bluuebunny/crossref_metadata_2025_split huggingface dataset:
Long-term outcomes of psychoactive drug use in trauma patients: A multicenter patient-reported outcomes study
Authors: El Moheb Mohamad , Herrera-Escobar Juan P. , Breen Kerry , Orlas Claudia , Haynes Ashley N. , Levy-Carrick Nomi C. , Nehra Deepika , Sanchez Sabrina E. , Salim Ali , Velmahos George , Kaafarani Haytham M.A. 
Published: December 2020

INTRODUCTION
Psychoactive drug use (PDU) is reported in up to 40% of trauma patients and is associated with a higher rate of in-hospital complications. However, little is known about its long-term impact on trauma patients. We aimed to assess the long-term functional, mental, and psychosocial outcomes of PDU in trauma patients 6 to 12 months after injury.

METHODS
Trauma patients with moderate to severe injuries (Injury Severity Score, >9) who had a toxicology screen upon admission to one of three level 1 trauma centers were contacted by phone 6 to 12 months postinjury. Psychoactive drug use was defined as the presence of a psychoactive, nonprescribed substance on toxicology screen including amphetamine, barbiturate, benzodiazepine, cannabinoid, methamphetamine, methadone, opioid, oxycodone, methylenedioxymethamphetamine (ecstasy), phencyclidine, tricyclic antidepressant, and cocaine. The interviews systematically evaluated functional limitations, social functioning, chronic pain, and mental health (posttraumatic stress disorder, depression, anxiety). Patients with a score of ≤47 on the Short-Form Health Survey version 2.0 social functioning subdomain were considered to have social dysfunction. Multivariable regression models were built to determine the independent association between PDU and long-term outcomes.

RESULTS
Of the 1,699 eligible patients, 571 (34%) were included in the analysis, and 173 (30.3%) screened positive for PDU on admission. Patients with PDU were younger (median age [interquartile range], 43 [28–55] years vs. 66 [46–78] years, p < 0.001), had more penetrating injuries (8.7% vs. 4.3%, p = 0.036), and were less likely to have received a college education (41.3% vs. 54.5%, p = 0.004). After adjusting for patients’ characteristics including the presence of a baseline psychiatric comorbidity, patients with PDU on admission were more likely to suffer from daily chronic pain, mental health disorders, and social dysfunction 6 to 12 months after injury. There was no difference in the functional limitations between patients with and without PDU.

CONCLUSION
On the long term, PDU in trauma patients is strongly and independently associated with worse mental health, more chronic pain, and severe impairment in social functioning. A trauma hospitalization presents an opportunity to identify patients at risk and to mitigate the long-term impact of PDU on recovery.

LEVEL OF EVIDENCE
Prognostic/epidemiologic, level III.
Smart Investment of Virus RNA Testing Resources to Enhance Covid-19 Mitigation
Authors: Gorji Hossein , Arnoldini Markus , Jenny David F. , Hardt Wolf-Dietrich , Jenny Patrick 
Published: December 2020

AbstractCovid-19 mitigation commonly involves contact tracing (CT) and social distancing. Due to its high economic toll and its impact on personal freedom, we need to ease social distancing and deploy alternative measures, while preventing further waves of infections. While reliable mass testing (for virus RNA) would require too many resources to be effective, CT, which focuses on isolating symptomatic cases and their contacts, has been implemented in many countries. However, the latter approach has reduced efficiency when high numbers of positive patients are burdening the tracing centers. Moreover, CT misses transmissions by asymptomatic cases. Therefore, its effect in reducing the reproduction number has a theoretical limit.To improve effectiveness of contact tracing, we propose to complement it with a strategy relying on identifying and testing symptom free subgroups with a significantly higher than average virus prevalence. We call this smart testing (ST). By testing everybody in these subgroups, in addition to symptomatic cases, also large fractions of pre- and asymptomatic persons can be identified, which enhances the effectiveness of contact tracing. High prevalence subgroups can be found in different ways, which are discussed in this paper. A particularly efficient way is via preselection using cheap and fast virus antigen tests, as proposed recently. Mathematical modeling quantifies the potential reduction of the reproduction number by such a two-stage ST strategy. In addition to global scenarios, also more realistic local applications of two-stage ST have been investigated, that is, within counties, institutions, schools, companies, etc., where members have internal as well as external contacts. All involved model parameters have been varied within realistic ranges and results are presented with probabilities. Even with the most pessimistic parameter set, these results suggest that the effect of two-stage ST on the reproduction number would clearly outweigh its economic cost. Two-stage ST is technically and logistically feasible. Further, it is locally effective also when only applied within small local subpopulations. Thereby, two-stage ST efficiently complements the portfolio of mitigation strategies, which allow easing social distancing without compromising public health.Single Sentence SummaryIdentification of high prevalence groups within subpopulations to enhance detection rate of Covid-19 infections by virus RNA tests combined with subsequent isolation.
Sex-specific associations of adiposity with cardiometabolic traits: multi-life-stage cohort study with repeat metabolomics
Authors: O’Keeffe Linda M. , Bell Joshua A. , O’Neill Kate N. , Lee Matthew , Woodward Mark , Peters Sanne , Smith George Davey , Kearney Patricia M. 
Published: December 2020

