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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Scalf, Cassandra; Chariker, Julia; Rouchka, Eric; Ashley, Noah;

    Upregulated DEGs in hypothalamus. Raw DEG data for upregulated genes in hypothalamus. (XLSX 344 kb)

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ figsharearrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    figshare
    Dataset . 2019
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Dataset . 2019
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ figsharearrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Dataset . 2019
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Dataset . 2019
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Y.-T, Huang; A., Steptoe; R., S.Patel; E., FullerThomson; +1 Authors

    Introduction Older adults are more vulnerable to COVID-19 infections; however, little is known about which comorbidity patterns are related to a higher risk of COVID-19 infection. This study investigated the role of long-term conditions or comorbidity patterns on COVID-19 infection and related hospitalisations. Methods This study included 4,428 individuals from Waves 8 (2016−2017) and 9 (2018−2019) of the English Longitudinal Study of Ageing (ELSA) who also participated in the ELSA COVID-19 Substudy in 2020. Comorbidity patterns were identified using an agglomerative hierarchical clustering method. The relationships between comorbidity patterns or long-term conditions and COVID-19 related outcomes were examined using multivariable logistic regression. Results Among a representative sample of community-dwelling older adults in England, those with cardiovascular disease (CVD) and complex comorbidities had an almost double risk of COVID-19 infection (OR=1.87, 95%CI=1.42−2.46) but not of COVID-19 related hospitalisation. A similar pattern was observed for the heterogeneous comorbidities cluster (OR=1.56, 95%CI=1.24−1.96). The individual investigations of long-term conditions with COVID-19 infection highlighted primary associations with CVD (OR=1.46, 95%CI=1.23−1.74), lung diseases (OR=1.40, 95%CI=1.17−1.69), psychiatric conditions (OR=1.40, 95%CI=1.16−1.68), retinopathy/eye diseases (OR=1.39, 95%CI=1.18−1.64), and arthritis (OR=1.27, 95%CI=1.09−1.48). In contrast, metabolic disorders and diagnosed diabetes were not associated with any COVID-19 outcomes. Discussion/Conclusion This study provides novel insights into the comorbidity patterns that are more vulnerable to COVID-19 infections and hospitalisations, highlighting the vulnerability of those with CVD and other complex comorbidities. These findings facilitate crucial new evidence that should be considered for appropriate screening measures and tailored interventions for older adults in the ongoing global outbreak.

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    Dataset . 2023
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Dietrich, Muriel; Kearney, Teresa; Seamark, Ernest C.J.; Paweska, Janusz T.; +1 Authors

    Seasonal reproduction is a period of extreme physiological and behavioral changes, yet we know little about how it may affect host microbial communities (i.e. microbiota) and pathogen transmission. Here, we investigated shifts of the bacterial microbiota in saliva, urine and faeces during the seasonal reproduction of bats in South Africa, and test for an interaction in shedding patterns of both bacterial (Leptospira) and viral (adeno- and herpes-viruses) agents. Based on a comparative approach in two cave-dwelling bat species and high-throughput sequencing of the 16S rRNA gene, we demonstrated a clear signature in microbiota changes over the reproduction season, consistent across the multiple body habitats investigated, and associated with sex, age and reproductive condition of bats. We observed in parallel highly dynamic shedding patterns for both bacteria and viruses, but did not find a significant association between viral shedding and bacterial microbiota composition. Indeed, only Leptospira shedding was associated with alterations in both the diversity and composition of the urinary microbiota. These results illustrate how seasonal reproduction in bats substantially affects microbiota composition and infection dynamics, and have broad implications for the understanding of disease ecology in important reservoir hosts, such as bats. Rousettus_SampleList_SalivaList and details of Rousettus samples.Miniopterus_SampleList_Saliva-Urine-FaecesList and details of Miniopterus samples.Rousettus fasta DryadA zip folder with fasta files for Rousettus.Miniopterus fasta dryadA zip folder with fasta files for Miniopterus.Rousettus_Saliva_OTUsOTU table for Rousettus saliva samples.Miniopterus_Saliva_OTUsOTU table for Miniopterus saliva samples.Miniopterus_Faeces_OTUsOTU table for Miniopterus faeces samples.Miniopterus_Urine_OTUsOTU table for Miniopterus urine samples.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DRYAD; NARCISarrow_drop_down
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    DRYAD; NARCIS
    Dataset . 2018
    License: CC 0
    Data sources: Datacite; NARCIS
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    ZENODO
    Dataset . 2018
    License: CC 0
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DANS-EASY
    Dataset . 2018
    Data sources: B2FIND
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DRYAD; NARCISarrow_drop_down
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      DRYAD; NARCIS
      Dataset . 2018
      License: CC 0
      Data sources: Datacite; NARCIS
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2018
      License: CC 0
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DANS-EASY
      Dataset . 2018
      Data sources: B2FIND
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Dudas, Gytis; Bedford, Trevor;

