bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experimental Device to Evaluate Aerosol Dispersion in Venues
Authors: Schulz Isabell , Hehnen Felix , Lausch Kevin Harry , Geisler Sophia Mareike , Hasirci Ümit , Wolff Sophia , Rese Tim , Schimek Sebastian , Lommel Michael , Paschereit Christian Oliver , Moritz Stefan , Kriegel Martin , Kertzscher Ulrich 
Published: June 2023

AbstractThe COVID-19 pandemic has focused attention to the importance of understanding and mitigating the airborne transmission of pathogens in indoor environments. This study investigated the aerosol distribution in different indoor venues with varying ventilation concepts, including displacement, mixed, and natural ventilation. A measurement system was developed to investigate venue-specific aerosol distribution patterns using a sodium chloride solution as a tracer. To analyse the spatial dispersion of aerosols, Computational Fluid Dynamics (CFD) simulations were conducted in addition to experimental investigations. The investigations indicated the lowest aerosol load for the venue with displacement ventilation and the highest for the natural ventilated venue. The measurement system developed in this study provides a useful tool for assessing the effectiveness of ventilation measures in reducing airborne transmission of pathogens in indoor environments. It also proved its wide range of applications, as it can be used in various sized and shaped indoor environments, with or without an audience.
Lockdowns and Selection Pressure: A Modelling Study
Authors: Thompson James , Wattam Stephen 
Published: June 2023

AbstractSince COVID-19 was first identified in China in 2019, the SARS-CoV-2 virus has mutated and given rise to a large number of variants. A range of non-pharmaceutical interventions have been deployed against the virus in an effort to save lives and reduce pressure on health-care systems. These interventions may have favoured some variants over others. Despite this possibility, there has thus far been very little work investigating the impact of such interventions on the evolution of the virus.Using mathematical and computational models, we investigate the impact of a lockdown specifically, for the case in which two SARS-CoV-2 variants are circulating simultaneously in a population. We find that under certain conditions, lockdowns disrupt the competition between variants in such a way that highly transmissible variants with long infectious periods are selected for, ultimately leading tomorecases overall than would have occurred without the lockdown, due to larger second waves of cases.These are the results of a modelling study and we do not claim to have found evidence of such unfavourable selection effects occurring in reality. On the other hand, our results are consistent with evolutionary theory and suggest that the selective effects of non-pharmaceutical interventions deserve greater scrutiny.
Progressive lesion necrosis is related to increasing aphasia severity in chronic stroke
Authors: Johnson Lisa , Newman-Norlund Roger , Teghipco Alex , Rorden Chris , Bonilha Leonardo , Fridriksson Julius 
Published: June 2023

AbstractBackgroundVolumetric investigations of cortical damage resulting from stroke indicate that lesion size and shape continue to change even in the chronic stage of recovery. However, the potential clinical relevance of continued lesion growth has yet to be examined. In the present study, we investigated the prevalence of lesion expansion and the relationship between expansion and changes in aphasia severity in a large sample of individuals in the chronic stage of aphasia recovery.MethodsRetrospective structural MRI scans from 104 stroke survivors with at least 2 observations more than 6 months apart (k=301 observations) were included. Lesion demarcation was performed using an automated lesion segmentation software and lesion volumes at each timepoint were subsequently calculated. A paired-sample t-test between initial and final scans was conducted to determine if chronic lesion volumes change over time. Finally, we investigated the association between lesion expansion and changes on the Western Aphasia Battery (WAB) in a group of participants assessed and scanned at 2 timepoints (N=54) using a GLM.ResultsMost participants (81%) showed evidence of lesion expansion. A t-test revealed lesion volumes at follow-up scans were significantly larger than initial scans (p<0.0001).Change on the language performance was significantly associated with change in lesion volume (p=0.025) and age at stroke (p=0.031). The results suggest that with every 100cm3increase in lesion size, language performance decreases by 8.8 points, and for every 10-year increase in age at stroke, language performance decreases by 1.9 points.ConclusionsThe present study confirms and extends prior reports that lesion expansion occurs well into the chronic stage of stroke. For the first time, we present evidence that expansion is predictive of longitudinal changes in language performance in individuals with aphasia. Future research should focus on the potential mechanisms that may lead to necrosis in areas surrounding the chronic stroke lesion.
Comparison of CT-Derived Plaque Characteristic Index with CMR Perfusion for Ischemia Diagnosis in Stable CAD
Authors: Guo Wei-feng , Xu Hai-jia , Lu Yi-ge , Qiao Guan-yu , Yang Shan , Zhao Shi-hai , Jin Hang , Dai Neng , Yao Zhi-feng , Yin Jia-sheng , Li Chen-guang , He Wei , Zeng Mengsu 
Published: June 2023

