bluuebunny/crossref_metadata_2025_split huggingface dataset:
Framework for the public health assessment of electronic cigarettes
Authors: Banks Emily , Martin Melonie , Harris Miranda 
Published: May 2021

BackgroundElectronic cigarettes (e-cigarettes) are relatively new products with substantial public health impacts. Evidence on their effects is diverse and emerging rapidly, presenting challenges to high-quality policymaking and decision-making. This paper addresses these challenges by developing and presenting a framework for the public health assessment of e-cigarettes, using the Australian context as an example.MethodsFramework development involved stakeholder engagement, development of guiding principles, and consideration of existing relevant frameworks and the evidence requirements of current policy options, identified in published and grey literature.ResultsGuiding principles include the need for the framework to: be evidence based; include consideration of the likely balance of benefits and risks of e-cigarettes, uncertainty and safety; support equity; support the ongoing application of evidence to high-quality policy and practice; and consider potential competing interests. The framework draws upon: health technology assessment; health impact assessment; environmental health risk assessment; healthcare recommendations evidence evaluation; consumer goods regulation; medicine and chemical scheduling; tobacco product evaluation; previous reviews and the precautionary principle. Final framework components are: (1) characterisation of products under consideration; (2) definition of populations of interest; (3) characterisation of tobacco smoking, control and impacts on health and well-being; (4) review of evidence on patterns of e-cigarette use; (5) review of evidence on e-cigarette use and health outcomes; (6) assessment of likely risks, benefits and safety; (7) identification and assessment of policy options to optimise health outcomes.ConclusionsStructured and ongoing public health assessment of e-cigarette use is likely to support health through enhancing evidence-based decision-making.
The simplest wormhole in Rastall and k-essence theories
Authors: Bronnikov Kirill A. , Barcellos Vinícius A. G. , de Carvalho Laura P. , Fabris Júlio C. 
Published: May 2021

AbstractThe geometry of the Ellis–Bronnikov wormhole is implemented in the Rastall and k-essence theories of gravity with a self-interacting scalar field. The form of the scalar field potential is determined in both cases. A stability analysis with respect to spherically symmetric time-dependent perturbations is carried out, and it shows that in k-essence theory the wormhole is unstable, like the original version of this geometry supported by a massless phantom scalar field in general relativity. In Rastall’s theory, it turns out that a perturbative approach reveals the same inconsistency that was found previously for black hole solutions: time-dependent perturbations of the static configuration prove to be excluded by the equations of motion, and the wormhole is, in this sense, stable under spherical perturbations.
Social Determinants of Adherence to COVID-19 Risk Mitigation Measures Among Adults With Cardiovascular Disease
Authors: Hagan Kobina K. , Javed Zulqarnain , Cainzos-Achirica Miguel , Sostman Dirk , Vahidy Farhaan S. , Valero-Elizondo Javier , Acquah Isaac , Yahya Tamer , Kash Bita , Andrieni Julia D. , Dubey Prachi , Hyder Adnan A. , Nasir Khurram 
Published: May 2021

Background:
Social determinants of health (SDOH) may limit the practice of coronavirus disease 2019 (COVID-19) risk mitigation guidelines with health implications for individuals with underlying cardiovascular disease (CVD). Population-based evidence of the association between SDOH and practicing such mitigation strategies in adults with CVD is lacking. We used the National Opinion Research Center’s COVID-19 Household Impact Survey conducted between April and June 2020 to evaluate sociodemographic disparities in adherence to COVID-19 risk mitigation measures in a sample of respondents with underlying CVD representing 18 geographic areas of the United States.

Methods:
CVD status was ascertained by self-reported history of receiving heart disease, heart attack, or stroke diagnosis. We built de novo, a cumulative index of SDOH burden using education, insurance, economic stability, 30-day food security, urbanicity, neighborhood quality, and integration. We described the practice of measures under the broad strategies of personal protection (mask, hand hygiene, and physical distancing), social distancing (avoiding crowds, restaurants, social activities, and high-risk contact), and work flexibility (work from home, canceling/postponing work). We reported prevalence ratios and 95% CIs for the association between SDOH burden (quartiles of cumulative indices) and practicing these measures adjusting for age, sex, race/ethnicity, comorbidity, and interview wave.

