bluuebunny/crossref_metadata_2025_split huggingface dataset:
No difference in risk of hospitalisation between reported cases of the SARS-CoV-2 Delta variant and Alpha variant in Norway
Authors: Veneti Lamprini , Valcarcel Salamanca Beatriz , Seppälä Elina , Starrfelt Jostein , Storm Margrethe Larsdatter , Bragstad Karoline , Hungnes Olav , Bøås Håkon , Kvåle Reidar , Vold Line , Nygård Karin , Buanes Eirik Alnes , Whittaker Robert 
Published: September 2021

AbstractObjectivesTo estimate the risk of hospitalisation among reported cases of the Delta-variant of SARS-CoV-2 compared to the Alpha variant in Norway. We also estimated the risk of hospitalisation by vaccination status.MethodsWe conducted a cohort study on laboratory-confirmed cases of SARS-CoV-2 in Norway, diagnosed between 3 May and 15 August 2021. We calculated adjusted risk ratios (aRR) with 95% confidence intervals (CIs) using multivariable binomial regression, accounting for variant, vaccination status, demographic characteristics, week of sampling and underlying comorbidities.ResultsWe included 7,977 cases of Delta and 12,078 cases of Alpha. Overall, 347 (1.7%) cases were hospitalised. The aRR of hospitalisation for Delta compared to Alpha was 0.97 (95%CI 0.76–1.23). Partially vaccinated cases had a 72% reduced risk of hospitalisation (95%CI 59%–82%), and fully vaccinated cases had a 76% reduced risk (95%CI 61%–85%), compared to unvaccinated cases.ConclusionsWe found no difference in the risk of hospitalisation for Delta cases compared to Alpha cases in Norway. Further research from a wide variety of settings is needed to better understand the association between the Delta variant and severe disease. Our results support the notion that partially and fully vaccinated persons are highly protected against hospitalisation with COVID-19.HighlightsThe SARS-CoV-2 Delta variant has dominated in Norway since July 2021There was no difference in the risk of hospitalisation for Delta cases compared to AlphaPartially and fully vaccinated cases had >70% decreased risk of hospitalisation
Advanced analysis in epidemiological modeling: Detection of wave
Authors: Atangana Abdon , İğret Araz Seda 
Published: September 2021

AbstractSome mathematical concepts have been used in the last decades to predict the behavior of spread of infectious diseases. Among them, the reproductive number concept has been used in several published papers for study the stability of the spread. Some conditions were suggested to predict there would be either stability or instability. An analysis was also suggested to determine conditions under which infectious classes will increase or die out. Some authors pointed out limitations of the reproductive number, as they presented its inability to fairly help understand the spread patterns. The concept of strength number and analysis of second derivatives of the mathematical models were suggested as additional tools to help detect waves. In this paper, we aim at applying these additional analyses in a simple model to predict the future.
The association between SARS-CoV-2 infection and neuronal damage: A population-based nested case-control study
Authors: Aziz N. Ahmad , Santos Marina L.S. , Breteler Monique M.B. 
Published: September 2021

ABSTRACTObjectiveTo assess whether severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection is associated with changes in plasma levels of neurofilament light chain (NfL), an extremely sensitive marker of neuroaxonal damage, in community-dwelling individuals.SettingThis study was embedded within the Rhineland Study, an ongoing community-based cohort study in Bonn, GermanyDesignCross-sectional nested case-control study.ParticipantsParticipants were selected based on results from a previously conducted seroprevalence survey within the framework of the Rhineland Study. Cases were defined as those individuals who had had two positive confirmatory test results, including a recombinant spike-based immunofluorescence assay and a plaque reduction neutralization test (N=21). As controls, a random sample of individuals with a negative ELISA test result (Controls I, N=1117), and those with a borderline or positive ELISA test result who failed confirmatory testing (Controls II, N=68), were selected.Outcome measuresPlasma levels of NfL at the time of measurement, as well as change in plasma NfL levels compared to previously measured pre-pandemic levelsResultsAfter adjustment for age, sex and batch effects, serologically confirmed SARS-CoV-2 infection was neither associated with cross-sectional NfL levels, nor with the magnitude of change from pre-pandemic levels, compared to either of the two control groups. Similarly, after adjustment for age, sex and batch effects, self-reported neurological symptoms – including altered sense of smell or taste, headache, myalgia and fever – were not associated with changes in NfL levels in participants with a serologically confirmed SARS-CoV-2 infection (all p ≥ 0.56).ConclusionsOur findings indicate that mild-to-moderate coronavirus disease-19 is unlikely to be associated with a clinically relevant degree of neuroaxonal damage, even in those cases associated with neurological symptoms.
Heterogeneity of recurrences in rectal cancer: application of population models facilitates personalized medicine
Authors: Nagtegaal Iris D. , van de Water Carlijn , Borst Dyogo , Marijnen Corrie A.M. , van de Velde Cornelis J.H. , Hugen Niek , Jongejans Eelke 
Published: September 2021