AbstractBackgroundSex differences in cardiometabolic disease risk are commonly observed across the life course but are poorly understood and may be due to different cardiometabolic consequences of adiposity in females and males. We examined whether adiposity influences cardiometabolic trait levels differently in females and males at four different life stages.MethodsData were from two generations (offspring, Generation 1 [G1] and their parents, Generation 0 [G0]) of the Avon Longitudinal Study of Parents and Children birth cohort study. Body mass index (BMI) and total fat mass from dual-energy x-ray absorptiometry were measured at mean age 9y, 15y and 18y in G1. Waist circumference was measured at 9y and 15y in G1. Concentrations of 148 cardiometabolic traits quantified using nuclear magnetic resonance spectroscopy were measured at 15y, 18y and 25y in G1. In G0, all three adiposity measures and the same 148 traits were available at 50y.Using linear regression models, sex-specific associations of adiposity measures at each time point (9y, 15y and 18y) with cardiometabolic traits 3 to 6 years later were examined in G1. In G0, sex-specific associations of adiposity measures and cardiometabolic traits were examined cross- sectionally at 50y.Results3081 G1 and 4887 G0 participants contributed to analyses. BMI was more strongly associated with key atherogenic traits in males at younger ages (15y-25y) and associations were more similar between the sexes or stronger in females at 50y, particularly for apolipoprotein-B-containing lipoprotein particles and lipid concentrations. For example, a 1- SD (3.8 kg/m2) higher BMI at 18y was associated with 0.36 SD (95% Confidence Interval (CI) = 0.20, 0.52) higher concentrations of extremely large very-low-density lipoprotein (VLDL) particles at 25y in males compared with 0.15 SD (95% CI = 0.09, 0.21) in females. In contrast, at 50y, a 1-SD (4.8 kg/m2) higher BMI was associated with 0.33 SD (95% CI = 0.25, 0.42) and 0.30 SD (95% CI = 0.26, 0.33) higher concentrations of extremely large VLDL particles in males and females respectively. Sex-specific associations of DXA-measured fat mass and waist circumference were similar to findings for BMI in both generations and at all ages.ConclusionThe results of this study suggest that the adverse cardiometabolic effects of adiposity are stronger and begin earlier in the life course among males compared with females until mid life, particularly for key atherogenic lipids. Adolescent and young adult males may therefore be high priority targets for obesity prevention efforts.
Risk assessment for airborne disease transmission by poly-pathogen aerosols
Authors: Nordsiek Freja , Bodenschatz Eberhard , Bagheri Gholamhossein 
Published: December 2020