Results:

Two thousand thirty-six of 25 269 (7.0%) adults, representing 8.69 million in 18 geographic areas of the United States, reported underlying CVD. Compared with the least SDOH burden, fewer individuals with the greatest SDOH burden practiced all personal protection (75.6% versus 89.0%) and social distancing measures (41.9% versus 58.9%) and had any flexible work schedule (26.2% versus 41.4%). These associations remained statistically significant after full adjustment: personal protection (prevalence ratio, 0.83 [95% CI, 0.73–0.96];
P
=0.009), social distancing (prevalence ratio, 0.69 [95% CI, 0.51–0.94];
P
=0.018), and work flexibility (prevalence ratio, 0.53 [95% CI, 0.36–0.79];
P
=0.002).

Conclusions:
SDOH burden is associated with lower COVID-19 risk mitigation practices in the CVD population. Identifying and prioritizing individuals whose medical vulnerability is compounded by social adversity may optimize emerging preventive efforts, including vaccination guidelines.
Additive Effect of Coexisting Aneurysms Increases Subarachnoid Hemorrhage Risk in Patients With Multiple Aneurysms
Authors: Feng Xin , Tong Xin , Peng Fei , Niu Hao , Qi Peng , Lu Jun , Zhao Yang , Jin Weitao , Wu Zhongxue , Liu He , Zhao Yuanli , Liu Aihua , Wang Daming 
Published: May 2021

Background and Purpose:
It remains unclear whether the additive effect of coexisting intracranial aneurysms increases the risk of subarachnoid hemorrhage (SAH) in patients with multiple intracranial aneurysms. We have performed a tentative analysis for the additive effect of coexisting aneurysms.

Methods:
This multi-institutional cross-sectional study included 1781 aneurysms from 746 patients with multiple intracranial aneurysms. Using the generalized linear mixed model, we analyzed risk factors associated with individual aneurysm rupture and assessed the additive risk of SAH for each patient.

Results:

The coexisting aneurysms number was not significantly associated with individual intracranial aneurysm rupture, both in unadjusted and adjusted analyses. Patient-level analysis found that an increased number of coexisting aneurysms was significantly associated with a greater estimated additive risk (
P
<0.001). Estimated additive risks of patients with 2, 3, and 4 or more coexisting intracranial aneurysms were 25.9%, 31.8%, and 38.1%, respectively, which are comparable to the actual incidence of SAH in those patients (26.6%, 29.5%, and 36.5%, respectively), with a Spearman correlation coefficient of 1.000 (
P
<0.001). Compared with aneurysm-related factors, the estimated additive effect had better discrimination power for SAH risk, with areas under the receiver operating characteristic curve of 0.821.

Conclusions:
We found that a greater number of coexisting aneurysms did not increase rupture risk of individual aneurysms, but the potential additive effect might increase SAH risk in patients with multiple intracranial aneurysms.
An analysis of form and function of a research article between and within publishers and journals
Authors: Nathan Sarah , Haynes Leah , Meyer Jessica , Sumner Josh , Hudson-Vitale Cynthia , McIntosh Leslie D. 
Published: May 2021

Abstract
The identification and subsequent analysis of research articles for machine learning and natural language processing is a complicated task given the lack of consistent article organization principles and heading naming conventions across publishers and journals. Given this, an understanding of how research articles organizationally follow a common function and their use of various heading terms, or forms, is a critical step in applying machine learning techniques for data and information mining across a corpus of articles. To address this need, the authors developed and implemented an article heading form and function analysis across 12 publishers including both research articles and nonresearch articles. Our aim was to (a) identify each of the labeled sections used by research articles, define these sections based on their rhetorical function, and determine frequency of use; (b) within the given data set, determine all of the alternative labels used to identify these sections; and (c) determine whether these sections can be used to consistently determine (1) whether an article is a true research article, or (2) whether an article is not a research article. The results indicated wide variability in the organization of research articles with 24 common sections, known by 186 different names both within and across publishing houses.
Die Handelsgüter der Tarsis-Schiffe (1Kön 10,22): Ein Vergleich zwischen Masoretischem Text und Septuaginta
Authors: Albrecht Felix 
Published: May 2021

