bluuebunny/crossref_metadata_2025_split huggingface dataset:
Age-Specific SARS-CoV-2 Infection Fatality and Case Identification Fraction in Ontario, Canada
Authors: Fisman David N. , Drews Steven J. , Tuite Ashleigh R. , O’Brien Sheila F. 
Published: November 2020

AbstractBackgroundSARS-CoV-2 is a novel pandemic pathogen that displays great variability in virulence across cases. Due to limitations in diagnostic testing only a subset of infections are identified. Underestimation of true infections makes calculation of infection fatality ratios (IFR) challenging.Seroepidemiology allows estimation of true cumulative incidence of infection in populations, for estimation of IFR.MethodsSeroprevalence estimates were derived using retention samples stored by Canadian Blood Services in May 2020. These were compared to non-long-term care-linked case and fatality data from the same period. Estimates were combined to generate IFR and case identification fraction estimates.ResultsOverall IFR was estimated to be 0.80% (0.75 to 0.85%), consistent with estimates from other jurisdictions. IFR increased exponentially with age from 0.01% (0.002 to 0.04%) in those aged 20-29 years, to 12.71% (4.43 to 36.50%) in those aged 70 and over. We estimated that 5.88 infections (3.70 to 9.21) occurred for every case identified, with a higher fraction of cases identified in those aged 70 and older (42.0%) than those aged 20-29 (9.4%). IFR estimates in those aged 60 and older were identical to pooled estimates from other countries.ConclusionsTo our knowledge these are the first Canadian estimates SARS-CoV-2 IFR and case identification fraction. Notwithstanding biases associated with donor sera they are similar to estimates from other countries, and approximately 80-fold higher than estimates for influenza A (H1N1) during the 2009 epidemic. Ontario’s first COVID-19 pandemic wave is likely to have been accurately characterized due to a high case identification fraction.
EPIDEMIOLOGICAL AND CLINICAL CHARACTERISTICS OF COVID-19 PATIENTS IN KENYA
Authors: Ombajo Loice Achieng , Mutono Nyamai , Sudi Paul , Mutua Mbuvi , Sood Mohammed , Ali Loo Alliy Muhammad , Juma Phoebe , Odhiambo Jackline , Shah Reena , Wangai Frederick , Maritim Marybeth , Anzala Omu , Amoth Patrick , Kamuri Evans , Munyu Waweru , Thumbi SM 
Published: November 2020

AbstractBackgroundMore than 49,000 cases of infection and 900 deaths from COVID-19 have been recorded in the Kenya. However, the characteristics and risk factors for severe outcomes among hospitalized COVID-19 patients in this setting have not been described.MethodsWe extracted demographic, laboratory, clinical and outcome data from medical records of RT-PCR confirmed SARS-CoV2 patients admitted in six hospitals in Kenya between March and September, 2020. We used Cox proportional hazards regressions to determine factors related to in-hospital mortality.ResultsData from 787 COVID-19 patients was available. The median age was 43 years (IQR 30-53), with 505 (64%) males. At admission, 455 (58%) were symptomatic. The commonest symptoms were cough (337, 43%), loss of taste or smell (279, 35%), and fever (126, 16%). Co-morbidities were reported in 340 (43%), with cardiovascular disease, diabetes and HIV documented in 130 (17%), 116 (15%), 53 (7%) respectively. 90 (11%) were admitted to ICU for a mean of 11 days, 52 (7%) were ventilated with a mean of 10 days, 107 (14%) died. The risk of death increased with age [hazard ratio (HR) 1.57 (95% CI 1.13 – 2.19)] for persons >60 years compared to those <60 years old; having co-morbidities [HR 2.34 (1.68 – 3.25)]; and among males [HR 1.76 (1.27, 2.44)] compared to females. Elevated white blood cell count and aspartate aminotransferase were associated with higher risk of death.ConclusionsWe identify the risk factors for mortality that may guide stratification of high risk patients.
Childhood obesity prediction from nationwide health records
Authors: Rossman Hagai , Shilo Smadar , Barbash-Hazan Shiri , Artzi Nitzan Shalom , Hadar Eran , Balicer Ran D. , Feldman Becca , Wiznitzer Arnon , Segal Eran 
Published: November 2020