AbstractBackgroundCoronary CT angiography (CCTA) and cardiac magnetic resonance (CMR) have been used to diagnose lesion-specific ischemia in patients with coronary artery disease (CAD).ObjectiveThe aim of this study was to investigate the diagnostic performance of CCTA-derived plaque characteristic index compared with myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) derived from CMR perfusion in the assessment of lesion-specific ischemia.MethodsBetween October 2020 and March 2022, consecutive patients with suspected or known CAD, who were clinically referred for ICA were prospectively enrolled. All participants sequentially underwent CCTA and CMR and invasive fractional flow reserve (FFR) within 2 weeks. The diagnostic performance of CCTA-derived plaque characteristics, CMR perfusion-derived stress MBF, and MPR were compared. Lesions with FFR ≤ 0.80 were considered to be hemodynamically significant stenosis.ResultsNighty-two patients with 141 vessels were included in this study. Plaque length, minimum luminal area, plaque area, percent area stenosis, total atheroma volume, vessel volume, lipid rich volume, spotty calcium, napkin-ring signs, stress MBF and MPR in flow-limiting stenosis group were significantly different from non-flow limiting group. The overall accuracy, sensitivity, specificity, PPV, and NPV of lesion-specific ischemia diagnosis were 61.0%, 55.3%, 63.1%, 35.6%, 79.3% for stress MBF, and 89.4%, 89.5%, 89.3%, 75.6%, 95.8% for MPR, meanwhile 82.3%, 79.0%, 84.5%, 65.2%, 91.6% for CCTA-Derived plaque characteristic index.ConclusionIn our prospective study, CCTA-derived plaque characteristics and MPR derived from CMR performed well in diagnosing lesion-specific myocardial ischemia, and were significantly better than stress MBF in stable coronary artery disease.
Food insecurity, adolescent suicidal thoughts and behaviours, and country-level context: a multi-country cross-sectional analysis
Authors: Steare Thomas , Lewis Gemma , Evans-Lacko Sara , Pitman Alexandra , Rose-Clarke Kelly , Patalay Praveetha 
Published: June 2023

AbstractBackgroundPreventing adolescent suicide is a global priority. Inequalities in adolescent suicide and attempt rates are reported across countries, including a greater risk in adolescents experiencing food insecurity. Little is known about the extent to which country-level contextual factors moderate the magnitude of socio-economic inequalities in suicidal thoughts and behaviours. We aimed to examine the cross-country variability and national moderators of the association between food insecurity and suicidal thoughts and behaviours in school-attending adolescents.MethodsWe analysed data on 309,340 school-attending adolescents from 83 countries that participated in the Global School-based Student Health Survey between 2003 and 2018. We used Poisson regression to identify whether suicidal thoughts and behaviours were more prevalent in adolescents experiencing food insecurity compared to food-secure adolescents. Meta-regression and mixed-effects regression were used to determine whether country-level indicators moderated the magnitude of inequality.FindingsSuicidal ideation, suicide planning and suicide attempts were more prevalent in food-insecure adolescents compared to food-secure adolescents in 72%, 78%, and 90% of countries respectively; however, the magnitude of these associations varied between countries. We observed wider inequalities in countries with greater levels of national wealth and universal health coverage and lower prevalence of adolescent food insecurity. Economic inequality had no moderating role.InterpretationFood insecurity could contribute to the development of adolescent suicidal thoughts and behaviours, and this association is likely to be moderated by country-level context. Food insecurity may be a modifiable target to help prevent adolescent suicide, especially in countries where food insecurity is less common.FundingWellcome Trust.
Childhood maltreatment influences adult brain structure through its effects on immune, metabolic and psychosocial factors
Authors: Orellana Sofia C. , Bethlehem Richard A.I. , Simpson-Kent Ivan , van Harmelen Anne-Laura , Vértes Petra E. , Bullmore Edward T. 
Published: June 2023

