To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
New technologies and disruptions related to Coronavirus disease-2019 have led to expansion of decentralized approaches to clinical trials. Remote tools and methods hold promise for increasing trial efficiency and reducing burdens and barriers by facilitating participation outside of traditional clinical settings and taking studies directly to participants. The Trial Innovation Network, established in 2016 by the National Center for Advancing Clinical and Translational Science to address critical roadblocks in clinical research and accelerate the translational research process, has consulted on over 400 research study proposals to date. Its recommendations for decentralized approaches have included eConsent, participant-informed study design, remote intervention, study task reminders, social media recruitment, and return of results for participants. Some clinical trial elements have worked well when decentralized, while others, including remote recruitment and patient monitoring, need further refinement and assessment to determine their value. Partially decentralized, or “hybrid” trials, offer a first step to optimizing remote methods. Decentralized processes demonstrate potential to improve urban-rural diversity, but their impact on inclusion of racially and ethnically marginalized populations requires further study. To optimize inclusive participation in decentralized clinical trials, efforts must be made to build trust among marginalized communities, and to ensure access to remote technology.
Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.
We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample.
Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs.
An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.
Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).
A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.
In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.
Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.
Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.
This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.
32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.
Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.
Gatherings where people are eating and drinking can increase the risk of getting and spreading SARS-CoV-2 among people who are not fully vaccinated; prevention strategies like wearing masks and physical distancing continue to be important for some groups. We conducted an online survey to characterise fall/winter 2020–2021 holiday gatherings, decisions to attend and prevention strategies employed during and before gatherings. We determined associations between practicing prevention strategies, demographics and COVID-19 experience. Among 502 respondents, one-third attended in person holiday gatherings; 73% wore masks and 84% practiced physical distancing, but less did so always (29% and 23%, respectively). Younger adults were 44% more likely to attend gatherings than adults ≥35 years. Younger adults (adjusted prevalence ratio (aPR) 1.53, 95% CI 1.19–1.97), persons who did not experience COVID-19 themselves or have relatives/close friends experience severe COVID-19 (aPR 1.56, 95% CI 1.18–2.07), and non-Hispanic White persons (aPR 1.57, 95% CI 1.13–2.18) were more likely to not always wear masks in public during the 2 weeks before gatherings. Public health messaging emphasizing consistent application of COVID-19 prevention strategies is important to slow the spread of COVID-19.
Clinical trials continue to face significant challenges in participant recruitment and retention. The Recruitment Innovation Center (RIC), part of the Trial Innovation Network (TIN), has been funded by the National Center for Advancing Translational Sciences of the National Institutes of Health to develop innovative strategies and technologies to enhance participant engagement in all stages of multicenter clinical trials. In collaboration with investigator teams and liaisons at Clinical and Translational Science Award institutions, the RIC is charged with the mission to design, field-test, and refine novel resources in the context of individual clinical trials. These innovations are disseminated via newsletters, publications, a virtual toolbox on the TIN website, and RIC-hosted collaboration webinars. The RIC has designed, implemented, and promised customized recruitment support for 173 studies across many diverse disease areas. This support has incorporated site feasibility assessments, community input sessions, recruitment materials recommendations, social media campaigns, and an array of study-specific suggestions. The RIC’s goal is to evaluate the efficacy of these resources and provide access to all investigating teams, so that more trials can be completed on time, within budget, with diverse participation, and with enough accrual to power statistical analyses and make substantive contributions to the advancement of healthcare.
Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and spectroscopy of materials on length scales ranging from microns to atoms. By using a high-speed, direct electron detector, it is now possible to record a full two-dimensional (2D) image of the diffracted electron beam at each probe position, typically a 2D grid of probe positions. These 4D-STEM datasets are rich in information, including signatures of the local structure, orientation, deformation, electromagnetic fields, and other sample-dependent properties. However, extracting this information requires complex analysis pipelines that include data wrangling, calibration, analysis, and visualization, all while maintaining robustness against imaging distortions and artifacts. In this paper, we present py4DSTEM, an analysis toolkit for measuring material properties from 4D-STEM datasets, written in the Python language and released with an open-source license. We describe the algorithmic steps for dataset calibration and various 4D-STEM property measurements in detail and present results from several experimental datasets. We also implement a simple and universal file format appropriate for electron microscopy data in py4DSTEM, which uses the open-source HDF5 standard. We hope this tool will benefit the research community and help improve the standards for data and computational methods in electron microscopy, and we invite the community to contribute to this ongoing project.
We describe here efforts to create and study magnetized electron–positron pair plasmas, the existence of which in astrophysical environments is well-established. Laboratory incarnations of such systems are becoming ever more possible due to novel approaches and techniques in plasma, beam and laser physics. Traditional magnetized plasmas studied to date, both in nature and in the laboratory, exhibit a host of different wave types, many of which are generically unstable and evolve into turbulence or violent instabilities. This complexity and the instability of these waves stem to a large degree from the difference in mass between the positively and the negatively charged species: the ions and the electrons. The mass symmetry of pair plasmas, on the other hand, results in unique behaviour, a topic that has been intensively studied theoretically and numerically for decades, but experimental studies are still in the early stages of development. A levitated dipole device is now under construction to study magnetized low-energy, short-Debye-length electron–positron plasmas; this experiment, as well as a stellarator device that is in the planning stage, will be fuelled by a reactor-based positron source and make use of state-of-the-art positron cooling and storage techniques. Relativistic pair plasmas with very different parameters will be created using pair production resulting from intense laser–matter interactions and will be confined in a high-field mirror configuration. We highlight the differences between and similarities among these approaches, and discuss the unique physics insights that can be gained by these studies.