AbstractDue to heterogeneity in presentation and outcome, patients with metastatic disease cannot be considered a single group. The timing, location and combinations of recurrences determine the feasibility of treatment of the individual patient in an era in which the options for local and systemic treatment have expanded. Studies investigating this complexity are hampered by the lack of both large cohorts and adequate methods.In a well-defined cohort of rectal cancer patients from a randomized clinical trial, with long standardized follow-up, we applied spatial projection models derived from population ecology to overcome the complexity problem. We describe the recurrence patterns in detail and performed stochastic simulation experiments resulting in 1.5 million evaluable patients. The risk of subsequent recurrences was dependent on the presentation of the first recurrent event and decreased with increasing recurrence-free interval. The risk of local recurrence for the median patient (65.8 years, pT3 adenocarcinoma) was threefold increased after the development of rare metastases. The risk of development of rare metastases was increased after the development of other extrahepatic metastases.Our cross-disciplinary approach delivers insights allowing for the development of personalized strategies for (local) treatment of recurrent disease, as well as for surveillance strategies that may potentially impact large patient cohorts. In this proofof-principle study we demonstrate the feasibility of spatial projection models for cancer research.
Artificial Intelligence in Dry Eye Disease
Authors: Storås Andrea M. , Strümke Inga , Riegler Michael A. , Grauslund Jakob , Hammer Hugo L. , Yazidi Anis , Halvorsen Pål , Gundersen Kjell G. , Utheim Tor P. , Jackson Catherine 
Published: September 2021

AbstractDry eye disease (DED) has a prevalence of between 5 and 50%, depending on the diagnostic criteria used and population under study. However, it remains one of the most underdiagnosed and undertreated conditions in ophthalmology. Many tests used in the diagnosis of DED rely on an experienced observer for image interpretation, which may be considered subjective and result in variation in diagnosis. Since artificial intelligence (AI) systems are capable of advanced problem solving, use of such techniques could lead to more objective diagnosis. Although the term ‘AI’ is commonly used, recent success in its applications to medicine is mainly due to advancements in the sub-field of machine learning, which has been used to automatically classify images and predict medical outcomes. Powerful machine learning techniques have been harnessed to understand nuances in patient data and medical images, aiming for consistent diagnosis and stratification of disease severity. This is the first literature review on the use of AI in DED. We provide a brief introduction to AI, report its current use in DED research and its potential for application in the clinic. Our review found that AI has been employed in a wide range of DED clinical tests and research applications, primarily for interpretation of interferometry, slit-lamp and meibography images. While initial results are promising, much work is still needed on model development, clinical testing and standardisation.
Using multivariate models to examine the impact of COVID-19 pandemic and gender differences on health and health care
Authors: Ye Jiancheng , Ren Zhimei 
Published: September 2021

