bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deciphering how early life adiposity influences breast cancer risk using Mendelian randomization
Authors: Vabistsevits Marina , Smith George Davey , Sanderson Eleanor , Richardson Tom G , Lloyd-Lewis Bethan , Richmond Rebecca C 
Published: June 2021

AbstractStudies suggest that adiposity in childhood may reduce the risk of breast cancer in later life. The biological mechanism underlying this effect is unclear but is likely to be independent of body size in adulthood. Using a Mendelian randomization framework, we investigated 18 hypothesised mediators of the protective effect of childhood adiposity on later-life breast cancer, including hormonal, reproductive, physical, and glycaemic traits.Our results indicate that, while most of the hypothesised mediators are affected by childhood body size, only IGF-1, testosterone, age at menarche and age at menopause influenced breast cancer risk. However, accounting for those traits in multivariable Mendelian randomization showed that the protective effect of childhood body size still remained. This suggests either a direct effect of childhood body size on breast cancer risk or mediation via other pathways not considered.Our work presents a framework for the systematic exploration of potential biological mediators of disease in Mendelian randomization analysis.
Hospital profiling using Bayesian decision theory
Authors: Hengelbrock Johannes , Rauh Johannes , Cederbaum Jona , Kähler Maximilian , Höhle Michael 
Published: June 2021

AbstractBackgroundFor evaluating the quality of care provided by hospitals, special interest lies in the identification of performance outliers. We study a setting where the decision to classify hospitals as performance outliers or non-outliers is based on the observed result of a single binary quality indicator.MethodsWe propose to embed the classification of providers into a Bayesian decision theoretical framework which enables the derivation of optimal decision rules with respect to the expected decision consequences. We argue that these consequences depend upon for which pathway to quality improvement the profiling of hospitals takes place. We propose paradigmatic utility functions for the two pathways external reporting and change in care delivery and compare the resulting optimal decision rules with regard to their threshold values, sensitivity and specificity. We further apply them to the area of hip replacement surgeries by analyzing data from the mandatory German hospital profiling program. Based on five quality indicators, we re-evaluate the performance of 1,277 hospitals which treated over 180,000 patients for hip-replacement surgeries during 2017.ResultsBased on the utilities we assigned to the classification decisions, the decision rule for change in care delivery classifies more high-volume providers as outliers compared to the decision rule for external reporting, with consequences for both sensitivity and specificity. The re-evaluation of the five quality indicators illustrates that classification decisions are highly dependent upon the underlying utilities.ConclusionAnalyzing the classification of hospitals as a decision theoretic problem and considering pathway-specific consequences of decisions can help to derive an appropriate decision rule. Contrasting decision rules with regard to their underlying assumptions about the utilities of classification consequences can be helpful to make implicit assumptions transparent and justifiable.
Technology in Palliative Care (TIP): the identification of digital priorities for palliative care research using a modified Delphi method
Authors: Nwosu Amara Callistus , McGlinchey Tamsin , Sanders Justin , Stanley Sarah , Palfrey Jennifer , Lubbers Patrick , Chapman Laura , Finucane Anne , Mason Stephen 
Published: June 2021

ABSTRACTBackgroundDevelopments in digital health (describing technologies which use computing platforms, connectivity, software, and sensors for health care and related purposes) has the potential to transform the delivery of health and social care to help citizens manage their own health. Currently, we lack consensus about digital health research priorities in palliative care and lack theories about how these technologies might improve care outcomes. Global palliative care need is expected to increase due to the consequences of an ageing population; therefore, it is important for healthcare leaders to identify innovations to ensure that an increasingly frail population have appropriate access to palliative care services. Consequently, it is important to articulate research priorities as the first step to determine how we should allocate finite resources to a field saturated with rapidly developing innovations.AimsTo identify research priority areas for digital health in palliative care.MethodsWe selected the digital health trends, most relevant to palliative care, from a list of emerging trends reported by the ‘Future Today Institute’. We conducted a modified Delphi process and consensus meeting with palliative care experts to identify research priorities. We used the views of public representatives to gain their perspectives of the agreed priorities.ResultsOne hundred and three experts (representing 11 countries) participated in the 1st Delphi round. Fifty-five participated in the 2nd round (53% of 1st round). Eleven experts attended the final consensus meeting. We identified 16 priorities areas, which were summarised into eight themes. These themes were: big data, mobile devices, telehealth and telemedicine, virtual reality, artificial intelligence, the smart home, biotechnology and digital legacy.ConclusionsThe identified priorities in this paper represent a wide range of important emerging areas in field of digital health, personalised medicine, and data science. Human-centred design and robust governance systems should be considered in future research. It is important that the risks of using these technologies in palliative care are properly addressed to ensure that these tools are used meaningfully, wisely and safely and do not cause unintentional harm.
Projecting the impact of SARS-CoV-2 variants on the COVID-19 epidemic and social restoration in the United States: a mathematical modelling study
Authors: Li Rui , Li Yan , Zou Zhuoru , Liu Yiming , Li Xinghui , Zhuang Guihua , Shen Mingwang , Zhang Lei 
Published: June 2021