AbstractIn the case of airborne diseases, pathogen copies are transmitted by droplets of respiratory tract fluid that are exhaled by the infectious that stay suspended in the air for some time and, after partial or full drying, inhaled as aerosols by the susceptible. The risk of infection in indoor environments is typically modelled using the Wells-Riley model or a Wells-Riley-like formulation, usually assuming the pathogen dose follows a Poisson distribution (mono-pathogen assumption). Aerosols that hold more than one pathogen copy, i.e. poly-pathogen aerosols, break this assumption even if the aerosol dose itself follows a Poisson distribution. For the largest aerosols where the number of pathogen in each aerosol can sometimes be several hundred or several thousand, the effect is non-negligible, especially in diseases where the risk of infection per pathogen is high. Here we report on a generalization of the Wells-Riley model and dose-response models for poly-pathogen aerosols by separately modeling each number of pathogen copies per aerosol, while the aerosol dose itself follows a Poisson distribution. This results in a model for computational risk assessment suitable for mono-/poly-pathogen aerosols. We show that the mono-pathogen assumption significantly overestimates the risk of infection for high pathogen concentrations in the respiratory tract fluid. The model also includes the aerosol removal due to filtering by the individuals which becomes significant for poorly ventilated environments with a high density of individuals, and systematically includes the effects of facemasks in the infectious aerosol source and sink terms and dose calculations.
Social network-based strategies for classroom size reduction can help limit outbreaks of SARS-CoV-2 in high schools. A simulation study in classrooms of four European countries
Authors: Kaiser Anna , Kretschmer David , Leszczensky Lars 
Published: December 2020

AbstractDividing classrooms may reduce the risk of SARS-CoV-2 outbreaks in schools. We investigate how classroom cohorting strategies, which downsize and isolate groups, may curb the spread of SARS-CoV-2. Using agent-based modelling based on a rich multi-country network dataset comprising 507 classrooms and 12,291 students, we assess random cohorting and three network-based strategies that consider students’ out-of-school contacts with classmates. Investigating effects on the number of cross-cohort transmissions, overall infections, and quarantines, our findings suggest that all cohorting strategies help to contain outbreaks, but that minimizing out-of-school contact between cohorts is most effective. Since this strategy may be hard to implement in practice, we show that a network chain nomination procedure and splitting classes by gender, both of which are easier to realize, also outperform random cohorting considerably. For all cohorting strategies, we find that rota-systems with instruction in alternating weeks contain outbreaks more effectively than same-day in-person instruction.
Predictive accuracy of computer-aided versions of the on-admission National Early Warning Score in estimating the risk of COVID-19 for unplanned admission to hospital: a retrospective development and validation study
Authors: Faisal Muhammad , Mohammed Mohammed A , Richardson Donald , Steyerberg Ewout W. , Fiori Massimo , Beatson Kevin 
Published: December 2020

AbstractObjectivesTo consider the potential of the National Early Warning Score (NEWS2) for COVID-19 risk prediction on unplanned admission to hospital.DesignLogistic regression model development and validation study using a cohort of unplanned emergency medical admission to hospital.SettingYork Hospital (YH) as model development dataset and Scarborough Hospital (SH) as model validation dataset.ParticipantsUnplanned adult medical admissions discharged over 3 months (11 March 2020 to 13 June 2020) from two hospitals (YH for model development; SH for external model validation) based on admission NEWS2 electronically recorded within ±24 hours of admission. We used logistic regression modelling to predict the risk of COVID-19 using NEWS2 (Model M0’) versus enhanced cNEWS models which included age + sex (model M1’) + subcomponents (including diastolic blood pressure + oxygen flow rate + oxygen scale) of NEWS2 (model M2’). The ICD-10 code ‘U071’ was used to identify COVID-19 admissions. Model performance was evaluated according to discrimination (c statistic), calibration (graphically), and clinical usefulness at NEWS2 ≥5.ResultsThe prevalence of COVID-19 was higher in SH (11.0%=277/2520) than YH (8.7%=343/3924) with higher index NEWS2 (3.2 vs 2.8) but similar in-hospital mortality (8.4% vs 8.2%). The c-statistics for predicting COVID-19 for cNEWS models (M1’,M2’) was substantially better than NEWS2 alone (M0’) in development (M2’: 0.78 (95%CI 0.75-0.80) vs M0’ 0.71 (95%CI 0.68-0.74)) and validation datasets (M2’: 0.72 (95%CI 0.69-0.75) vs M0’ 0.65 (95%CI 0.61-0.68)). Model M2’ had better calibration than Model M0’ with improved sensitivity (M2’: 57% (95%CI 51%-63%) vs M0’ 44% (95%CI 38%-50%)) and similar specificity (M2’: 76% (95%CI 74%-78%) vs M0’ 75% (95%CI 73%-77%)) for validation dataset at NEWS2≥5.ConclusionsModel M2’ is reasonably accurate for predicting the on-admission risk of COVID-19. It may be clinically useful for an early warning system at the time of admission especially to triage large numbers of unplanned hospital admissions.
NEC-Associated DNA Methylation Signatures in Colon are Evident in Stool Samples of Affected Individuals
Authors: Good Misty , Chu Tianjiao , Shaw Patricia , Nolan Lila S. , McClain Lora , Chamberlain Austin , Castro Carlos , Gong Qingqing , Cooksey Krista , Linneman Laura , Finegold David N. , Peters David 
Published: December 2020