A national need is to prepare for and respond to accidental or intentional disasters categorized as chemical, biological, radiological, nuclear, or explosive (CBRNE). These incidents require specific subject-matter expertise, yet have commonalities. We identify 7 core elements comprising CBRNE science that require integration for effective preparedness planning and public health and medical response and recovery. These core elements are (1) basic and clinical sciences, (2) modeling and systems management, (3) planning, (4) response and incident management, (5) recovery and resilience, (6) lessons learned, and (7) continuous improvement. A key feature is the ability of relevant subject matter experts to integrate information into response operations. We propose the CBRNE medical operations science support expert as a professional who (1) understands that CBRNE incidents require an integrated systems approach, (2) understands the key functions and contributions of CBRNE science practitioners, (3) helps direct strategic and tactical CBRNE planning and responses through first-hand experience, and (4) provides advice to senior decision-makers managing response activities. Recognition of both CBRNE science as a distinct competency and the establishment of the CBRNE medical operations science support expert informs the public of the enormous progress made, broadcasts opportunities for new talent, and enhances the sophistication and analytic expertise of senior managers planning for and responding to CBRNE incidents.
To evaluate hospital characteristics and practices used by Thai hospitals to prevent catheter-associated urinary tract infection (CAUTI), central line-associated bloodstream infection (CLABSI), and ventilator-associated pneumonia (VAP), the 3 most common types of healthcare-associated infection (HAI) in Thailand.
Thai hospitals with an intensive care unit and 250 or more hospital beds
Between January 1, 2010, and October 31, 2010, research nurses collected data from all eligible hospitals. The survey assessed hospital characteristics and practices to prevent CAUTI, CLABSI, and VAP. Ordinal logistic regression was used to assess relationships between hospital characteristics and use of prevention practices.
A total of 204 (80%) of 256 hospitals responded. Most hospitals (93%) reported regularly using alcohol-based hand rub. The most frequently reported prevention practice by infection was as follows: for CAUTI, condom catheters in men (47%); for CLABSI, avoiding routine central venous catheter changes (85%); and for VAP, semirecumbent positioning (84%). Hospitals with peripherally inserted central catheter insertion teams were more likely to regularly use elements of the CLABSI prevention bundle. Greater safety scores were associated with regular use of several VAP prevention practices. The only hospital characteristic associated with increased use of at least 1 prevention practice for each infection was membership in an HAI collaborative.
While reported adherence to hand hygiene was high, many of the prevention practices for CAUTI, CLABSI, and VAP were used infrequently in Thailand. Policies and interventions emphasizing specific infection prevention practices, establishing a strong institutional safety culture, and participating in collaboratives to prevent HAI may be beneficial.
Immigration is the story of American history. From the earliest days of our nation, generation upon generation of immigrants has come to be part of a land that offers freedom and opportunity to those willing to do their part. Immigrants built our great cities. They cultivated our rich farmlands. They built the railroads and highways that bind America from sea to shining sea. It is said that under every railroad tie, an Irishman is buried.
Immigrants erected houses of worship to practice their faiths. They fought under America's colors in our wars. In fact, seventy thousand immigrants are serving in the U.S. armed forces in the world today. Immigrants worked hard so that their children could enjoy the ever-widening possibilities in our land. Over the centuries, immigrants came to America from every part of the globe and reached the American Dream. They created a nation that is the envy of the world.
That is our history. But it is also our present and our future. As recent years have made clear, however, our current system is broken and fails to meet our nation's modern needs. Our borders are out of control at a time of heightened concern about terrorism. Vast numbers cross our borders and remain illegally, creating an underground society that is vulnerable to exploitation and abuse. I heartily agree with Professor Hing's philosophy.
Feeding diets depleted of vitamin E and Se to cattle can induce a disease known as nutritional degenerative myopathy. It is believed that an increased peroxidative challenge in muscle is involved in the pathogenesis of this disease. A number of species can up-regulate the activity of some antioxidant enzymes, including glutathione reductase (EC 188.8.131.52), glutathione transferase (EC 184.108.40.206), glucose-6-phosphate dehydrogenase (EC 220.127.116.11), catalase (EC 18.104.22.168), and superoxide dismutase (EC 22.214.171.124), in an attempt to mitigate the effects of a peroxidative challenge. A 2 × 2 factorial study was set up to examine possible changes in the activities of these antioxidant enzymes in muscles of ruminant calves fed on diets low in either vitamin E or Se. Four groups of four calves each were fed on a basal diet of NaOH-treated barley which was supplemented with α-tocopherol or Se or both for a total of 50 weeks. Calves fed on diets depleted of vitamin E, but not those ted on diets low in Se, developed subclinical myopathy, as judged by increases in the activity of plasma creatine kinase (EC 126.96.36.199), and had increased muscle concentrations of two indices of lipid peroxidation, namely thiobarbituric acidreactive substances, with and without ascorbate activation. Feeding diets depleted of vitamin E and diets low in Se both increased muscle activities of glucose-6-phosphate dehydregenase in heart, biceps and supraspinatus. This change may have occurred in an attempt to maintain intracellular pools of reduced glutathione. No other changes in antioxidant enzyme activity were observed.