AbstractBackgroundThe SARS-CoV-2 Alpha variant B.1.1.7 became prevalent in the United States (US). We aimed to evaluate the impact of vaccination scale-up and potential reduction in the vaccination effectiveness on the COVID-19 epidemic and social restoration in the US.MethodsWe extended a published compartmental model and calibrated the model to the latest US COVID-19 data. We estimated the vaccine effectiveness against B.1.1.7 and evaluated the impact of a potential reduction in vaccine effectiveness on future epidemics. We projected the epidemic trends under different levels of social restoration.ResultsWe estimated the overall existing vaccine effectiveness against B.1.1.7 to be 88.5% (95%CI: 87.4-89.5%) and vaccination coverage would reach 70% by the end of August, 2021. With this vaccine effectiveness and coverage, we anticipated 498,972 (109,998-885,947) cumulative infections and 15,443 (3,828-27,057) deaths nationwide over the next 12 months, of which 95.0% infections and 93.3% deaths were caused by B.1.1.7. Complete social restoration at 70% vaccination coverage would only slightly increase cumulative infections and deaths to 511,159 (110,578-911,740) and 15,739 (3,841-27,638), respectively. However, if the vaccine effectiveness were reduced to 75%, 50% or 25% due to new SARS-CoV-2 variants, we predicted 667,075 (130,682-1,203,468), 1.7m (0.2-3.2m), 19.0m (5.3-32.7m) new infections and 19,249 (4,281-34,217), 42,265 (5,081-79,448), 426,860 (117,229-736,490) cumulative deaths to occur over the next 12 months. Further, social restoration at a lower vaccination coverage would lead to even greater future outbreaks.ConclusionCurrent COVID-19 vaccines remain effective against the B.1.1.7 variant, and 70% vaccination coverage would be sufficient to restore social activities to a pre-pandemic level. Further reduction in vaccine effectiveness against SARS-CoV-2 variants would result in a potential surge of the epidemic in the future.
Defining the Critical Components of Informed Consent for Genetic Testing: A Delphi Study
Authors: Ormond Kelly E. , Borensztein Maia , Hallquist Miranda L.G. , Buchanan Adam H. , Faucett W. Andrew , Peay Holly L. , Smith Maureen E. , Tricou Eric P. , Uhlmann Wendy R. , Wain Karen E. , Coughlin Curtis R. 
Published: June 2021

ABSTRACTPurposeInformed consent for genetic testing has historically happened during pre-test genetic counseling, without specific guidance defining which core concepts are required.MethodsThe Clinical Genome Resource (ClinGen) Consent and Disclosure Recommendations Workgroup (CADRe) used a modified Expert Delphi consensus process to identify the core concepts essential to consent for clinical genetic testing. A literature review identified 77 concepts that are included in informed consent for genetic tests. Twenty five experts (9 medical geneticists, 8 genetic counselors, and 9 bioethicists) completed two rounds of Delphi surveys ranking concepts’ importance to informed consent.ResultsThe most highly ranked concepts included: (1) genetic testing is voluntary; (2) why the test is recommended and what does it test for; (3) what results will be returned and to whom; (4) are there other types of potential results; what choices exist; (5) how will prognosis and management be impacted by results; (6) what is the potential family impact; (7) what are the test limitations and next steps; (8) potential risk for genetic discrimination and legal protections.ConclusionDefining the core concepts necessary for informed consent for genetic testing provides a foundation for quality patient care across a variety of healthcare providers and clinical indications.
Family functioning but not social capital is associated with better mental health in adolescents affected by violence and displacement by armed conflict in Colombia
Authors: Tamayo-Aguledo William , Acosta-Ortiz Alida , Hamid Aseel , Gómez-García Carolina , García-Durán María Camila , Daccach-González Vanessa , Solmi Francesca , Bell Vaughan 
Published: June 2021

