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 email@example.com
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.
Advancements in high content image analysis have led to an increase in the adoption of these techniques in basic science and clinical research. High-throughput approaches to imaging and image analysis require minimal user interventions, circumventing the often prohibitively time-consuming and unreliable standard manual analysis. In this study, we demonstrate how high content imaging (HCI) techniques, in combination with high content analysis (HCA), can be paired with more traditional manual analysis to quantify both micro- and macro-level features of biopsied tissue sections. High-resolution, full-color images of stained tissue were acquired and stitched together to reconstruct the entire tissue section, which enabled analyses that required accurate identification of a given region's location within the tissue section. A custom region of interest grid was generated that followed the curvature of the tissue. The composite images were used in two separate analyses: tissue layer thickness as a macro-level approach, and nuclei density as a micro-level approach. Ultimately, the flexibility of the HCI and HCA methodologies used in this study allowed for complex analysis of tissue that would not have been otherwise feasible.
The transformation of Second World War heritage sites is a common challenge for today's memory culture. In this project, we combine ground truthing with drone-based, high-resolution laser scanning to document recent anthropogenically and environmentally caused transformation processes, and to raise public awareness of the importance of the ever-changing conflict landscape of the ‘Huertgen Forest’.
Recent findings have shown that the continued expansion of the scope and scale of data collected in electronic health records are making the protection of personally identifiable information (PII) more challenging and may inadvertently put our institutions and patients at risk if not addressed. As clinical terminologies expand to include new terms that may capture PII (e.g., Patient First Name, Patient Phone Number), institutions may start using them in clinical data capture (and in some cases, they already have). Once in use, PII-containing values associated with these terms may find their way into laboratory or observation data tables via extract-transform-load jobs intended to process structured data, putting institutions at risk of unintended disclosure. Here we aim to inform the informatics community of these findings, as well as put out a call to action for remediation by the community.
Since 2013, the coverage of innovative and expensive drugs by the French National Health Insurance considers cost-effectiveness and budget impact, as assessed by the National Authority for Health (HAS) on the basis of an evaluation submitted by the firm. First CAR-T cell therapies were subject to economic evaluation in 2019 in France. We aim at describing the process and results of the economic evaluation of tisagenlecleucel and axicabtagene ciloleucel and the challenges these evaluations raised.
Primary evaluations were submitted by the firms to be reviewed by HAS. The final analyses were submitted to the Committee of Economic Evaluation and Public Health (CEESP), composed of independent economists, clinicians and patients’ representatives. The CEESP issued Opinions related to i) the methodological quality of economic evidence and ii) the cost-effectiveness and budget impact of the drugs under review.
The estimated incremental cost-utility ratio (ICUR) of tisagenlecleucel were rejected, being based on insufficient clinical evidence to estimate and extrapolate the long-term progression and to compare tisagenlecleucel with alternatives. Thus, the CEESP concluded that tisagenlecleucel was not proved cost-effective. The estimated ICUR of axicabtagene ciloleucel at 114,509EUR/QALY vs. chemotherapies was associated with an acceptable level of evidence despite being based on a frail indirect comparison and limited data on quality of life. In a context where France has no official cost-effectiveness threshold, the CEESP considered axicabtagene ciloleucel ICUR to be “very high” and questioned the collective acceptability of the claimed price.
The CEESP stressed that the main source of uncertainty surrounding the ICUR estimates of both drugs was related to the lack of hindsight on effectiveness, especially in terms of overall survival and safety.
The economic evaluation of CAR-T cell therapies highlights the sources of uncertainty underlying the decision and the risk of inefficient resource allocation driven by limited clinical data. It calls for payment schemes accounting for the uncertainty, and effective collection of relevant post-marketing data.
Machine learning uses historical data to make predictions about new data. It has been frequently applied in healthcare to optimise diagnostic classification through discovery of hidden patterns in data that may not be obvious to clinicians. Congenital Heart Defect (CHD) machine learning research entails one of the most promising clinical applications, in which timely and accurate diagnosis is essential. The objective of this scoping review is to summarise the application and clinical utility of machine learning techniques used in paediatric cardiology research, specifically focusing on approaches aiming to optimise diagnosis and assessment of underlying CHD. Out of 50 full-text articles identified between 2015 and 2021, 40% focused on optimising the diagnosis and assessment of CHD. Deep learning and support vector machine were the most commonly used algorithms, accounting for an overall diagnostic accuracy > 0.80. Clinical applications primarily focused on the classification of auscultatory heart sounds, transthoracic echocardiograms, and cardiac MRIs. The range of these applications and directions of future research are discussed in this scoping review.