Abstract
This article examines 1 Kgs/3 Kgdms 10:22 (par. 2 Chr 9:21) by the comparison of MT and LXX and provides a reconstruction of the Hebrew Vorlage of 3 Kgdms 10:22.
Improving the Analysis and Forecast of Hurricane Dorian (2019) with Simultaneous Assimilation of GOES-16 All-Sky Infrared Brightness Temperatures and Tail Doppler Radar Radial Velocities
Authors: Hartman Christopher M. , Chen Xingchao , Clothiaux Eugene E. , Chan Man-Yau 
Published: May 2021

AbstractRecent studies have shown that the assimilation of all-sky infrared (IR) observations can be beneficial for tropical cyclone analyses and predictions. The assimilation of Tail Doppler Radar (TDR) radial velocity observations has also been shown to improve tropical cyclone analyses and predictions; however, there is a paucity of literature on the impacts of simultaneously assimilating them with all-sky infrared IR brightness temperatures (BTs). This study examines the impacts of assimilating combinations of GOES-16 all-sky IR brightness temperatures, NOAA P-3 TDR radial velocities, and conventional observations from the Global Telecommunications System (GTS) on the analyses and forecasts of Hurricane Dorian (2019). It is shown that including IR and/or TDR observations on top of conventional GTS observations significantly reduces both track and intensity forecast errors. Track errors are reduced the most (25% at lead times greater than 48 h) when TDR and GTS observations are assimilated. In terms of intensity, errors are always lower at lead times greater than 48 h when IR BTs are assimilated. Simultaneously assimilating TDR and IR observations has the potential to further improve the intensity forecast by as much as 37% at a lead time of 48 h to 72 h. The improved intensity forecasts produced by the experiments assimilating all three observation sources are shown to be a result of the competing effects of IR assimilation producing an overly broad area of strong cyclonic circulation and TDR assimilation constraining that circulation to a more realistic size and intensity. Interestingly, the order in which observations are assimilated has non-negligible impacts on the analyses and forecasts of Dorian.
Comprehensive CRISPR-Cas9 screens identify genetic determinants of drug responsiveness in multiple myeloma
Authors: Bohl Stephan R. , Schmalbrock Laura K. , Bauhuf Imke , Meyer Tatjana , Dolnik Anna , Szyska Martin , Blätte Tamara J. , Knödler Sarah , Röhner Linda , Miller Denise , Kull Miriam , Langer Christian , Döhner Hartmut , Letai Anthony , Damm Frederik , Heckl Dirk , Bullinger Lars , Krönke Jan 
Published: May 2021

Abstract
The introduction of new drugs in the past years has substantially improved outcome in multiple myeloma (MM). However, the majority of patients eventually relapse and become resistant to one or multiple drugs. While the genetic landscape of relapsed/ resistant multiple myeloma has been elucidated, the causal relationship between relapse-specific gene mutations and the sensitivity to a given drug in MM has not systematically been evaluated. To determine the functional impact of gene mutations, we performed combined whole-exome sequencing (WES) of longitudinal patient samples with CRISPR-Cas9 drug resistance screens for lenalidomide, bortezomib, dexamethasone, and melphalan. WES of longitudinal samples from 16 MM patients identified a large number of mutations in each patient that were newly acquired or evolved from a small subclone (median 9, range 1-55), including recurrent mutations in TP53, DNAH5, and WSCD2. Focused CRISPR-Cas9 resistance screens against 170 relapse-specific mutations functionally linked 15 of them to drug resistance. These included cereblon E3 ligase complex members for lenalidomide, structural genes PCDHA5 and ANKMY2 for dexamethasone, RB1 and CDK2NC for bortezomib, and TP53 for melphalan. In contrast, inactivation of genes involved in the DNA damage repair pathway, including ATM, FANCA, RAD54B, and BRCC3, enhanced susceptibility to cytotoxic chemotherapy. Resistance patterns were highly drug specific with low overlap and highly correlated with the treatment-dependent clonal evolution in patients. The functional association of specific genetic alterations with drug sensitivity will help to personalize treatment of MM in the future.
Achromobacter xylosoxidansairway infection is associated with lung disease severity in children with cystic fibrosis
Authors: Marsac Charlotte , Berdah Laura , Thouvenin Guillaume , Sermet-Gaudelus Isabelle , Corvol Harriet 
Published: May 2021