AbstractBackgroundThe effect of the Colombian armed conflict on the mental health of adolescents is still poorly understood. Given social interventions are most likely to inform policy, we tested whether two potential intervention targets, family functioning and social capital, were associated with mental health in Colombian adolescents, and whether this was moderated by experience of violence and displacement.MethodsWe examined the cross-sectional association between family functioning, cognitive social capital, structural social capital and 12-month prevalence of Composite International Diagnostic Interview (CIDI) diagnosed psychiatric disorder, using data on 12-17-year-old adolescents (N = 1754) from the 2015 National Mental Health Survey of Colombia, a nationally representative epidemiological study. We tested whether associations survived cumulative adjustment for demographic confounders, experience of non-specific violence and harm, and displacement by armed conflict.ResultsNeither structural nor cognitive social capital were associated with better mental health. Better family functioning was associated with reduced risk of poor mental health in an unadjusted analysis (OR 0.90 [0.85 – 0.96]), and after cumulative adjustments for demographic confounders (OR 0.91 [0.86 – 0.97]), non-specific violence and harm (OR 0.91 [0.86 – 0.97]) and social capital variables (OR 0.91 [0.85 – 0.97]). In the final model, each additional point on the family APGAR scale was associated with a 9% reduced odds of any CIDI diagnosed disorder in the last 12 months.ConclusionsBetter family functioning was associated with better mental health outcomes for all adolescents. This effect remained present in those affected by the armed conflict even after accounting for potential confounders.
Early warning signals predict emergence of COVID-19 waves
Authors: O’Brien Duncan A. , Clements Christopher F. 
Published: June 2021

AbstractEarly warning signals (EWSs) aim to predict changes in complex systems from phenomenological signals in time series data. These signals have recently been shown to precede the initial emergence of disease outbreaks, offering hope that policy makers can make predictive rather than reactive management decisions. Here, using daily COVID-19 case data in combination with a novel, sequential analysis, we show that composite EWSs consisting of variance, autocorrelation, and return rate not only pre-empt the initial emergence of COVID-19 in the UK by 14 to 29 days, but also the following wave six months later. We also predict there is a high likelihood of a third wave as of the data available on 9th June 2021. Our work suggests that in highly monitored disease time series such as COVID-19, EWSs offer the opportunity for policy makers to improve the accuracy of time critical decisions based solely upon surveillance data.
Persistently high proportions of Plasmodium-infected Anopheles funestus mosquitoes in two villages in the Kilombero valley, south-eastern Tanzania
Authors: Mapua Salum A. , Hape Emmanuel E. , Kihonda Japhet , Bwanary Hamis , Kifungo Khamis , Kilalangongono Masoud , Kaindoa Emmanuel W. , Ngowo Halfan S. , Okumu Fredros O. 
Published: June 2021