Adolescents with CHD require transition to specialised adult-centred care. Previous studies have shown that adolescents’ knowledge of their medical condition is correlated with transition readiness. Three-dimensional printed models of CHD have been used to educate medical trainees and patients, although no studies have focused on adolescents with CHD. This study investigates the feasibility of combining patient-specific, digital 3D heart models with tele-education interventions to improve the medical knowledge of adolescents with CHD.
Adolescent patients with CHD, aged between 13 and 18 years old, were enrolled and scheduled for a tele-education session. Patient-specific digital 3D heart models were created using images from clinically indicated cardiac magnetic resonance studies. The tele-education session was performed using commercially available, web-conferencing software (Zoom, Zoom Video Communications Inc.) and a customised software (Cardiac Review 3D, Indicated Inc.) incorporating an interactive display of the digital 3D heart model. Medical knowledge was assessed using pre- and post-session questionnaires that were scored by independent reviewers.
Twenty-two adolescents completed the study. The average age of patients was 16 years old (standard deviation 1.5 years) and 56% of patients identified as female. Patients had a variety of cardiac defects, including tetralogy of Fallot, transposition of great arteries, and coarctation of aorta. Post-intervention, adolescents’ medical knowledge of their cardiac defects and cardiac surgeries improved compared to pre-intervention (p < 0.01).
Combining patient-specific, digital 3D heart models with tele-education sessions can improve adolescents’ medical knowledge and may assist with transition to adult-centred care.
A novel paediatric disease, multi-system inflammatory syndrome in children, has emerged during the 2019 coronavirus disease pandemic.
To describe the short-term evolution of cardiac complications and associated risk factors in patients with multi-system inflammatory syndrome in children.
Retrospective single-centre study of confirmed multi-system inflammatory syndrome in children treated from 29 March, 2020 to 1 September, 2020. Cardiac complications during the acute phase were defined as decreased systolic function, coronary artery abnormalities, pericardial effusion, or mitral and/or tricuspid valve regurgitation. Patients with or without cardiac complications were compared with chi-square, Fisher’s exact, and Wilcoxon rank sum.
Thirty-nine children with median (interquartile range) age 7.8 (3.6–12.7) years were included. Nineteen (49%) patients developed cardiac complications including systolic dysfunction (33%), valvular regurgitation (31%), coronary artery abnormalities (18%), and pericardial effusion (5%). At the time of the most recent follow-up, at a median (interquartile range) of 49 (26–61) days, cardiac complications resolved in 16/19 (84%) patients. Two patients had persistent mild systolic dysfunction and one patient had persistent coronary artery abnormality. Children with cardiac complications were more likely to have higher N-terminal B-type natriuretic peptide (p = 0.01), higher white blood cell count (p = 0.01), higher neutrophil count (p = 0.02), severe lymphopenia (p = 0.05), use of milrinone (p = 0.03), and intensive care requirement (p = 0.04).
Patients with multi-system inflammatory syndrome in children had a high rate of cardiac complications in the acute phase, with associated inflammatory markers. Although cardiac complications resolved in 84% of patients, further long-term studies are needed to assess if the cardiac abnormalities (transient or persistent) are associated with major cardiac events.
The Chronos 14Carbon-Cycle Facility is a new radiocarbon laboratory at the University of New South Wales, Australia. Built around an Ionplus 200 kV MIni-CArbon DAting System (MICADAS) Accelerator Mass Spectrometer (AMS) installed in October 2019, the facility was established to address major challenges in the Earth, Environmental and Archaeological sciences. Here we report an overview of the Chronos facility, the pretreatment methods currently employed (bones, carbonates, peat, pollen, charcoal, and wood) and results of radiocarbon and stable isotope measurements undertaken on a wide range of sample types. Measurements on international standards, known-age and blank samples demonstrate the facility is capable of measuring 14C samples from the Anthropocene back to nearly 50,000 years ago. Future work will focus on improving our understanding of the Earth system and managing resources in a future warmer world.