BackgroundDespite the increasing prevalence ofAchromobacter xylosoxidanslung infection in patients with cystic fibrosis (CF), its clinical pathogenicity remains controversial. The objective of this study was to evaluate the effects of this emerging bacterium on lung disease severity in CF children.MethodsThis case–control retrospective study took place in two French paediatric CF centres. 45 cases infected byA. xylosoxidanswere matched for age, sex,CFTRgenotypes and pancreatic status to 45 never-infected controls. Clinical data were retrieved from clinical records over the 2 years before and afterA. xylosoxidansinitial infection.ResultsAt infection onset, lung function was lower in cases compared with controls (p=0.006). Over the 2 years prior toA. xylosoxidansacquisition, compared with controls, cases had more frequent pulmonary exacerbations (p=0.02), hospitalisations (p=0.05), and intravenous (p=0.03) and oral (p=0.001) antibiotic courses. In the 2 years followingA. xylosoxidansinfection, cases remained more severe with more frequent pulmonary exacerbations (p=0.0001), hospitalisations (p=0.0001), and intravenous (p=0.0001) and oral antibiotic courses (p=0.0001). Lung function decline tended to be faster in cases (−5.5% per year) compared with controls (−0.5% per year).ConclusionsThis case–control study demonstrates thatA. xylosoxidansoccurs more frequently in the patients with the worse lung disease. Further studies assessing the pathogenicity of this emerging pathogen and international treatment recommendations are warranted.
Emergency medical dispatcher training as a strategy to improve pre-hospital care in low- and middle-income countries: the case study of Nepal
Authors: Jacobson Claire L. , Basnet Samjhana , Bhatt Ajay , Parajuli Shreejesh , Shrestha Sanu Krishna 
Published: May 2021

Abstract
Background
Low- and middle-income countries (LMICs) often face significant challenges related to providing effective pre-hospital care services. Barriers to providing care include lack of financial resources, poor road infrastructure, lack of trained first responders and ambulance staff, and issues regarding coordination/communication between different entities involved in Emergency Medical Services. Prior initiatives to characterize and improve the state of pre-hospital care in LMICs have largely focused on improving access to high-quality ambulance services by providing training programs to community first responders and ambulance staff on how to recognize and manage key emergency conditions. In this article, we discuss an alternative strategy for improving pre-hospital care: the creation of a context-specific Emergency Medical Dispatcher (EMD) training curriculum and program.

Methods
We describe the current pre-hospital care setting in Nepal, the process of creating and piloting the Nepal-specific EMD training manual, and the early impact of its implementation.

Results
The 30-h EMD training was designed, piloted, and revised in collaboration with the three largest EMS organizations in Nepal. The training is now required for all dispatchers at the Dhulikhel Hospital Dispatch Center, one of the largest ambulance dispatch networks in Nepal. Dispatchers are trained in the following knowledge and skill areas: telecommunication guidelines, triaging and documentation procedures, delivery of Basic Life Support instructions to callers, other medical and trauma-condition specific instructions, and limited resource management. The short-term positive impacts of the training’s implementation include improved documentation procedures, better prioritization of ambulance resources, delivery of Basic Life Support instructions to callers, and improved communication between dispatch, responders, and healthcare facilities.