AbstractChildhood maltreatment (CM) leads to a lifelong susceptibility to mental ill-health which might be reflected by it’s effects on adult brain structure, perhaps indirectly mediated by its effects on adult metabolic, immune, and psychosocial systems. Indexing these systemic factors via body mass index (BMI), C-reactive protein (CRP) and rates of adult trauma (AT), respectively, we tested three hypotheses: (H1) CM has direct or indirect causal effects on adult trauma, BMI and CRP; (H2) adult trauma, BMI and CRP are all independently related to adult brain structure; and (H3) effects of CM on adult brain structure are mediated by its effects on adult trauma, BMI and CRP. Using path analysis and data from N=116,887 participants in UK Biobank we find that CM is related to greater BMI and AT levels, and that only these two variables mediate CM’s effects on CRP [H1]. Regression analyses on the UKB MRI sub-sample (N=21,738) revealed that greater CRP and BMI were both related to a spatially convergent pattern of cortical effects (Spearman’sρ=0.87) characterised by fronto-occipital increases and temporo-parietal reductions in thickness, and that AT is related to lower subcortical volumes [H2]. Finally, path models indicated that CM has indirect effects in a subset of brain regions through its influence on BMI, CRP and AT [H3]. Results provide evidence that childhood maltreatment can influence brain structure decades after exposure by increasing individual risk towards adult trauma, obesity or inflammation.
Identifying gaps in health literacy research through parental participation
Authors: Grepmeier Eva-Maria , Pöhnl Laura , von Sommoggy Julia , Pawellek Maja , Lander Jonas , Schulz Anja Alexandra , Hasenpusch Claudia , Schwalfenberg Anja , Dierks Marie-Luise , Bitzer Eva Maria , Apfelbacher Christian 
Published: June 2023

AbstractIntroductionInvolving patients and the public in design, conduct and dissemination of research has gained momentum in recent years. While methods to prioritize research on treatment uncertainties have been successfully applied for various disease entities, patient and public involvement has not been prominent to prioritize research in health literacy (HL). This study aimed to set up a participatory process on identifying HL research gaps from a parent’s perspective in two use cases: early childhood allergy prevention (ECAP) and COVID-19 in children with allergies (COVICAL).MethodsTo prepare and empower parents, we developed and provided preparatory webinars, introductory materials, i.e., factsheets and a brochure, and a scientific podcast with seven episodes. Recruitment was carried out by our cooperation partner German Allergy and Asthma Association e. V., via local day care centres and paediatricians as well as via snowballing. The identification of research gaps took place within five workshops with n= 55 participants, four face-to-face-workshops across Germany, one online workshop. Research ideas and needs were reviewed for overlap and redundancy and compared to the existing research state of the art.ResultsMore than 150 initial research ideas and needs were collected which after review were reduced to a total of 37 ECAP, 33 COVICAL and 7 generic HL research questions. These were particularly related to the ease of finding and presenting good quality health information, information environment, health communication, professional education, and HL testing.ConclusionsInvolving parents in the formulation of HL research priorities proved to be challenging but feasible. Research ideas often reflect wishes directed at health professionals and the health system, i.e., organizational, and systemic HL. An e Delphi process will follow to elicit the TOP 10 research priorities in each use case. This project will help to plan patient/parent centred HL research in ECAP and COVICAL.Plain Language SummaryWe carried out a participatory HL research exercise to identify HL research gaps from a parent’s perspective. The aim was to learn about parents’/participants’ uncertainty in two use cases ECAP and COVICAL and what issues they would like to be answered by scientists. This resulted in a total of 37 ECAP, 33 COVICAL and 7 generic HL research questions.
AI-MET: A Deep Learning-based Clinical Decision Support System for Distinguishing Multisystem Inflammatory Syndrome in Children from Endemic Typhus
Authors: Bautista-Castillo Abraham , Chun Angela , Vogel Tiphanie P. , Kakadiaris Ioannis A. 
Published: June 2023