Glyphosate-resistant (GR) Palmer amaranth is one of the most difficult to control weeds in soybean production fields in Nebraska and the United States. An integrated approach is required for effective management of GR Palmer amaranth. Cultural practices such as narrow row spacing might augment herbicide efficacy for management of GR Palmer amaranth. The objectives of this study were to evaluate the effect of row spacing and herbicide programs for management of GR Palmer amaranth in dicamba/glyphosate-resistant (DGR) soybean. Field experiments were conducted in a grower’s field with a uniform population of GR Palmer amaranth near Carleton, Nebraska, in 2018 and 2019. Year-by-herbicide program-by-row spacing interactions were significant for all variables; therefore, data were analyzed by year. Herbicides applied PRE controlled GR Palmer amaranth ≥95% in both years 14 d after PRE (DAPRE). Across soybean row-spacing, most PRE followed by (fb) early-POST (EPOST) herbicide programs provided 84% to 97% control of Palmer amaranth compared with most EPOST fb late-post (LPOST) programs, excluding dicamba in single and sequential applications (82% to 95% control). Mixing microencapsulated acetochlor with a POST herbicide in PRE fb EPOST herbicide programs controlled Palmer amaranth ≥93% 14 d after EPOST and ≥96% 21 d after LPOST with no effect on Palmer amaranth density. Interaction of herbicide program-by-row spacing on Palmer amaranth control was not significant; however, biomass reduction was significant at soybean harvest in 2019. The herbicide programs evaluated in this study caused no soybean injury. Due to drought conditions during a majority of the 2018 growing season, soybean yield in 2018 was reduced compared with 2019.
New material attributable to Deltasuchus motherali, a neosuchian from the Cenomanian of Texas, provides sampling across much of the ontogeny of this species. Detailed descriptions provide information about the paleobiology of this species, particularly with regards to how growth and development affected diet. Overall snout shape became progressively wider and more robust with age, suggesting that dietary shifts from juvenile to adult were not only a matter of size change, but of functional performance as well. These newly described elements provide additional characters upon which to base more robust phylogenetic analyses. The authors provide a revised diagnosis of this species, describing the new material and discussing incidents of apparent ontogenetic variation across the sampled population. The results of the ensuing phylogenetic analyses both situate Deltasuchus within an endemic clade of Appalachian crocodyliforms, separate and diagnosable from goniopholidids and pholidosaurs, herein referred to as Paluxysuchidae. This title is also available as Open Access on Cambridge Core.
To assess the relationship between food insecurity, sleep quality, and days with mental and physical health issues among college students.
An online survey was administered. Food insecurity was assessed using the ten-item Adult Food Security Survey Module. Sleep was measured using the nineteen-item Pittsburgh Sleep Quality Index (PSQI). Mental health and physical health were measured using three items from the Healthy Days Core Module. Multivariate logistic regression was conducted to assess the relationship between food insecurity, sleep quality, and days with poor mental and physical health.
Twenty-two higher education institutions.
College students (n 17 686) enrolled at one of twenty-two participating universities.
Compared with food-secure students, those classified as food insecure (43·4 %) had higher PSQI scores indicating poorer sleep quality (P < 0·0001) and reported more days with poor mental (P < 0·0001) and physical (P < 0·0001) health as well as days when mental and physical health prevented them from completing daily activities (P < 0·0001). Food-insecure students had higher adjusted odds of having poor sleep quality (adjusted OR (AOR): 1·13; 95 % CI 1·12, 1·14), days with poor physical health (AOR: 1·01; 95 % CI 1·01, 1·02), days with poor mental health (AOR: 1·03; 95 % CI 1·02, 1·03) and days when poor mental or physical health prevented them from completing daily activities (AOR: 1·03; 95 % CI 1·02, 1·04).
College students report high food insecurity which is associated with poor mental and physical health, and sleep quality. Multi-level policy changes and campus wellness programmes are needed to prevent food insecurity and improve student health-related outcomes.
Without a robust evidence base to support recommendations for medical services at mass gatherings (MGs), levels of care will continue to vary and preventable morbidity and mortality will exist. Accordingly, researchers and clinicians publish case reports and case series to capture and explain some of the health interventions, health outcomes, and host community impacts of MGs. Streamlining and standardizing post-event reporting for MG medical services and associated health outcomes could improve inter-event comparability, thereby supporting and promoting growth of the evidence base for this discipline. The present paper is focused on theory building, proposing a set of domains for data that may support increasingly comprehensive, yet lean, reporting on the health outcomes of MGs. This paper is paired with another presenting a proposal for a post-event reporting template.
The conceptual categories of data presented are based on a textual analysis of 54 published post-event medical case reports and a comparison of the features of published data models for MG health outcomes.