ABSTRACTObjectiveTo examine the effect of the COVID-19 pandemic, the effect of sex, and the joint effect of sex and the COVID-19 pandemic in relation to health communication, physical activity, mental health, and behavioral health.MethodsWe drew data from the National Cancer Institute’s 2020 Health Information National Trends Survey (HINTS). We described and compared the characteristics of social determinants of health, physical activity, mental health, alcohol use, patterns of social networking service use, and health information data sharing. Analyses were weighted to provide nationally representative estimates. Multivariate models (multiple linear regression, multiple logistic regression, and multinomial logistic model) were used to assess the sole and joint effect of sex and pandemic. In addition, we applied the Bonferroni correction to adjust p-values to decrease the risks of type I errors when making multiple statistical tests.ResultsWomen are more likely to use mobile health and health communication technologies. The effect of sex after the COVID-19 pandemic is significant on mental health, and women are more possible to have depression or anxiety disorders. The effect of sex is also significant before and after the pandemic regarding seeking health or medical information. Women have a smaller quantity and intensity of physical activity, which has a negative effect on health.ConclusionGender differences exist regardless of the COVID-19 pandemic and the pandemic amplifies the differences in some health and health care domains. Intersectional gender analyses are integral to addressing issues that arise and mitigating the exacerbation of inequities. Responses to the pandemic should consider diverse perspectives, including sex and gender.
Life course circumstances contribute to the acceleration of phenotypic and functional aging in Chinese middle-aged and older adults
Authors: Cao Xingqi , Ma Chao , Zheng Zhoutao , He Liu , Hao Meng , Chen Xi , Crimmins Eileen M. , Gill Thomas M. , Levine Morgan E. , Liu Zuyun 
Published: September 2021

AbstractAccelerated aging implies health inequalities in late life and consequently, poses a huge challenge to society. With two well-validated aging measures, this study aimed to evaluate the relative contributions of life course circumstances to variance in these aging measures with policy implications. We assembled data for 6224 middle-aged and older adults (≥45 years) who participated in the 2014 life course survey, the 2015 biomarker collection, and the 2015 main survey of the China Health and Retirement Longitudinal Study (CHARLS). Two aging measures, including physiological dysregulation (PD) and frailty index (FI), were calculated. Life course circumstances, i.e., 70 circumstances variables involving childhood and adulthood circumstances, demographics, and behaviors, were categorized into 11 study domains for simplicity. The Shapley value decomposition, hierarchical clustering, and general linear regression models were performed. The Shapley value decomposition revealed that all 11 study domains accounted for about 6.3% and 29.7% of the variance in PD and FI, respectively. We then identified six subpopulations who shared similar patterns in terms of childhood and adulthood circumstances. One subpopulation (i.e., disadvantaged) who reported experiencing more childhood and adulthood adversity consistently exhibited accelerated aging indicated by the two aging measures. We conclude that life course circumstances contribute differently to the acceleration of phenotypic and functional aging in Chinese middle-aged and older adults. Special attention should be given to promoting health for the disadvantaged subpopulation and narrowing their health gap with advantaged counterparts. Our findings highlight the role of life course circumstances in ameliorating health inequalities in late life.
Generation of CD34+CD43+ hematopoietic progenitors to induce thymocytes from human pluripotent stem cells
Authors: Flippe Léa , Gaignerie Anne , Sérazin Céline , Baron Olivier , Saulquin Xavier , Anegon Ignacio , David Laurent , Guillonneau Carole 
Published: September 2021