AbstractThe COVID-19 pandemic brought several diagnostic challenges, including the post-infectious sequelae multisystem inflammatory syndrome in children (MIS-C). Some of the clinical features of this syndrome can be found in other pathologies such as Kawasaki disease, toxic shock syndrome, and endemic typhus. Endemic typhus, or murine typhus, is an acute infection treated much differently than MIS-C, so early detection is crucial to a favorable prognosis for patients with these disorders. Clinical Decision Support Systems (CDSS) are computer systems designed to support the decision-making of medical teams about their patients and intended to improve uprising clinical challenges in healthcare. In this article, we present a CDSS to distinguish between MIS-C and typhus that includes a scoring system that allows the timely distinction of both pathologies only using clinical and laboratory features typically available within the first six hours of presentation to the Emergency Department (ED). The proposed approach was trained and tested on datasets of 87 typhus patients and 133 MIS-C patients. A comparison was made against five well-known statistical and machine-learning models. A second dataset with 111 MIS-C patients was used to verify the AI-MET effectiveness and robustness. The performance assessment for AI-MET and the five statistical and machine learning models was done by computing Sensitivity, Specificity, Accuracy, and Precision. The AI-MET system scores 100 percent in the five metrics used on the training and testing dataset and 99 percent on the validation dataset.
Covid19Vaxplorer: a free, online, user-friendly COVID-19 Vaccine Allocation Comparison Tool
Authors: Trejo Imelda , Hung Pei-Yao , Matrajt Laura 
Published: June 2023

AbstractBackgroundThere are many COVID-19 vaccines currently available, however, Low- and middle-income countries (LMIC) still have large proportions of their populations unvaccinated. Decision-makers must decide how to effectively allocate available vaccines (e.g. boosters or primary series vaccination, which age groups to target) but LMIC often lack the resources to undergo quantitative analyses of vaccine allocation, resulting in adhoc policies. We developedCovid19Vaxplorer(https://covid19vaxplorer.fredhutch.org/), a free, user-friendly online tool that simulates region-specific COVID-19 epidemics in conjunction with vaccination with the purpose of providing public health officials worldwide with a tool for vaccine allocation planning and comparison.MethodsWe developed an age-structured mathematical model of SARS-CoV-2 transmission and COVID-19 vaccination. The model considers vaccination with up to three different vaccine products, primary series and boosters. We simulated partial immunity derived from waning of natural infection and vaccination. The model is embedded in an online tool,Covid19Vaxplorerthat was optimized for its ease of use. By prompting users to fill information through several windows to input local parameters (e.g. cumulative and current prevalence), epidemiological parameters (e.g basic reproduction number, current social distancing interventions), vaccine parameters (e.g. vaccine efficacy, duration of immunity) and vaccine allocation (both by age groups and by vaccination status).Covid19Vaxplorerconnects the user to the mathematical model and simulates, in real time, region-specific epidemics. The tool then produces key outcomes including expected numbers of deaths, hospitalizations and cases, with the possibility of simulating several scenarios of vaccine allocation at once for a side-by-side comparison.ResultsWe provide two usage examples ofCovid19Vaxplorerfor vaccine allocation in Haiti and Afghanistan, which had as of Spring 2023 2% and 33% of their populations vaccinated, and show that for these particular examples, using available vaccine as primary series vaccinations prevents more deaths than using them as boosters. Covid19Vaxplorer allows users in 183 regions in the world to compare several vaccination strategies simultaneously, adjusting parameters to their local epidemics, infrastructure and logistics.Covid19Vaxploreris an online, free, user-friendly tool that facilitates evidence-based decision making for vaccine distribution.
Stacking multiple prediction models to optimise performance in local settings: exemplars in cardiometabolic disease
Authors: Ghosh Sreejita , Gratton Jasmine , Vermeulen Roel , Asselbergs Folkert , Vlaanderen Jelle J. , Schmidt A. Floriaan 
Published: June 2023