A comparison of existing data models illustrates that none of the models are explicitly informed by a conceptual lens. Based on an analysis of the literature reviewed, four data domains emerged. These included: (i) the Event Domain, (ii) the Hazard and Risk Domain, (iii) the Capacity Domain, and (iv) the Clinical Domain. These domains mapped to 16 sub-domains.
Data modelling for the health outcomes related to MGs is currently in its infancy. The proposed illustration is a set of operationally relevant data domains that apply equally to small, medium, and large-sized events. Further development of these domains could move the MG community forward and shift post-event health outcomes reporting in the direction of increasing consistency and comprehensiveness.
Currently, data collection and analysis related to understanding health outcomes arising from MGs is not informed by robust conceptual models. This paper is part of a series of nested papers focused on the future state of post-event medical reporting.
Case reports are commonly used to report the health outcomes of mass gatherings (MGs), and many published reports of MGs demonstrate substantial heterogeneity of included descriptors. As such, it is challenging to perform rigorous comparisons of health services and outcomes between similar and dissimilar events. The degree of variation in published reports has not yet been investigated.
Examine patterns of post-event medical reporting in the existing literature and identify inconsistencies in reporting.
A systematic review of case reports was conducted. Included were English studies, published between January 2009 and December 2018, in Prehospital and Disaster Medicine (PDM) or Current Sports Medicine Reports (CSMR). Analysis of each paper was used to develop a list of 27 categories of data.
Seventy-five studies were initially reviewed with 54 publications meeting the inclusion criteria. Forty-two were full case reports (78%) and 12 were conference proceedings (22%). Of the 27 categories of data studied, only 13 were consistently reported in more than 50% of publications. Reporting patterns included inconsistent use of terminology/language and variable retrievability of reports. Reporting on event descriptors, hazard and risk analysis, and clinical outcomes were also inconsistent.
Case reports are essential tools for researchers and event team members such as medical directors and event producers. The authors found that current case reports, in addition to being inconsistent in content, were generally descriptive rather than explanatory; that is, focused on describing the outcomes as opposed to exploring possible connections between context and health outcomes.
This paper quantifies and demonstrates the current state of heterogeneity in MG event reporting. This heterogeneity is a significant impediment to the functional use of published reports to further the science of MG planning and to improve health outcomes. Future work based on the insights gained from this analysis will aim to align and standardize reporting to improve the quality and value of event reporting.
Standardizing and systematizing the reporting of health outcomes from mass gatherings (MGs) will improve the quality of data being reported. Setting minimum standards for case reporting is an important strategy for improving data quality. This paper is one of a series of papers focused on understanding the current state, and shaping the future state, of post-event case reporting.
Multiple data sources were used in creating a lean, yet comprehensive list of essential reporting fields, including a: (1) literature synthesis drawn from analysis of 54 post-event case reports; (2) comparison of existing data models for MGs; (3) qualitative analysis of gaps in current case reports; and (4) set of data domains developed based on the preceding sources.
Existing literature fails to consistently report variables that may be essential for not only describing the health outcomes of a given event, but also for explaining those outcomes. In the context of current and future state reporting, 25 essential variables were identified. The essential variables were organized according to four domains, including: (i) Event Domain; (ii) Hazard and Risk Domain; (iii) Capacity Domain; and (iv) Clinical Domain.
The authors propose a first-generation template for post-event medical reporting. This template standardizes the reporting of 25 essential variables. An accompanying data dictionary provides background and standardization for each of the essential variables. Of note, this template is lean and will develop over time, with input from the international MG community. In the future, additional groups of variables may be helpful as “overlays,” depending on the event category and type.
This paper presents a template for post-event medical reporting. It is hoped that consistent reporting of essential variables will improve both data collection and the ability to make comparisons between events so that the science underpinning MG health can continue to advance.
This SHEA white paper identifies knowledge gaps and challenges in healthcare epidemiology research related to coronavirus disease 2019 (COVID-19) with a focus on core principles of healthcare epidemiology. These gaps, revealed during the worst phases of the COVID-19 pandemic, are described in 10 sections: epidemiology, outbreak investigation, surveillance, isolation precaution practices, personal protective equipment (PPE), environmental contamination and disinfection, drug and supply shortages, antimicrobial stewardship, healthcare personnel (HCP) occupational safety, and return to work policies. Each section highlights three critical healthcare epidemiology research questions with detailed description provided in supplementary materials. This research agenda calls for translational studies from laboratory-based basic science research to well-designed, large-scale studies and health outcomes research. Research gaps and challenges related to nursing homes and social disparities are included. Collaborations across various disciplines, expertise and across diverse geographic locations will be critical.