AbstractBackgroundIn south-eastern Tanzania where insecticide-treated nets have been widely used for more than 20 years, malaria transmission has greatly reduced but remains highly heterogenous over small distances. This study investigated the seasonal prevalence of Plasmodium sporozoite infections in the two main malaria vector species, Anopheles funestus and Anopheles arabiensis for 34 months, starting January 2018 to November 2020.MethodsAdult mosquitoes were collected using CDC-light traps and Prokopack aspirators inside local houses in Igumbiro and Sululu villages, where earlier surveys had found very high densities of An. funestus. Collected females were sorted by taxa, and the samples examined using ELISA assays for detecting Plasmodium circumsporozoite protein in their salivary glands.ResultsOf 7,859 An. funestus tested, 4.6% (n = 365) were positive for Pf sporozoites in the salivary glands. On the contrary, only 0.4% (n = 9) of the 2,382 An. arabiensis tested were positive. The sporozoite prevalence did not vary significantly between the villages or seasons. Similarly, the proportions of parous females of either species were not significantly different between the two villages (p > 0.05) but was slightly higher in An. funestus (0.50) than in An. arabiensis (0.42). Analysis of the 2020 data determined that An. funestus contributed 97.7% of all malaria transmitted in households in these two villages.ConclusionsIn contexts where individual vector species mediate most of the pathogen transmission, it may be most appropriate to pursue a species-specific approach to better understand the ecology of the dominant vectors and target them with effective interventions to suppress transmission. Despite the widespread use and overall impact of insecticide-treated nets over the past two decades in the two study villages, there is still persistently high Plasmodium infection prevalence in local populations of An. funestus, which now carry ∼97% of all malaria infections and mediates intense year-round transmission. Further reduction in malaria burden in these or other similar settings requires effective targeting of An. funestus.
Conditionality of COVID-19 vaccine acceptance in European countries
Authors: Heyerdahl Leonardo W , Vray Muriel , Lana Benedetta , Tvardik Nastassia , Gobat Nina , Wanat Marta , Tonkin-Crine Sarah , Anthierens Sibyl , Goossens Herman , Giles-Vernick Tamara 
Published: June 2021

AbstractThe COVID-19 vaccine rollout in recent months offers a powerful preventive measure that may help control SARS-CoV-2 transmission. Nevertheless, long-standing public hesitation around vaccines has heightened public health concerns that vaccine coverage may not achieve desired public health impacts.This cross-sectional survey was conducted online in December 2020 among 7000 respondents (aged 18 to 65) in Belgium, France, Germany, Italy, Spain, Sweden, and Ukraine. The survey included open text boxes for fuller explanation of responses. Projected COVID-19 vaccine coverage varied and may not be sufficiently high among certain populations to achieve herd immunity. Overall, 56.9% would accept a COVID-19 vaccine, 19.0% would not, and 24.1% did not know or preferred not to say. By country, between 44% (France) and 66% (Italy) of respondents would accept a COVID-19 vaccine. Respondents expressed conditionality in open responses, voicing concerns about vaccine safety and mistrust of authorities. Public health campaigns must tackle these safety concerns.HighlightsMixed-method survey studied expected COVID vaccine uptake in 7 European countries.Projected COVID vaccine acceptance by country ranged from 44% to 66%.Explicit COVID vaccine acceptance or rejection was conditional.Study finds concerns about vaccine safety and authorities’ competence and honesty.Vaccine communications should address safety anxieties and target specific groups.
Behavioral and psychological symptoms of dementia in different dementia disorders: A large-scale study of 10 000 individuals
Authors: Schwertner Emilia , Pereira Joana B. , Xu Hong , Secnik Juraj , Winblad Bengt , Eriksdotter Maria , Nagga Katarina , Religa Dorota 
Published: June 2021

AbstractBackgroundThe majority of individuals with dementia will suffer from psychological and behavioral symptoms of dementia (BPSD). There is limited number of studies investigating BPSD in different types of dementia.ObjectivesTo characterize BPSD in Alzheimer’s disease (AD), vascular dementia (VaD), mixed (Mixed) dementia, Parkinson’s disease dementia (PDD), dementia with Lewy bodies (DLB), frontotemporal dementia (FTD) and unspecified dementia (UNS) in individuals residing in long-term care facilities.MethodsWe included 10,405 individuals with dementia living in long-term care facilities from the Swedish registry for cognitive/dementia disorders (SveDem) and the Swedish BPSD registry. BPSD was assessed with the Neuropsychiatric Inventory - Nursing Home Version (NPI-NH). Multivariate logistic regression models were used to evaluate the associations between dementia diagnoses and different BPSDs.ResultsThe most common symptoms were aberrant motor behavior, agitation and irritability. Compared to AD, we found a lower risk of delusions (in FTD, unspecified dementia), hallucinations (in FTD), agitation (VaD, in PDD, unspecified dementia), elation/euphoria (in DLB), anxiety (Mixed, VaD, unspecified dementia), disinhibition (in PDD); irritability (in DLB, FTD, unspecified dementia), aberrant motor behavior (in Mixed, VaD, unspecified dementia), sleep and night-time behavior changes (in unspecified dementia). Higher risk of delusions (in DLB), hallucinations (in DLB, PDD), apathy (VaD, FTD), disinhibition (in FTD) and appetite and eating abnormalities (in FTD) were also found in comparison to AD.ConclusionsBPSD was common in all types of dementia, with the most common symptoms being aberrant motor behavior, agitation, and irritability. Additionally, presentation of BPSD differ in different dementia types.
Fusion of Fully Integrated Analog Machine Learning Classifier with Electronic Medical Records for Real-time Prediction of Sepsis Onset
Authors: Sadasivuni Sudarsan , Saha Monjoy , Bhatia Neal , Banerjee Imon , Sanyal Arindam 
Published: June 2021

