A Systematic Evidence Map Protocol of Time Activity Data in Exposure Science

Time Activity Data (TAD) describe the frequency, duration, and timing of human activities. Given that activity dictates the rate of contact a person has with an environmental hazard, activity data can be used to derive rigorous estimates of exposure. TAD have been used to support exposure estimation in a variety of contexts, though there has been no systematic characterization of the use of this approach in exposure science and environmental epidemiology. Here, we propose a protocol in pursuit of characterizing the body of peer-reviewed literature using TAD in the estimation of exposure to chemical, biological, and physical hazards in the form of an evidence map. This protocol details the proposed search strategy and plan for data extraction including study population demographics, methods of TAD collection, and study participant activities. Reflexive journaling, codebook development, and descriptive statistics will be conducted to analyze the data collected as part of this evidence map

Classification of antineutrophil cytoplasmic autoantibody vasculitides: The role of antineutrophil cytoplasmic autoantibody specificity for myeloperoxidase or proteinase 3 in disease recognition and prognosis

Anti-neutrophil cytoplasmic antibody (ANCA) vasculitis is a complex disease, with much debate about the utility of systems for classification and diagnosis. We compared three currently used classification systems in predicting disease prognosis

Coronavirus disease 2019 (COVID-19) pharmacologic treatments for children : research priorities and approach to pediatric studies

CITATION: Garcia-Prats, A. J. et al. 2021. Coronavirus Disease 2019 (COVID-19) Pharmacologic Treatments for Children: Research Priorities and Approach to Pediatric Studies. Clinical infectious diseases, 72(6):1067–1073. doi:10.1093/cid/ciaa885The original publication is available at https://academic.oup.com/cid/Clinical trials of pharmacologic treatments of coronavirus disease 2019 (COVID-19) are being rapidly designed and implemented in adults. Children are often not considered during development of novel treatments for infectious diseases until very late. Although children appear to have a lower risk compared with adults of severe COVID-19 disease, a substantial number of children globally will benefit from pharmacologic treatments. It will be reasonable to extrapolate efficacy of most treatments from adult trials to children. Pediatric trials should focus on characterizing a treatment’s pharmacokinetics, optimal dose, and safety across the age spectrum. These trials should use an adaptive design to efficiently add or remove arms in what will be a rapidly evolving treatment landscape, and should involve a large number of sites across the globe in a collaborative effort to facilitate efficient implementation. All stakeholders must commit to equitable access to any effective, safe treatment for children everywhere.https://academic.oup.com/cid/article/72/6/1067/5864500?login=truePublishers versio

Persistence of livestock-associated antibiotic-resistant Staphylococcus aureus among industrial hog operation workers in North Carolina over 14 days

ObjectivesThis study aimed to evaluate the persistence of nasal carriage of Staphylococcus aureus, methicillin-resistant S. aureus and multidrug-resistant S. aureus over 14 days of follow-up among industrial hog operation workers in North Carolina.MethodsWorkers anticipating at least 24 h away from work were enrolled June–August 2012. Participants self-collected a nasal swab and completed a study journal on the evening of day 1, and each morning and evening on days 2–7 and 14 of the study. S. aureus isolated from nasal swabs were assessed for antibiotic susceptibility, spa type and absence of the scn gene. Livestock association was defined by absence of scn.ResultsTwenty-two workers provided 327 samples. S. aureus carriage end points did not change with time away from work (mean 49 h; range >0–96 h). Ten workers were persistent and six were intermittent carriers of livestock-associated S. aureus. Six workers were persistent and three intermittent carriers of livestock-associated multidrug-resistant S. aureus. One worker persistently carried livestock-associated methicillin-resistant S. aureus. Six workers were non-carriers of livestock-associated S. aureus. Eighty-two per cent of livestock-associated S. aureus demonstrated resistance to tetracycline. A majority of livestock-associated S. aureus isolates (n=169) were CC398 (68%) while 31% were CC9. No CC398 and one CC9 isolate was detected among scn-positive isolates.ConclusionsNasal carriage of livestock-associated S. aureus, multidrug-resistant S. aureus and methicillin-resistant S. aureus can persist among industrial hog operation workers over a 14-day period, which included up to 96 h away from work

Rituximab as an immunosuppressant in antineutrophil cytoplasmic antibody-associated vasculitis

Rituximab has been used in antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) since 2003. Our objective was to describe outcomes and adverse events following rituximab since that time in an inception cohort

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Towards early inclusion of children in tuberculosis drugs trials : a consensus statement

Children younger than 18 years account for a substantial proportion of patients with tuberculosis worldwide. Available treatments for paediatric drug-susceptible and drug-resistant tuberculosis, albeit generally eff ective, are hampered by high pill burden, long duration of treatment, coexistent toxic eff ects, and an overall scarcity of suitable child-friendly formulations. Several new drugs and regimens with promising activity against both drug-susceptible and drug-resistant strains have entered clinical development and are either in various phases of clinical investigation or have received marketing authorisation for adults; however, none have data on their use in children. This consensus statement, generated from an international panel of opinion leaders on childhood tuberculosis and incorporating reviews of published literature from January, 2004, to May, 2014, addressed four key questions: what drugs or regimens should be prioritised for clinical trials in children? Which populations of children are high priorities for study? When can phase 1 or 2 studies be initiated in children? What are the relevant elements of clinical trial design? The consensus panel found that children can be included in studies at the early phases of drug development and should be an integral part of the clinical development plan, rather than studied after regulatory approval in adults is obtained.National Institute of Allergy and Infectious Diseases and National Institute of Health.Department of Health and Human Services.http://www.thelancet.com/infection2016-06-30hb201

The evolving research agenda for paediatric tuberculosis infection

There are unique challenges facing the diagnosis and management of tuberculosis infection in children. Following exposure to an infectious tuberculosis case and subsequent infection, children frequently progress to tuberculosis disease more rapidly than adults. Increasingly, investigators recognize the concept of sub clinical disease, an entity referring to early asymptomatic disease. Our understanding of the pathogenesis of tuberculosis in children remains limited but could be improved through animal models, laboratory studies evaluating the responses of blood or respiratory samples to mycobacteria in vitro, as well as evaluating immune responses in children exposed to tuberculosis. Identifying children with sub-clinical disease, at high risk of progression to clinically apparent disease, through biomarker discover, would mean that treatment could be targeted to those most likely to benefit. These studies could be embedded in large observational or interventional cohorts. The optimization and discovery of novel treatments for tuberculosis infection in children need to account for mechanisms of action of tuberculosis drugs as well as child-specific factors including pharmacokinetics and appropriate formulations. In this article we present the result of discussions at a large international meeting and the series of research priorities that were developed

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark