bluuebunny/crossref_metadata_2025_split huggingface dataset:
Longitudinal SARS-CoV-2 RNA Wastewater Monitoring Across a Range of Scales Correlates with Total and Regional COVID-19 Burden in a Well-Defined Urban Population
Authors: Acosta Nicole , Bautista María A. , Waddell Barbara J. , McCalder Janine , Beaudet Alexander Buchner , Man Lawrence , Pradhan Puja , Sedaghat Navid , Papparis Chloe , Bacanu Andra , Hollman Jordan , Krusina Alexander , Southern Danielle , Williamson Tyler , Li Carmen , Bhatnagar Srijak , Murphy Sean , Chen Jianwei , Kuzma Darina , Meddings Jon , Hu Jia , Cabaj Jason L. , Conly John M. , Ruecker Norma J. , Achari Gopal , Ryan M. Cathryn , Frankowski Kevin , Hubert Casey R.J. , Parkins Michael D. 
Published: November 2021

AbstractWastewater-based epidemiology (WBE) is an emerging surveillance tool that has been used to monitor the ongoing COVID-19 pandemic by tracking SARS-CoV-2 RNA shed into wastewater. WBE was performed to monitor the occurrence and spread of SARS-CoV-2 from three wastewater treatment plants (WWTP) and six neighborhoods in the city of Calgary, Canada (population 1.3 million). A total of 222 WWTP and 192 neighborhood samples were collected from June 2020 to May 2021, encompassing the end of the first-wave (June 2020), the second-wave (November end to December, 2020) and the third-wave of the COVID-19 pandemic (mid-April to May, 2021). Flow-weighted 24-hour composite samples were processed to extract RNA that was then analyzed for two SARS-CoV-2-specific regions of the nucleocapsid gene, N1 and N2, using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Using this approach SARS-CoV-2 RNA was detected in 98.06 % (406/414) of wastewater samples. SARS-CoV-2 RNA abundance was compared to clinically diagnosed COVID-19 cases organized by the three-digit postal code of affected individuals’ primary residences, enabling correlation analysis at neighborhood, WWTP and city-wide scales. Strong correlations were observed between N1 & N2 gene signals in wastewater and new daily cases for WWTPs and neighborhoods. Similarly, when flow rates at Calgary’s three WWTPs were used to normalize observed concentrations of SARS-CoV-2 RNA and combine them into a city-wide signal, this was strongly correlated with regionally diagnosed COVID-19 cases and clinical test percent positivity rate. Linked census data demonstrated disproportionate SARS-CoV-2 in wastewater from areas of the city with lower socioeconomic status and more racialized communities. WBE across a range of urban scales was demonstrated to be an effective mechanism of COVID-19 surveillance.
Pragmatic Randomized Trial Assessing the Impact of Digital Health Technology on Quality of Life in Patients With Congestive Heart Failure: Design and Rationale
Authors: Victoria-Castro Angela M. , Martin Melissa L. , Yamamoto Yu , Ahmad Tariq , Arora Tanima , Calderon Frida , Desai Nihar R. , Gerber Brett , Lee Kyoung A. , Jacoby Daniel , Melchinger Hannah , Nguyen Andrew , Shaw Melissa M. , Simonov Michael , Williams Alyssa , Weinstein Jason , Wilson F. Perry 
Published: November 2021