ABSTRACTThe objective of this work is to develop a fusion artificial intelligence (AI) model that combines patient electronic medical record (EMR) and physiological sensor data to accurately predict early risk of sepsis. The fusion AI model has two components - an on-chip AI model that continuously analyzes patient electrocardiogram (ECG) data and a cloud AI model that combines EMR and prediction scores from on-chip AI model to predict fusion sepsis onset score. The on-chip AI model is designed using analog circuits for sepsis prediction with high energy efficiency for integration with resource constrained wearable device. Combination of EMR and sensor physiological data improves prediction performance compared to EMR or physiological data alone, and the late fusion model has an accuracy of 93% in predicting sepsis 4 hours before onset. The key differentiation of this work over existing sepsis prediction literature is the use of single modality patient vital (ECG) and simple demographic information, instead of comprehensive laboratory test results and multiple vital signs. Such simple configuration and high accuracy makes our solution favorable for real-time, at-home use for self-monitoring.
Increased LPS levels coexist with systemic inflammation and result in monocyte activation in severe COVID-19 patients
Authors: Teixeira Paula C , Dorneles Gilson P , Filho Paulo C Santana , Silva Igor M da , Schipper Lucas L , Postiga Isabelle AL , Moreira Neves Carla Andretta , Rodrigues Júnior Luiz Carlos , Peres Alessandra , de Souto Janeusa Trindade , da Fonseca Simone Gonçalves , Eller Sarah , Oliveira Tiago F , Rotta Liane N. , Thompson Claudia , Romão Pedro R T 
Published: June 2021

AbstractThis study aimed to evaluate the link between microbial translocation markers and systemic inflammation at the earliest time-point after hospitalization and at the last 72 h of hospitalization in survivors and non-survivors COVID-19 patients. Sixty-six SARS-CoV-2 RT-PCR+ infected patients and nine non-COVID-19 pneumonia controls were admitted in this study. Blood samples were collected at hospital admission (T1) (Controls and COVID-19+ patients) and 0-72 h before hospital discharge (T2, alive or dead) to analyze systemic cytokines and chemokines, LPS concentrations and soluble CD14 (sCD14) levels. THP-1 human monocytic cell line was incubated with plasma from survivors and non-survivors COVID-19 patients and their phenotype, activation status, TLR4, and chemokine receptors were analyzed by flow cytometry. COVID-19 patients presented higher IL-6, IFN-γ, TNF-α, TGF-β1, CCL2/MCP-1, CCL4/MIP-1β, and CCL5/RANTES levels than controls. Moreover, LPS and sCD14 were higher at hospital admission in SARS-CoV-2-infected patients. Non-survivors COVID-19 patients had increased LPS levels concomitant with higher IL-6, TNF-α, CCL2/MCP-1, and CCL5/RANTES levels at T2. Increased expression of CD16 and CCR5 were identified in THP-1 cells incubated with the plasma of survivor patients obtained at T2. The incubation of THP-1 with T2 plasma of non-survivors COVID-19 leads to higher TLR4, CCR2, CCR5, CCR7, and CD69 expression. In conclusion, increased microbial translocation during hospitalization coexist with the inflammatory condition of SARS-CoV-2 infection and could lead to higher monocyte activation in non-survivors COVID-19 patients.