ABSTRACTImmunotherapy using primary T cells has revolutionized medical care in some pathologies in recent years but limitations associated to challenging cell genome edition, insufficient cell number production, the use of only autologous cells and lack of product standardization have limited its uses in the clinic. The alternative use of T cells generated in vitro from human pluripotent stem cells (hPSCs) offers great advantages by providing a self-renewing source of T cells that can be readily genetically modified and facilitate the use of standardized universal off-the-shelf allogeneic cell products and rapid clinic access. However, despite their potential, the feasibility and functionality of T-cells differentiated from hPSCs needs better comprehension before moving to the clinic. In this study, we generated human induced pluripotent stem cells from T-cells (T-iPSCs) allowing preservation of already recombined TCR, with the same properties as human embryonic stem cells (hESCs). Based on these cells, we differentiated with high efficiency hematopoietic progenitor stem cells (HPSCs), capable of self-renewal and differentiation into any cell blood type, and then DN3a thymic progenitors from several T-iPSC lines. To better comprehend differentiation, we analyzed the transcriptomic profiles of the different cell types and demonstrated that HPSCs differentiated from hiPSCs had a very similar profile to cord blood hematopoietic stem cells (HSCs). Furthermore, differentiated T-cell progenitors had a similar profile to thymocytes at the DN3a stage of thymic lymphopoiesis. Therefore, with this approach, we were able to regenerate precursors of therapeutic human T cells to potentially treat a wide number of diseases.
Genetic, metabolic, and molecular insights into the diverse outcomes of diet-induced obesity in mice
Authors: Bachmann Alexis Maximilien , Morel Jean-David , El Alam Gaby , Rodríguez-López Sandra , de lima Tanes Imamura , Goeminne Ludger J.E. , Benegiamo Giorgia , Bou Sleiman Maroun , Auwerx Johan 
Published: September 2021

AbstractOverweight and obesity are increasingly common public health issues worldwide, leading to a wide range of diseases from metabolic syndrome to steatohepatitis and cardiovascular diseases. While the increase in the prevalence of obesity is partly attributable to changes in lifestyle (i.e. increased sedentarity and changes in eating behaviour), the metabolic and clinical impacts of these obesogenic conditions varies between sexes and genetic backgrounds. The conception of personalised treatments of obesity and its complications require a thorough understanding of the diversity of responses to conditions such as high-fat diet intake. By analysing nine genetically diverse mouse strains, we show that much like humans, mice respond to high-fat diet in a genetic- and sex-dependent manner. Physiological and molecular responses to high-fat diet are associated with expression of genes involved in immunity and mitochondrial function. Finally, we find that mitochondrial function may explain part of the diversity of physiological responses. By exploring the complex interactions between genetics and metabolic phenotypes via gene expression and molecular traits, we shed light on the importance of genetic background and sex in determining metabolic outcomes. In addition to providing the community with an extensive resource for optimizing future experiments, our work serves as an exemplary design for more generalizable translational studies.
Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches
Authors: Cheng Xiaolong , Li Zexu , Shan Ruocheng , Li Zihan , Chao Lumen , Peng Jian , Fei Teng , Li Wei 
Published: September 2021

AbstractA major challenge in the application of the CRISPR-Cas13d (RfxCas13d, or CasRx) RNA editing system is to accurately predict its guide RNA (gRNA) dependent on-target and off-target effect. Here, we performed CRISPR-Cas13d proliferation screens that target protein-coding genes and long non-coding RNAs (lncRNAs), followed by a systematic modeling of Cas13d on-target efficiency and off-target viability effect. We first designed a deep learning model, named DeepCas13, to predict the on-target activity of a gRNA with high accuracy from its sequence and secondary structure. DeepCas13 outperforms existing methods and accurately predicts the efficiency of guides targeting both protein-coding and non-coding RNAs (e.g., circRNAs and lncRNAs). Next, we systematically studied guides targeting non-essential genes, and found that the off-target viability effect, defined as the unintended effect of guides on cell viability, is closely related to their on-target RNA cleavage efficiency. This finding suggests that these gRNAs should be used as negative controls in proliferation screens to reduce false positives, possibly coming from the unwanted off-target viability effect of efficient guides. Finally, we applied these models to our screens that included guides targeting 234 lncRNAs, and identified lncRNAs that affect cell viability and proliferation in multiple cell lines. DeepCas13 is freely accessible via http://deepcas13.weililab.org.
MOSCATO: A Supervised Approach for Analyzing Multi-Omic Single-Cell Data
Authors: Towle-Miller Lorin M , Miecznikowski Jeffrey C 
Published: September 2021