AbstractNeonatal necrotizing enterocolitis (NEC) is a devastating and unpredictable gastrointestinal disease with a high mortality rate in premature infants. Currently, no predictive or diagnostic biomarkers exist for NEC. Clinical intervention is reactive to the overt manifestations of disease resulting in high levels of morbidity and mortality. To better understand the molecular mechanisms that underpin NEC, we have undertaken a high resolution genome wide epigenomic analysis using solution phase hybridization and next generation DNA sequencing of bisulfite converted DNA. Our data reveal a broad and significant genomic hypermethylation in surgical NEC tissues compared to non-NEC controls. These changes were found to be far more pronounced in regions outside CpG islands and gene regulatory elements, which suggests that NEC-specific hypermethylation is not a non-specific global phenomenon. We identified a number of important biological pathways that are dysregulated in NEC and observed a clear association between NEC methylation changes and gene expression. Significantly, we found that the same patterns of global methylation identified in surgical NEC tissue are also detectable in stool samples from affected infants. To our knowledge, this is the first evidence of a methylomic signature that is both associated with NEC and detectable non-invasively. These findings point towards a new opportunity for the development of novel screening, diagnostic and phenotyping methods for NEC that could be deployed in the NICU for improved detection of this devastating disease.
Serological prevalence of SARS-CoV-2 infection and associated factors in healthcare workers in a “non-COVID” hospital in Mexico City
Authors: Cruz-Arenas Esteban , Cabrera-Ruiz Elizabeth , Laguna-Barcenas Sara , Colin-Castro Claudia A. , Chavez Tatiana , Franco-Cendejas Rafael , Ibarra Clemente , Perez-Orive Javier 
Published: December 2020

ABSTRACTIn spite of high mortality from COVID-19, in Mexico the number of confirmed cases and diagnostic tests per million population are lower than for other comparable countries, which leads to uncertainty about the actual extent of the pandemic. In Mexico City, healthcare workers represent an important fraction of individuals with SARS-CoV-2 infection. This work aims to estimate the frequency of antibodies to SARS-CoV-2 and identify associated factors in healthcare workers at a large hospital in Mexico City. We conducted a serological survey in a non-COVID national referral teaching hospital. We selected a representative sample of 300 individuals. Blood samples were collected and questionnaires were applied between August 10th and September 9th, 2020. ELISA results indicate a serological prevalence of SARS-CoV-2 infection of 13.0%. Working in the janitorial and security groups, having an educational level below a university degree, and living with a larger number of people, were also identified as sociodemographic factors that increase the risk of having SARS-CoV-2 infection. Thus, less favored socioeconomic groups are at significantly higher risk of experiencing SARS-CoV-2 infection. Even in healthcare workers there is still a majority of individuals that are seronegative, and thus the risk of continued epidemic waves and mortality remains high.
A Healthy Buildings Guideline for the COVID-19 Pandemic and Beyond
Authors: Federspiel Clifford 
Published: December 2020

AbstractPublic health experts have confirmed that airborne transmission of SARS-CoV-2 (COVID-19) is one of the primary mechanisms of infection (CDC, 2020). In addition to social distancing, mask wearing and hand washing, experts now recommend increasing the ventilation and filtration of indoor air. While there is widespread consensus on this general approach, to date there are no published guidelines for the levels of ventilation, filtration, etc. that are required to control the pandemic. This is an urgent concern because colder weather in the Northern Hemisphere has moved social activity indoors where the risk of infection is higher.
Predicting patients with false negative SARS-CoV-2 testing at hospital admission: A retrospective multi-center study
Authors: Ghazi Lama , Simonov Michael , Mansour Sherry , Moledina Dennis , Greenberg Jason , Yamamoto Yu , Biswas Aditya , Wilson F. Perry 
Published: December 2020