ABSTRACTHeart failure is a complex syndrome that contributes significantly to mortality and morbidity in the Unites States. Self-management is an ACC/AHA-recommended management tool for chronic conditions, however, those with congestive heart failure have historically poor compliance, low health literacy, and comorbidities that lead to reduced adherence to therapies and lifestyle modifications. Digital health technologies have the potential to enhance care and improve self-management. This manuscript describes the rationale and challenges of the design and implementation of a pragmatic randomized controlled trial to evaluate the efficacy of three digital health technologies in the management of congestive heart failure. Leveraging the use of a fully electronic enrollment and consent platform, the trial will randomize 200 patients across heart failure clinics in the Yale New Haven Health system to receive either usual care or one of three distinct digital technologies designed to promote self-management and provide critical data to clinicians. Our primary outcome will measure the change in quality of life as assessed by the Kansas City Cardiomyopathy Questionnaire (KCCQ) at 3 months. Initial recruitment efforts have highlighted the large digital divide in our population of interest. Assessing not only clinical outcomes, but patient usability and ease of clinical integration of digital technologies will prove beneficial in determining the feasibility and success of the integration of such technologies into the healthcare system. Future learnings will illustrate strategies to improve patient engagement with, and integration of, digital health technologies to enhance the patient-clinician relationship.Clinicaltrials.govNCT04394754
COVID-19 Vaccine Rollouts and the Reproduction of Urban Spatial Inequality: Disparities Within Large U.S. Cities in March and April 2021 by Racial/Ethnic and Socioeconomic Composition
Authors: DiRago Nicholas V. , Li Meiying , Tom Thalia , Schupmann Will , Carrillo Yvonne , Carey Colleen M. , Gaddis S. Michael 
Published: November 2021

ABSTRACTRollouts of COVID-19 vaccines in the U.S. were opportunities to redress disparities that surfaced during the pandemic. Initial eligibility criteria, however, neglected geographic, racial/ethnic, and socioeconomic considerations. Marginalized populations may have faced barriers to then-scarce vaccines, reinforcing disparities. Inequalities may have subsided as eligibility expanded. Using spatial modeling, we investigate how strongly local vaccination levels were associated with socioeconomic and racial/ethnic composition as authorities first extended vaccine eligibility to all adults. We harmonize administrative, demographic, and geospatial data across postal codes in eight large U.S. cities over three weeks in Spring 2021. We find that, although vaccines were free regardless of health insurance coverage, local vaccination levels in March and April were negatively associated with poverty, enrollment in means-tested public health insurance (e.g., Medicaid), and the uninsured population. By April, vaccination levels in Black and Hispanic communities were only beginning to reach those of Asian and White communities in March. Increases in vaccination were smaller in socioeconomically disadvantaged Black and Hispanic communities than in more affluent, Asian, and White communities. Our findings suggest vaccine rollouts contributed to cumulative disadvantage. Populations that were left most vulnerable to COVID-19 benefited least from early expansions in vaccine availability in large U.S. cities.
Ignoring transmission dynamics leads to underestimation of the impact of a novel intervention against mosquito-borne disease
Authors: Cavany Sean M. , Huber John H. , Wieler Annaliese , Tran Quan Minh , Alkuzweny Manar , Elliott Margaret , España Guido , Moore Sean M. , Perkins T. Alex 
Published: November 2021

AbstractNew vector-control technologies to fight mosquito-borne diseases are urgently needed, the adoption of which depends on efficacy estimates from large-scale cluster-randomized trials (CRTs). The release ofWolbachia-infected mosquitoes is one promising strategy to curb dengue virus (DENV) transmission, and a recent CRT reported impressive reductions in dengue incidence following the release of these mosquitoes. Such trials can be affected by multiple sources of bias, however. We used mathematical models of DENV transmission during a CRT ofWolbachia-infected mosquitoes to explore three such biases: human movement, mosquito movement, and coupled transmission dynamics between trial arms. We show that failure to account for each of these biases would lead to underestimated efficacy, and that the majority of this underestimation is due to a heretofore unrecognized bias caused by transmission coupling. Taken together, our findings suggest thatWolbachia-infected mosquitoes could be even more promising than the recent CRT suggested. By emphasizing the importance of accounting for transmission coupling between arms, which requires a mathematical model, our results highlight the key role that models can play in interpreting and extrapolating the results from trials of vector control interventions.
Race-Specific, U.S. State-Specific COVID-19 Vaccination Rates Adjusted for Age
Authors: Wrigley-Field Elizabeth , Berry Kaitlyn M. , Persad Govind 
Published: November 2021