AbstractRapid rise in childhood obesity prevalence worldwide and its major implications for health warrant the development of better prevention strategies. Here, we analyzed electronic health records of children from Israel’s largest healthcare provider from 2002 to 2018 and developed a model for predicting children’s obesity. Data included demographics, anthropometric measurements, medications, diagnoses, and laboratory tests of children and their families. Obesity was defined as body mass index (BMI) ≥95th percentile for age and sex. To identify the most critical time-window in which the largest annual increases in BMI percentile occurs during early childhood, we first analyzed childhood BMI trajectories among 382,132 adolescents. Next, we devised a prediction model targeted to identify children at high risk of obesity prior to this time-window, predicting obesity at 5-6 years of age based on data from the first 2 years of life of 136,196 children. Retrospective BMI analysis revealed that among obese adolescents, the greatest acceleration in BMI percentiles occured between 2-4 years of age. Our model, validated temporally and geographically, accurately predicted obesity at 5-6 years old (auROC of 0.804). Discrimination results on subpopulations demonstrated its robustness across the pediatric population. The model’s most influential features included anthropometric measurements of the child and family. Other impactful features included ancestry and pregnancy glucose. Antibiotics exposure in utero and during the first 2 years of life had no observed impact on obesity prediction. Our model, targeted to identify children prior to the most critical time-window of BMI acceleration, may allow a more accurate identification and implementation of early prevention strategies for children at high risk of obesity and can be readily embedded into healthcare systems.
Quantitative measurement of IgG to SARS-CoV-2 proteins using ImmunoCAP
Authors: Keshavarz Behnam , Wiencek Joesph R. , Workman Lisa J. , Straesser Matthew D. , Muehling Lyndsey M. , Canderan Glenda , Drago Fabrizio , Bonham Catherine A. , Sturek Jeffrey M. , Ramani Chintan , McNamara Coleen A. , Woodfolk Judith A. , Kadl Alexandra , Platts-Mills Thomas A.E. , Wilson Jeffrey M. 
Published: November 2020

AbstractBackgroundDetailed understanding of the immune response to SARS-CoV-2, the cause of coronavirus disease 2019 (COVID-19), has been hampered by a lack of quantitative antibody assays.ObjectiveTo develop a quantitative assay for IgG to SARS-CoV-2 proteins that could readily be implemented in clinical and research laboratories.MethodsThe biotin-streptavidin technique was used to conjugate SARS-CoV-2 spike receptor-binding-domain (RBD) or nucleocapsid protein to the solid-phase of the ImmunoCAP resin. Plasma and serum samples from patients with COVID-19 (n=51) and samples from donors banked prior to the emergence of COVID-19 (n=109) were used in the assay. SARS-CoV-2 IgG levels were followed longitudinally in a subset of samples and were related to total IgG and IgG to reference antigens using an ImmunoCAP 250 platform.ResultsPerformance characteristics demonstrated 100% sensitivity and 99% specificity at a cut-off level of 2.5 µg/mL for both SARS-CoV-2 proteins. Among 36 patients evaluated in a post-hospital follow-up clinic, median levels of IgG to spike-RBD and nucleocapsid were 34.7 µg/mL (IQR 18-52) and 24.5 µg/mL (IQR 9-59), respectively. Among 17 patients with longitudinal samples there was a wide variation in the magnitude of IgG responses, but generally the response to spike-RBD and to nucleocapsid occurred in parallel, with peak levels approaching 100 µg/mL, or 1% of total IgG.ConclusionsWe have described a quantitative assay to measure IgG to SARS-CoV-2 that could be used in clinical and research laboratories and implemented at scale. The assay can easily be adapted to measure IgG to novel antigens, has good performance characteristics and a read-out in standardized units.
Interleukin- 10 (IL-10) gene polymorphisms and prostate cancer susceptibility: evidence from a meta-analysis
Authors: Yadav Upendra , Kumar Pradeep , Rai Vandana 
Published: November 2020