AbstractBackgroundRisk prediction models are used in healthcare settings to tailor therapies to individuals most likely to benefit. Despite appropriate external validation, difference in local characteristics (e.g. patient mix) may attenuate model performance. Prior to any implementation it is therefore advisable to explore local performance, typically requiring a modest amount of historic data. Depending on model performance, model adjustments might be necessary which often require large amounts of data. Here we explore a small sample size approach approximating de novo derivation, by combining model stacking and transfer learning, referred to asstacked transfer learning. As an example we focus on stacking previously trained risk prediction models for cardiovascular disease (CVD), stroke, (chronic) kidney disease, and diabetes.MethodsWe leverage data from the UK biobank to illustrate the benefits of stacking previously trained risk prediction models, predicting the risk of incident CVD, chronic kidney disease (CKD) or diabetes. To mimic sample sizes available in local settings, such as a small to large healthcare trust, we iterated the number of training cases between 10 and 1000. Model stacking was performed using a LASSO penalized logistic regression model, and compared performance of ade novomodel estimating the local association of 33 variables used in the aforementioned risk prediction models.ResultsWe found that stacked models require roughly one-tenths of the training sample size compared to de novo derivation of a prediction model. For example, predicting CVD the stacked model required 30 cases to reach a area under the curve (AUC) value (with 95% CI) of 0.732 (0.728, 0.735), while thede novomodel required 300 cases to reach approximately the same performance. As expected, the absolute performance depended on the predicted outcome, where for example the difference betweende novoand stacked modelling was smaller for CKD prediction.ConclusionWe show that our proposed ”stacked transfer learning” approach closely approximated the predictive performance of ade novomodel, often requiring only a fraction of the data. As such, this approach should be considered when tailoring a model to a local setting.
Quantifiable identification of flow-limited ventilator dyssynchrony with the deformed lung ventilator model
Authors: Agrawal Deepak. K. , Smith Bradford J. , Sottile Peter D. , Hripcsak George , Albers David J. 
Published: June 2023

AbstractObjectiveVentilator dyssynchrony (VD) is potentially harmful to patients with or at risk for acute respiratory distress syndrome (ARDS). In addition to injury solely caused by the ventilator, ventilator-induced lung injury may be instigated and exacerbated by patient respiratory efforts. Automated detection of VD from ventilator waveforms is challenging, and efforts have been made on a human-guided ML algorithm to detect some types of VD. We currently lack a methodological ability to define sub-breath phenotypes of VD that quantify severity anchored to physiologic understanding that could be used to relate VD to damage and guide ventilator management.Materials and MethodsA mathematical model is developed that represents the pressure and volume waveform signals of a breath into several pathophysiological temporal features observed in ventilator waveforms and then deformation terms are added corresponding to hypothesized flow-limited (FL) dyssynchronous breaths. Model parameters are estimated at the resolution of a single breath using a deterministic, multivariate, constrained interior-point method to create a parametric representation of breaths. Differential estimates of different FL-VD breaths are used to create severity metrics for FL-VD breaths and their associations with the ventilator settings and healthcare interventions are analyzed.ResultsA total of 93,007 breaths were analyzed from the raw ventilator waveform dataset of 13 intensive care unit patients who met inclusion criteria. A quantitative method was developed to determine the continuously varying FL-VD severity for each breath and was successfully applied to a cohort of patient-ventilator waveform data. Additionally, cross-validation, using a previously developed ML categorical VD identification algorithm, produced an area under the receiver operator curve of 0.97.Discussion & ConclusionThe VD-deformed lung ventilator (VD-DLV) model accurately detects FL-VD breaths and is able to quantify the severity of patient effort during patient-ventilator interaction. The presence and severity of deviations from normal are modeled in a way that is based on physiological hypotheses of lung damage and ventilator interactions. Therefore, the computed phenotypes have the predictive power to determine how the healthcare variables are associated with FL-VD breaths. This work paves the way for a large-scale study of VD causes and effects by identifying and quantifying VD breaths using the VD-DLV model.