AbstractAdvancements in genomic sequencing continually improve personalized medicine in complex diseases. Recent breakthroughs generate multiple types of signatures (or multi-omics) from each cell, producing different data ‘omic’ types per single-cell experiment. We introduce MOSCATO, a technique for selecting features across multi-omic single-cell datasets that relate to clinical outcomes. For example, we leverage penalization concepts often used in multi-omic network analytics to accommodate the high-dimensionality where multiple-testing is likely underpowered. We organize the data into multi-dimensional tensors where the dimensions correspond to the different ‘omic’ types. Using the outcome and the single-cell tensors, we perform regularized tensor regression to return a variable set for each ‘omic’ type that forms the clinically-associated network. Robustness is assessed over simulations based on available single-cell simulation methods. Real data comparing healthy subjects versus subjects with leukemia is also considered in order to identify genes associated with the disease. The flexibility of our approach enables future extensions on distributional assumptions and covariate adjustments. This algorithm may identify clinically-relevant genetic patterns on a cellular-level that span multiple layers of sequencing data and ultimately inform highly precise therapeutic targets in complex diseases. Code to perform MOSCATO and replicate the real data application is publicly available on GitHub athttps://github.com/lorinmil/MOSCATOandhttps://github.com/lorinmil/MOSCATOLeukemiaExample.
KIRCLE: An Analysis of Variations in KIR Genes in The Cancer Genome Atlas and UK Biobank
Authors: Gao Galen F. , Liu Dajiang , Zhan Xiaowei , Li Bo 
Published: September 2021

AbstractBackgroundNatural killer (NK) cells represent a critical component of the innate immune system’s response against cancer and viral infections, among other diseases. To distinguish healthy host cells from infected or tumor cells, killer immunoglobulin receptors (KIR) on NK cells bind and recognize Human Leukocyte Antigen (HLA) complexes on their target cells. Just like the HLAs they bind, these KIRs exhibit high allelic diversity in the human population.ResultsIn order to better understand these immunoreceptors, we have developed KIRCLE, a novel method for genotyping individual KIR genes from whole exome sequencing data, and used it to analyze approximately 60,000 patient samples in The Cancer Genome Atlas and UK Biobank. We were able to assess population frequencies for different KIR alleles and demonstrate that, similar to HLA alleles, individuals’ KIR alleles correlate strongly with their ethnicities. In addition, we observed associations between different KIR alleles and HLA alleles, including HLA-B*53 with KIR3DL2*013 (Fisher’s Exact FDR = 7.64e-51). Finally, we showcased statistically significant associations between KIR alleles and various clinical correlates, including peptic ulcer disease (Fisher’s Exact FDR = 0.0429) and age of onset of atopy and various KIR alleles (Mann-Whitney-U FDR = 0.0751).ConclusionsKIR polymorphism and NK cells play a critical role in many diseases, often through their interactions with HLA complexes. Peptic ulcer disease and atopy are just two diseases in which NK cells may play a role beyond their “classical” realm of anti-tumor and anti-viral responses.
Dockground scoring benchmarks for protein docking
Authors: Kotthoff Ian , Kundrotas Petras J. , Vakser Ilya A. 
Published: September 2021

AbstractProtein docking protocols typically involve global docking scan, followed by re-ranking of the scan predictions by more accurate scoring functions that are either computationally too expensive or algorithmically impossible to include in the global scan. Development and validation of scoring methodologies are often performed on scoring benchmark sets (docking decoys) which offer concise and nonredundant representation of the global docking scan output for a large and diverse set of protein-protein complexes. Two such protein-protein scoring benchmarks were built for the Dockground resource, which contains various datasets for the development and testing of protein docking methodologies. One set was generated based on the Dockground unbound docking benchmark 4, and the other based on protein models from the Dockground model-model benchmark 2. The docking decoys were designed to reflect the reality of the real-case docking applications (e.g., correct docking predictions defined as near-native rather than native structures), and to minimize applicability of approaches not directly related to the development of scoring functions (reducing clustering of predictions in the binding funnel and disparity in structural quality of the near-native and non-native matches). The sets were further characterized by the source organism and the function of the protein-protein complexes. The sets, freely available to the research community on the Dockground webpage, present a unique, user-friendly resource for the developing and testing of protein-protein scoring approaches.