AbstractProstate cancer is the second most frequent cancer in men. The frequency of the prostate cancer is greatly varies in different populations of the world. Three common polymorphisms in promoter region of interleukin-10 (IL-10) gene viz. -1082 A>G, -819 C>T and -592 C>A are extensively studied in prostate cancer with inconclusive results. So, a meta-analysis was performed to assess the association between these three IL-10 gene polymorphisms and risk of prostate cancer susceptibility. Suitable studies were retrieved by electronic databases search and odds ratios (ORs) with 95% confidence intervals (CIs) were used as association measure. All the statistical analyses were conducted in the Open Meta-Analyst program.In our meta-analysis we included 17 studies (10,718 samples), 11 studies (8,391 samples) and 13 studies (7,801 samples) for -1082 A>G, -819 C>T and -592 C>A polymorphisms respectively. The result of the -592 C>A polymorphism revealed low heterogeneity with no association in the overall analysis (ORAvs.C= 1.05, 95% CI= 0.99-1.12, p= 0.09, I2= 35.89%). In ethnicity based stratified analyses, significant association was found in Caucasian population with prostate cancer using allele contrast model (ORAvs.C= 1.08, 95% CI= 1.01-1.16, p= 0.02, I2= 19.37%), homozygote model (ORAAvs.CC= 1.24, 95% CI= 1.00-1.52, p= 0.04, I2= 0%), and dominant model (ORAA+CAvs.CC= 1.10, 95% CI= 1.00-1.22, p= 0.05, I2= 32.57%). No such results were found in the Asian population. In the other two polymorphisms i.e. -1082 A>G and -819 C>T, no significant association with prostate cancer was observed.In conclusion, results of present meta-analysis suggested that IL-10 -592 C>A polymorphism plays a role in the progression of the prostate cancer in the Caucasian population.
Passive smartphone-based assessment of cognitive changes in neurosurgery
Authors: Akeret Kevin , Vasella Flavio , Zindel-Geisseler Olivia , Dannecker Noemi , Brugger Peter , Regli Luca , Young Peter , Stienen Martin N. , Ghosh Arko 
Published: November 2020

AbstractClinical observations suggest dynamic alterations in behavior after brain surgery. While some alterations reportedly occur within days others gradually develop over several months. These alterations can be attributed to the pre-surgical impact of the diseased tissue, neuronal damage caused by the surgery, and subsequent plasticity. A key step towards a systems-level understanding of the brain-behavior relationships is to capture the dynamics of the cognitive alterations. Here, we first established in 38 healthy individuals that the day-to-day smartphone interactions can be used to inform on cognitive processing speed. Next, we analyzed the smartphone interactions in 12 patients undergoing intracranial tumor surgery, with postsurgical follow-up of up to a year. In healthy individuals, the speed of the touchscreen interactions was highly correlated to choice reaction times (R2 = 0.71) but less so to simple reaction times (R2 = 0.15) on Deary-Liewald tests. Touchscreen interactions slowed immediately after surgery but the post-surgical changes varied between patients. Data-driven models revealed the time-constant of the short-term postsurgical changes and the time taken to stabilize after the surgery. Furthermore, by using conceptually distinct types of touchscreen interaction speeds – i.e. unlocking time and app locating speed – we established that the post-surgical changes are domain-specific. Interestingly, in this small sample, the pre-surgical smartphone speeds were highly related to the speeds post-stabilization (R2 = 0.75 to 0.95). The proxy measures of cognition seamlessly captured on the smartphone can reveal postsurgical dynamics inaccessible to conventional testing. We propose that the transient cognitive alterations indicate the time-constrained influence of distinct neuronal mechanisms triggered by the surgery.
Evaluation of the disease outcome in Covid-19 infected patients by disease symptoms: a retrospective cross-sectional study in Ilam Province, Iran
Authors: Sadeghifar Jamil , Jalilian Habib , Momeni Khalil , Delam Hamed , Sheleme Tadesse , Rashidi Ayoub , Hemmati Fariba , Falahi Shahab , Arab-Zozani Morteza 
Published: November 2020