ABSTRACTWe provide the first age-standardized race/ethnicity-specific, state-specific vaccination rates for the United States, encompassing all states reporting race/ethnicity-specific vaccinations. The data reflect vaccinations through mid-October 2021. We use indirect age standardization to compare racial/ethnic state vaccination rates to national age-specific vaccination patterns. Results show that white and Black state median vaccination rates are, respectively, 89% and 76% of what would be predicted based on age; Hispanic and Native rates are almost identical to what would be predicted; and Asian-American/Pacific Islander rates are 110% of what would be predicted. We also find that racial/ethnic group vaccination rates are associated with state politics, as proxied by 2020 Trump vote share: for each percentage point increase in 2020 Trump vote share, vaccination rates decline by 1.08 percent of what would be predicted based on age. This decline is sharpest for Native American populations, although Native vaccinations are reported for relatively few states.
Listeriaspp. Isolated from Soil Samples Collected in the Great Smoky Mountains
Authors: Claxton Michelle L. , Hudson Lauren K. , Bryan Daniel W. , Denes Thomas G. 
Published: November 2021

ABSTRACTListeria monocytogenes,a foodborne pathogen, and otherListeriaspp. are present in natural environments. Isolating and characterizing strains from natural reservoirs can provide insight into the prevalence and diversity ofListeriaspp. in these environments, elucidate their contribution to contamination of agricultural and food processing environments and food products, and lead to the discovery of novel species. In this study, we evaluated the diversity ofListeriaspp. isolated from soil samples in a small region of the Great Smoky Mountains National Park (GSMNP), which is the most biodiverse national park in the United States National Park system. Of the 17Listeriaisolates that were recovered, whole-genome sequencing revealed that 14 were unique strains. The unique strains were shown to represent a diversity ofListeriaspp., includingL. monocytogenes(n=9),L. cossartiaesubsp.cossartiae(n=1),L. marthii(n=1),L. booriae(n=1), and a novel Listeria sp. (n=2). TheListeriaisolated in this study were collected from high elevation sites near a creek that drains into a series of rivers ultimately leading to the Mississippi River; thus, theListeriapresent in this natural environment could potentially travel downstream to a large region that includes portions of nine southeastern and midwestern states in the U.S. TheListeriaspp. isolated and described in this study provide insight into the diversity ofListeriaspp. found in the Great Smoky Mountains and indicate that this environment is a reservoir of novelListeriaspp.IMPORTANCEListeria monocytogenesis a foodborne pathogen that can cause serious systemic illness that, although rare, usually results in hospitalization and has a relatively high mortality rate compared to other foodborne pathogens. Identification of novel and diverseListeriaspp. provides insight into the genomic evolution, ecology, and evolution and variance of pathogenicity of this genus, especially in natural environments. ComparingL. monocytogenesandListeriaspp. isolates from natural environments, such as those recovered in this study, to contamination and/or outbreak strains may provide more information about the original natural sources of these strains and the pathways and mechanisms that lead to contamination of food products and agricultural or food processing environments.
Strict adherence to Mendel’s First Law across a large sample of human sperm genomes
Authors: Carioscia Sara A. , Weaver Kathryn J. , Bortvin Andrew N. , Ariad Daniel , Bell Avery Davis , McCoy Rajiv C. 
Published: November 2021