Conclusions
Context-specific Emergency Medical Dispatch training programs, which aim to optimize the emergency resources available in resource-limited settings, present a promising low-cost, high-impact interventional strategy to strengthen the pre-hospital care systems in low- and middle-income countries.
Assessment of trade-offs between feed efficiency, growth-related traits, and immune activity in experimental lines of layer chickens
Authors: Zerjal Tatiana , Härtle Sonja , Gourichon David , Guillory Vanaïque , Bruneau Nicolas , Laloë Denis , Pinard-van der Laan Marie-Hélène , Trapp Sascha , Bed’hom Bertrand , Quéré Pascale 
Published: May 2021

Abstract
Background
In all organisms, life-history traits are constrained by trade-offs, which may represent physiological limitations or be related to energy resource management. To detect trade-offs within a population, one promising approach is the use of artificial selection, because intensive selection on one trait can induce unplanned changes in others. In chickens, the breeding industry has achieved remarkable genetic progress in production and feed efficiency over the last 60 years. However, this may have been accomplished at the expense of other important biological functions, such as immunity. In the present study, we used three experimental lines of layer chicken—two that have been divergently selected for feed efficiency and one that has been selected for increased antibody response to inactivated Newcastle disease virus (ND3)—to explore the impact of improved feed efficiency on animals’ immunocompetence and, vice versa, the impact of improved antibody response on animals’ growth and feed efficiency.

Results
There were detectable differences between the low (R+) and high (R−) feed-efficiency lines with respect to vaccine-specific antibody responses and counts of monocytes, heterophils, and/or T cell population. The ND3 line presented reduced body weight and feed intake compared to the control line. ND3 chickens also demonstrated an improved antibody response against a set of commercial viral vaccines, but lower blood leucocyte counts.

Conclusions
This study demonstrates the value of using experimental chicken lines that are divergently selected for RFI or for a high antibody production, to investigate the modulation of immune parameters in relation to growth and feed efficiency. Our results provide further evidence that long-term selection for the improvement of one trait may have consequences on other important biological functions. Hence, strategies to ensure optimal trade-offs among competing functions will ultimately be required in multi-trait selection programs in livestock.
Application of machine learning in predicting hospital readmissions: a scoping review of the literature
Authors: Huang Yinan , Talwar Ashna , Chatterjee Satabdi , Aparasu Rajender R. 
Published: May 2021

Abstract
Background
Advances in machine learning (ML) provide great opportunities in the prediction of hospital readmission. This review synthesizes the literature on ML methods and their performance for predicting hospital readmission in the US.

Methods
This review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) Statement. The extraction of items was also guided by the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Electronic databases PUBMED, MEDLINE, and EMBASE were systematically searched from January 1, 2015, through December 10, 2019. The articles were imported into COVIDENCE online software for title/abstract screening and full-text eligibility. Observational studies using ML techniques for hospital readmissions among US patients were eligible for inclusion. Articles without a full text available in the English language were excluded. A qualitative synthesis included study characteristics, ML algorithms utilized, and model validation, and quantitative analysis assessed model performance. Model performances in terms of Area Under the Curve (AUC) were analyzed using R software. Quality in Prognosis Studies (QUIPS) tool was used to assess the quality of the reviewed studies.

Results
Of 522 citations reviewed, 43 studies met the inclusion criteria. A majority of the studies used electronic health records (24, 56%), followed by population-based data sources (15, 35%) and administrative claims data (4, 9%). The most common algorithms were tree-based methods (23, 53%), neural network (NN) (14, 33%), regularized logistic regression (12, 28%), and support vector machine (SVM) (10, 23%). Most of these studies (37, 85%) were of high quality. A majority of these studies (28, 65%) reported ML algorithms with an AUC above 0.70. There was a range of variability within AUC reported by these studies with a median of 0.68 (IQR: 0.64–0.76; range: 0.50–0.90).

Conclusions
The ML algorithms involving tree-based methods, NN, regularized logistic regression, and SVM are commonly used to predict hospital readmission in the US. Further research is needed to compare the performance of ML algorithms for hospital readmission prediction.