AbstractBackgroundnovel coronavirus disease-19 (COVID-19) announced as a global pandemic in the year 2020. With the spread of the disease, a better understanding of patient outcomes associated with their symptoms in diverse geographic levels is vital. We aimed to analysis clinical outcomes of COVID-19 patients by disease symptoms in Ilam province of Iran.MethodsThis is a retrospective study. Data were collected from integrated health system records for all hospitals affiliated to Ilam University of Medical Sciences between 26 Jan 2020 and 02 May 2020. All patients with definite positive test were enrolled in this study. We used descriptive analyses, chi-square test and binary logistic regression to analyze the data using SPSS version 22.ResultsThe mean age was 46.47±18.24 years. Of 3608 patients, 3477 (96.1%) were discharged and 129 (3.9%) were died. 54.2% of the patients were male and were in the age group of 30-40 years old age. Cough, sore throat, shortness of breath or difficulty breathing and fever or chills were the most common symptoms. People with symptoms of shortness of breath, abnormal radiographic findings of the chest, and chest pain and pressure were relatively more likely to die. Based on the findings of binary logistic regression probability of death in people who showed shortness of breath, abnormal chest radiographic findings and chest pain was 1.34, 1.24 and 1.32 times higher than those who did not show these symptoms, respectively.ConclusionOur study provides evidence that presentation of some symptoms does significantly impact on outcomes of patients infected with SARS-CoV-2. Early detection of symptoms and proper management of outcomes can reduce mortality in patients with COVID-19.
Ngari virus (Orthobunyavirus, Peribunyaviridae) in ixodid ticks collected from cattle in Guinea
Authors: Makenov MT , Toure AH , Bayandin RB , Gladysheva AV , Shipovalov AV , Boumbaly S , Sacko N , Korneev MG , Yakovlev SA , Zhurenkova OB , Grigoreva YaE , Fyodorova MV , Radyuk EV , Morozkin ES , Boiro MY , Khafizov K , Matsvay A , Karan LS 
Published: November 2020

AbstractNgari virus is a mosquito-borne virus belonging to the genus Orthobunyavirus (Peribunyaviridae family). This virus is pathogenic to humans and causes severe illness. Ngari virus is present in several African countries, including Madagascar. Here, we report the detection of Ngari virus in ixodid ticks collected from cows in Guinea.A tick survey was conducted in March-November of 2018 in six regions of Guinea. The sample comprised 710 pools, with a total of 2067 ticks belonging to five species collected from 197 cows. At the initial stage, we screened a subsample of tick pools of vector-borne viruses with a multiplex genus-specific primer panel. In the second stage of the study, we narrowed the search and screened all the samples by qPCR for the detection of Ngari virus. All positive samples were sequenced with primers flanking Ngari virus-specific fragments on the S and M segments.We found Ngari virus in 12 pools that were formed from engorged ticks collected from livestock in three villages of the Kindia and Kankan regions. Sequencing of the S and M segments confirmed that the detected viruses belong to Ngari virus, and the viruses were most similar to the strain Adrar, which was isolated in Mauritania. We detected viral RNA in ticks of the following species: Amblyomma variegatum, Rhipicephalus geigyi, and Rh. (Boophilus) spp. There is no evidence that ixodid ticks are competent vectors of the Ngari virus. Most likely, the ticks obtained the virus through blood from an infected host.
Care homes, their communities, and resilience in the face of the COVID-19 pandemic: interim findings from a qualitative study
Authors: Marshall Fiona , Gordon Adam L , Gladman John RF , Bishop Simon 
Published: November 2020