AbstractImportanceFalse negative SARS-CoV-2 tests can lead to spread of infection in the inpatient setting to other patients and healthcare workers. However, the population of patients with COVID who are admitted with false negative testing is unstudied.ObjectiveTo characterize and develop a model to predict true SARS-CoV-2 infection among patients who initially test negative for COVID by PCR.DesignRetrospective cohort study.SettingFive hospitals within the Yale New Haven Health System between 3/10/2020 and 9/1/2020. Participants: Adult patients who received diagnostic testing for SARS-CoV-2 virus within the first 96 hours of hospitalization.ExposureWe developed a logistic regression model from readily available electronic health record data to predict SARS-CoV-2 positivity in patients who were positive for COVID and those who were negative and never retested.Main Outcomes and MeasuresThis model was applied to patients testing negative for SARS-CoV-2 who were retested within the first 96 hours of hospitalization. We evaluated the ability of the model to discriminate between patients who would subsequently retest negative and those who would subsequently retest positive.ResultsWe included 31,459 hospitalized adult patients; 2,666 of these patients tested positive for COVID and 3,511 initially tested negative for COVID and were retested. Of the patients who were retested, 61 (1.7%) had a subsequent positive COVID test. The model showed that higher age, vital sign abnormalities, and lower white blood cell count served as strong predictors for COVID positivity in these patients. The model had moderate performance to predict which patients would retest positive with a test set area under the receiver-operator characteristic (ROC) of 0.76 (95% CI 0.70 - 0.83). Using a cutpoint for our risk prediction model at the 90th percentile for probability, we were able to capture 35/61 (57%) of the patients who would retest positive. This cutpoint amounts to a number-needed-to-retest range between 15 and 77 patients.Conclusion and RelevanceWe show that a pragmatic model can predict which patients should be retested for COVID. Further research is required to determine if this risk model can be applied prospectively in hospitalized patients to prevent the spread of SARS-CoV-2 infections.
InsectBrainDatabase - A unified platform to manage, share, and archive morphological and functional data
Authors: Heinze Stanley , el Jundi Basil , Berg Bente G. , Homberg Uwe , Menzel Randolf , Pfeiffer Keram , Dacke Marie , Warrant Eric , Pfuhl Gerit , Rybak Jürgen , Tedore Kevin 
Published: December 2020

AbstractInsect neuroscience generates vast amounts of highly diverse data, of which only a small fraction are findable, accessible and reusable, despite open data mandates by funding bodies. We have therefore developed the InsectBrainDatabase (IBdb), an open platform for depositing, sharing and managing a wide range of insect neuroanatomical and functional data. It facilities biological insight by enabling effective cross-species comparisons, by intimately linking data on structure and function, and by serving as hub for information on insect neuroethology. TheIBdbprovides novel visualization and search tools, which are also available in a unique private mode of the database, before data is made public. This allows users to manage and visualize unpublished data, creating a strong incentive for data contribution and eliminating additional effort when publicly depositing the data at a later stage. These design principles could also serve as a blueprint for similar databases in other fields.
MR-based camera-less eye tracking using deep neural networks
Authors: Frey Markus , Nau Matthias , Doeller Christian F. 
Published: December 2020

AbstractViewing behavior provides a window into many central aspects of human cognition and health, and it is an important variable of interest or confound in many fMRI studies. To make eye tracking freely and widely available for MRI research, we developed DeepMReye: a convolutional neural network that decodes gaze position from the MR-signal of the eyeballs. It performs camera-less eye tracking at sub-imaging temporal resolution in held-out participants with little training data and across a broad range of scanning protocols. Critically, it works even in existing datasets and when the eyes are closed. Decoded eye movements explain network-wide brain activity also in regions not associated with oculomotor function. This work emphasizes the importance of eye tracking for the interpretation of fMRI results and provides an open-source software solution that is widely applicable in research and clinical settings.