AbstractMendel’s Law of Segregation states that the offspring of a diploid, heterozygous parent will inherit either allele with equal probability. While the vast majority of loci adhere to this rule, research in model and non-model organisms has uncovered numerous exceptions whereby “selfish” alleles are disproportionately transmitted to the next generation. Evidence of such “transmission distortion” (TD) in humans remains equivocal in part because scans of human pedigrees have been under-powered to detect small effects. Recently published single-cell sequencing data from individual human sperm (n= 41,189; 969-3,377 cells from each of 25 donors) offer an opportunity to revisit this question with unprecedented statistical power, but require new methods tailored to extremely low-coverage data (∼0.01 × per cell). To this end, we developed a method, named rhapsodi, that leverages sparse gamete genotype data to phase the diploid genomes of the donor individuals, impute missing gamete genotypes, and discover meiotic recombination breakpoints, benchmarking its performance across a wide range of study designs. After applying rhapsodi to the sperm sequencing data, we then scanned the gametes for evidence of TD. Our results exhibited close concordance with binomial expectations under balanced transmission, in contrast to tenuous signals of TD that were previously reported in pedigree-based studies. Together, our work excludes the existence of even weak TD in this sample, while offering a powerful quantitative framework for testing this and related hypotheses in other cohorts and study systems.
Respiratory Syncytial virus NS1 protein targets the transactivator binding domain of MED25
Authors: Basse Vincent , Dong Jiawei , Peres de Oliveira Andressa , Vidalain Pierre-Olivier , Tangy Frederic , Galloux Marie , Eleouet Jean-Francois , Sizun Christina , Bajorek Monika 
Published: November 2021

AbstractRespiratory syncytial virus has evolved a unique strategy to evade host immune response by coding for two non-structural proteins NS1 and NS2. Recently it was shown that in infected cells, nuclear NS1 could be involved in transcription regulation of host genes linked to innate immune response, via an interaction with chromatin and the Mediator complex. Here we identified the MED25 Mediator subunit as an NS1 interactor in a yeast two-hybrid screen. We demonstrate that NS1 directly interacts with MED25 in vitro and in cellula, and that this interaction involves the C-terminal α3 helix of NS1 and the MED25 ACID domain. More specifically we showed by NMR that the NS1 α3 sequence primarily binds to the MED25 ACID H2 face, which is a transactivation domain (TAD) binding site for transcription regulators such as ATF6α, a master regulator of ER stress response activated upon viral infection. Moreover, we found out that the NS1 α3 helix could compete with ATF6α TAD binding to MED25. This finding points to a mechanism of NS1 interfering with innate immune response by impairing recruitment by cellular TADs of the Mediator via MED25 and hence transcription of specific genes by RNA polymerase II.ImportanceHuman RSV is the leading cause of infantile bronchiolitis in the world and one of the major causes of childhood deaths in resource-poor settings. It is a major unmet target for vaccines and anti-viral drugs. RSV non-structural protein NS1 is known to antagonize the cellular immune response and was recently shown to be involved in transcription regulation of infected cells. However, the exact mechanism of this regulation is not well defined. Here we show that nuclear NS1 interacts directly with the Mediator subunit MED25 and is able to compete with a cellular transcription activator, which is activated during viral infection. We hypothesize that this interaction may underlie regulation of the expression of genes involved in the innate immune response.
Glycated haemoglobin measurements from UK Biobank are different to those in linked primary care records: implications for combining biochemistry data from research studies and routine clinical care
Authors: Young Katherine G , McDonald Timothy J , Shields Beverley M 
Published: November 2021

AbstractData linkage of cohort or RCT data with routinely collected data is becoming increasingly commonplace, and this often involves combining biomarker measurements from different sources. However, sources may have different biases due to differences in assay method and calibration. Combining these measurements, or diagnoses based on these measurements, is therefore not always valid. We highlight an example using glycated haemoglobin A1c (HbA1c) test results from two different sources in UK Biobank data.
Predictive Modelling of Brain Disorders with Magnetic Resonance Imaging: A Systematic Review of Modelling Practices, Transparency, and Interpretability in the use of Convolutional Neural Networks
Authors: O’Connell Shane , Cannon Dara M , Ó Broin Pilib 
Published: November 2021

