}<-3.0$ (backward rapidity) defined relative to the proton beam direction. The $\eta$ and $\eta'$ production cross sections are measured differentially as a function of transverse momentum for $1.5<p_{\rm T}<10~{\rm GeV}$ and $3<p_{\rm T}<10~{\rm GeV}$, respectively. The differential cross sections are used to calculate nuclear modification factors. The nuclear modification factors for $\eta$ and $\eta'$ mesons agree at both forward and backward rapidity, showing no significant evidence of mass dependence. The differential cross sections of $\eta$ mesons are also used to calculate $\eta/\pi^0$ cross section ratios, which show evidence of a deviation from the world average. These studies offer new constraints on mass-dependent nuclear effects in heavy-ion collisions, as well as $\eta$ and $\eta'$ meson fragmentation.Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lhcbproject.web.cern.ch/Publications/p/LHCb-PAPER-2023-030.html (LHCb public pages

Simulating respiratory disease transmission within and between classrooms to assess pandemic management strategies at schools

The global spread of coronavirus disease 2019 (COVID-19) has emphasized the need for evidence-based strategies for the safe operation of schools during pandemics that balance infection risk with the society\u27s responsibility of allowing children to attend school. Due to limited empirical data, existing analyses assessing school-based interventions in pandemic situations often impose strong assumptions, for example, on the relationship between class size and transmission risk, which could bias the estimated effect of interventions, such as split classes and staggered attendance. To fill this gap in school outbreak studies, we parameterized an individual-based model that accounts for heterogeneous contact rates within and between classes and grades to a multischool outbreak data of influenza. We then simulated school outbreaks of respiratory infectious diseases of ongoing threat (i.e., COVID-19) and potential threat (i.e., pandemic influenza) under a variety of interventions (changing class structures, symptom screening, regular testing, cohorting, and responsive class closures). Our results suggest that interventions changing class structures (e.g., reduced class sizes) may not be effective in reducing the risk of major school outbreaks upon introduction of a case and that other precautionary measures (e.g., screening and isolation) need to be employed. Class-level closures in response to detection of a case were also suggested to be effective in reducing the size of an outbreak

Contact tracing is an imperfect tool for controlling COVID-19 transmission and relies on population adherence

Abstract: Emerging evidence suggests that contact tracing has had limited success in the UK in reducing the R number across the COVID-19 pandemic. We investigate potential pitfalls and areas for improvement by extending an existing branching process contact tracing model, adding diagnostic testing and refining parameter estimates. Our results demonstrate that reporting and adherence are the most important predictors of programme impact but tracing coverage and speed plus diagnostic sensitivity also play an important role. We conclude that well-implemented contact tracing could bring small but potentially important benefits to controlling and preventing outbreaks, providing up to a 15% reduction in R. We reaffirm that contact tracing is not currently appropriate as the sole control measure

Production of η and η′ mesons in pp and pPb collisions

The production of η and η ′ mesons is studied in proton-proton and proton-lead collisions collected with the LHCb detector. Proton-proton collisions are studied at center-of-mass energies of 5.02 and 13 TeV and proton-lead collisions are studied at a center-of-mass energy per nucleon of 8.16 TeV . The studies are performed in center-of-mass (c.m.) rapidity regions 2.5 &lt; y c . m . &lt; 3.5 (forward rapidity) and − 4.0 &lt; y c . m . &lt; − 3.0 (backward rapidity) defined relative to the proton beam direction. The η and η ′ production cross sections are measured differentially as a function of transverse momentum for 1.5 &lt; p T &lt; 10 GeV and 3 &lt; p T &lt; 10 GeV , respectively. The differential cross sections are used to calculate nuclear modification factors. The nuclear modification factors for η and η ′ mesons agree at both forward and backward rapidity, showing no significant evidence of mass dependence. The differential cross sections of η mesons are also used to calculate η / π 0 cross-section ratios, which show evidence of a deviation from the world average. These studies offer new constraints on mass-dependent nuclear effects in heavy-ion collisions, as well as η and η ′ meson fragmentation

Incorporating ligament laxity in a finite element model for the upper cervical spine

© 2017 Elsevier Inc. Background Context Predicting physiological range of motion (ROM) using a finite element (FE) model of the upper cervical spine requires the incorporation of ligament laxity. The effect of ligament laxity can be observed only on a macro level of joint motion and is lost once ligaments have been dissected and preconditioned for experimental testing. As a result, although ligament laxity values are recognized to exist, specific values are not directly available in the literature for use in FE models. Purpose The purpose of the current study is to propose an optimization process that can be used to determine a set of ligament laxity values for upper cervical spine FE models. Furthermore, an FE model that includes ligament laxity is applied, and the resulting ROM values are compared with experimental data for physiological ROM, as well as experimental data for the increase in ROM when a Type II odontoid fracture is introduced. Design/Setting The upper cervical spine FE model was adapted from a 50th percentile male full-body model developed with the Global Human Body Models Consortium (GHBMC). FE modeling was performed in LS-DYNA and LS-OPT (Livermore Software Technology Group) was used for ligament laxity optimization. Methods Ordinate-based curve matching was used to minimize the mean squared error (MSE) between computed load-rotation curves and experimental load-rotation curves under flexion, extension, and axial rotation with pure moment loads from 0 to 3.5 Nm. Lateral bending was excluded from the optimization because the upper cervical spine was considered to be primarily responsible for flexion, extension, and axial rotation. Based on recommendations from the literature, four varying inputs representing laxity in select ligaments were optimized to minimize the MSE. Funding was provided by the Natural Sciences and Engineering Research Council of Canada as well as GHMBC. The present study was funded by the Natural Sciences and Engineering Research Council of Canada to support the work of one graduate student. There are no conflicts of interest to be reported. Results The MSE was reduced to 0.28 in the FE model with optimized ligament laxity compared with an MSE 0f 4.16 in the FE model without laxity. In all load cases, incorporating ligament laxity improved the agreement between the ROM of the FE model and the ROM of the experimental data. The ROM for axial rotation and extension was within one standard deviation of the experimental data. The ROM for flexion and lateral bending was outside one standard deviation of the experimental data, but a compromise was required to use one set of ligament laxity values to achieve a best fit to all load cases. Atlanto-occipital motion was compared as a ratio to overall ROM, and only in extension did the inclusion of ligament laxity not improve the agreement. After a Type II odontoid fracture was incorporated into the model, the increase in ROM was consistent with experimental data from the literature. Conclusions The optimization approach used in this study provided values for ligament laxities that, when incorporated into the FE model, generally improved the ROM response when compared with experimental data. Successfully modeling a Type II odontoid fracture showcased the robustness of the FE model, which can now be used in future biomechanics studies