AbstractBackgroundFrom late February 2020, English care homes rapidly adapted their practices in response to the COVID-19 pandemic. In addition to accommodating new guidelines and policies, staff had to adjust to rapid reconfiguration of services external to the home that they would normally depend upon for support. This study examined the complex interdependencies of support as staff responded to COVID-19. The aim was to inform more effective responses to the ongoing pandemic, and to improve understanding of how to work with care home staff and organisations after the pandemic has passed.MethodsTen managers of registered care homes in the East Midlands of England were interviewed by videoconference or phone about their experiences of the crisis from a structured organisational perspective. Analysis used an adapted organisational framework analysis approach with a focus on social ties and interdependencies between organisations and individuals.ResultsThree key groups of interdependencies were identified: care processes and practice; resources; and governance. Care home staff had to deliver care in innovative ways, making high stakes decisions in circumstances defined by: fluid ties to organisations outside the care home; multiple, sometimes conflicting, sources of expertise and information; and a sense of deprioritisation by authorities. Organisational responses to the pandemic by central government resulted in resource constraints and additional work, and sometimes impaired the ability of staff and managers to make decisions. Local communities, including businesses, third-sector organisations and individuals, were key in helping care homes overcome challenges. Care homes, rather than competing, were found to work together to provide mutual support. Resilience in the system was a consequence of dedicated and resourceful staff using existing local networks, or forging new ones, to overcome barriers to care.ConclusionsThis study identified how interdependency between care home organisations, the surrounding community, and key statutory and non-statutory organisations beyond their locality, shaped decision making and care delivery during the pandemic. Recognising these interdependencies, and the expertise shown by care home managers and staff as they navigate them, is key to providing effective healthcare in care homes as the pandemic progresses, and as the sector recovers afterwards.
Penalized longitudinal mixed models with latent group structure, with an application in neurodegenerative diseases
Authors: Hatami Farhad , Perrakis Konstantinos , Cooper-Knock Johnathan , Mukherjee Sach , Dondelinger Frank 
Published: November 2020

SummaryLarge-scale longitudinal data are often heterogeneous, spanning latent subgroups such as disease subtypes. In this paper, we present an approach calledlongitudinal joint cluster regression(LJCR) for penalized mixed modelling in the latent group setting. LJCR captures latent group structure via a mixture model that includes both the multivariate distribution of the covariates and a regression model for the response. The longitudinal dynamics of each individual are modeled using a random effect intercept and slope model. Inference is done via a profile likelihood approach that can handle high-dimensional covariates via ridge penalization. LJCR is motivated by questions in neurodegenerative disease research, where latent subgroups may reflect heterogeneity with respect to disease presentation, progression and diverse subject-specific factors. We study the performance of LJCR in the context of two longitudinal datasets: a simulation study and a study of amyotrophic lateral sclerosis (ALS). LJCR allows prediction of progression as well as identification of subgroups and subgroup-specific model parameters.
Serum Vitamin D Levels Are Not Associated with Obstructive Lung Disease in the General Population: An NHANES Analysis (2007-2008 to 2009-2010)
Authors: Seedahmed Mohamed I , Baugh Aaron D , Kempker Jordan A 
Published: November 2020

ABSTRACTBackgroundObstructive lung disease is a significant cause of morbidity and healthcare burden within the United States. A growing body of evidence has suggested that vitamin D levels can influence the course or incidence of obstructive lung disease. However, there is an insufficient previous investigation of this association.Study Design and MethodsWe used the National Health and Nutrition Examination Survey (NHANES) cycles 2007-2008 and 2009-2010 spirometry results of individuals aged 40 years and older to assess the association between serum 25(OH)D levels and obstructive lung disease, as defined by the American Thoracic Society using the lower limit of normal (LLN). We used stage multivariate survey-logistic regression with backward selection.ResultsThe final model included body mass index, pack-years smoking history, and ethnicity. In the primary model, there was no association between vitamin D levels and obstructive lung disease. We noted an association between “Other Hispanic” self-identified race and serum Vitamin D levels wherein higher levels were associated with higher odds of obstructive lung disease in this ethnicity, but not among other racial or ethnic groups (OR= 1.48, p= 0.02).ConclusionsSerum Vitamin D levels among adults are not associated with the odds of obstructive lung disease in the general population. Results among non-Mexican Hispanic participants highlight the need for further research in minority populations. More work is needed to address the course and incidence of lung disease in the United States.RESEARCH IN CONTEXTWhat is the key question?In the general population, is there an independent association between Vitamin D and obstructive lung disease after controlling for relevant covariates?What is the bottom line?In exploring whether serum vitamin D levels are associated with odds of obstructive lung disease in the general US population, we did not find an independent association in the overall sample.Why read on?This paper adds nuance to the broad understanding of vitamin D’s role in lung pathophysiology.