AbstractBrain disorders comprise several psychiatric and neurological disorders which can be characterised by impaired cognition, mood alteration, psychosis, depressive episodes, and neurodegeneration. Clinical diagnoses primarily rely on a combination of life history information and questionnaires, with a distinct lack of discriminative biomarkers in use for psychiatric disorders. Given that symptoms across brain conditions are associated with functional alterations of cognitive and emotional processes, which can correlate with anatomical variation, structural magnetic resonance imaging (MRI) data of the brain are an important focus of research studies, particularly for predictive modelling. With the advent of large MRI data consortia (such as the Alzheimer’s Disease Neuroimaging Initiative) facilitating a greater number of MRI-based classification studies, convolutional neural networks (CNNs) – deep learning models suited to image processing – have become increasingly popular for research into brain conditions. This has resulted in a myriad of studies reporting impressive predictive performances, demonstrating the potential clinical value of deep learning systems. However, modelling practices, transparency, and interpretability vary widely across studies, making them difficult to compare and/or reproduce, thus potentially limiting clinical applications. Here, we conduct a qualitative systematic literature review of 60 studies carrying out CNN-based predictive modelling of brain disorders using MRI data and evaluate them based on three principles – modelling practices, transparency, and interpretability. We furthermore propose several recommendations aimed at maximising the potential for the integration of CNNs into clinical frameworks.
Is it time to use machine learning survival algorithms for survival and risk factors prediction instead of Cox proportional hazard regression? A comparative population-based study
Authors: Morsy Sara , Hieu Truong Hong , Makram Abdelrahman M , Hassan Osama Gamal , Minh Duc Nguyen Tran , Zayan Ahmad Helmy , Nhat-Nam Le-Dong , Huy Nguyen Tien 
Published: November 2021

AbstractPurposeApplying machine learning in medical statistics offers more accurate prediction models. In this paper, we aimed to compare the performance of the Cox Proportional Hazard model (CPH), Classification and Regression Trees (CART), and Random Survival Forest (RSF) in short-, and long-term prediction in glioblastoma patients.MethodsWe extracted glioblastoma cancer data from the Surveillance, Epidemiology, and End Results database (SEER). We used the CPH, CART, and RSF for the prediction of 1- to 10-year survival probabilities. The Brier Score for each duration was calculated, and the model with the least score was considered the most accurate.ResultsThe cohort included 26473 glioblastoma patients divided into two groups: training (n = 18538) and validation set (n = 7935). The average survival duration was seven months. For the short- and long-term predictions, RSF was the best algorithm followed by CPH and CART.ConclusionFor big data, RSF was found to have the highest accuracy and best performance. Using the accurate statistical model for survival prediction and prognostic factors determination will help the care of cancer patients. However, more developments of the R packages are needed to allow more illustrations of the effect of each covariate on the survival probability.
Mortality in cancer patients with congenital anomalies across different age groups: trend analysis and prognostic risk factors
Authors: Morsy Sara , Hieu Truong Hong , Ghozy Sherief , Tran Linh , Huy Nguyen Tien 
Published: November 2021

AbstractPurposeCongenital anomalies are one of the causes of the high mortality rate in children diagnosed with cancer. However, there is a gap of evidence of the rate of cancer mortality in older patients who had congenital anomalies. The study, therefore, aimed to investigate the epidemiology of cancer mortality in those patients.MethodsData were retrieved for patients with cancer and died due to congenital causes throughout 43 years from Surveillance, Epidemiology, and End Results program SEER. The age of patients was divided into nine groups each is formed of 10 years interval. Joinpoint analysis was used to calculate the trends of Cancer mortality and Cox proportional hazard ratio to identify the mortality risk factors.ResultsWe have included 2682 patients with death associated with congenital malformation. The mortality of cancer patients due to congenital anomalies greatly enhanced in the last years with the overall average annual percent was 3.8%. Interestingly, congenital anomalies had less mortality risk than other causes reported in SEER. Moreover, age, sex, radiation, chemotherapy, and behavior of tumor were significantly associated with higher survival in patients with congenital anomalies.ConclusionsCancer patients with congenital anomalies had less mortality risk than patients with other diseases reported in SEER. The mortality rates decreased recently, with the most mortality in the bone marrow and prostate tumors.Implications for Cancer SurvivorsCongenital anomalies are considered the least studied diseases in cancer patients. In this study, we studied how congenital anomalies did not increase the risk for cancer. However, our analysis implied the congenital anomalies in the male reproductive system were associated with the highest risk of cancer.