    Additional file 5 Maximum likelihood inference of masked sequence location from genomes (left) and GP sequences (right) via a CTMC model implemented in TreeTime. Horizontal bars indicate the posterior distribution of masked tip locations coloured by country (Sierra Leone in blue, Liberia in red, Guinea in green) and location (lighter colours indicate administrative divisions lying towards west of the country). The correct location of each tip is outlined in white with the smaller plot to the right showing only the probability of the correct location. Bars marked with an open circle indicate cases where the correct location is within the 95% credible set and solid circles indicate cases where the location with the most probability is also the correct location. Genomes still perform better in terms of correct guess (0.432 probability that best guess location is true location for genomes versus 0.259 for GP), cross entropy (12012.800 nats for genome versus 24397.109 nats for GP) and mean probability-weighted great circle distance between true location population centroid and estimated location population centroid (87.568 km for genome versus 124.909 km for GP).

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    Image . 2019
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Image . 2019
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ figsharearrow_drop_down
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      Image . 2019
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Image . 2019
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Grodzki, Marco; Bluhm, Andrew P.; Schaefer, Moritz; Tagmount, Abderrahmane; +6 Authors

    Additional file 8. HEK293T-Ace2 OC43 CRISPR study analysis.

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    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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    Authors: Borcherding, Nicholas; Mudd, Philip;

    Abstract: SARS-CoV-2 infection and mRNA vaccination induce robust CD4+ T cell responses that are critical for the development of protective immunity. Here, we evaluated spike-specific CD4+ T cells in the blood and draining lymph node (dLN) of human subjects following BNT162b2 mRNA vaccination using single-cell transcriptomics. We analyze multiple spike-specific CD4+ T cell clonotypes, including novel clonotypes we define here using Trex, a new deep learning-based reverse epitope mapping method integrating single-cell T cell receptor (TCR) sequencing and transcriptomics to predict antigen-specificity. Human dLN spike-specific T follicular helper cells (TFH) exhibited distinct phenotypes, including germinal center (GC)-TFH and IL-10+ TFH, that varied over time during the GC response. Paired TCR clonotype analysis revealed tissue-specific segregation of circulating and dLN clonotypes, despite numerous spike-specific clonotypes in each compartment. Analysis of a separate SARS-CoV-2 infection cohort revealed circulating spike-specific CD4+ T cell profiles distinct from those found following BNT162b2 vaccination. Our findings provide an atlas of human antigen-specific CD4+ T cell transcriptional phenotypes in the dLN and blood following vaccination or infection. More Information:The preprint is with the analysis of the data is available at Research Square.Sample information is available in the data_inventory.csv file.

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    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2023
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2023
      License: CC BY
      Data sources: Datacite
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    Authors: Bükülmez, Ayşegül; Baş, Melike Taşdelen; Çiftci, Esra;

    ABSTRACT BACKGROUND: Coronavirus disease 2019 (COVID-19) causes negative life changes brought about through lockdowns, in addition to severe complications and death. Among these changes, asplenism or hyposplenism has been reported in patients with celiac disease. It has been reported that the risk of pneumococcal sepsis is higher in celiac patients with hyposplenism. Moreover, celiac patients present high risk of admission to hospital due to influenza. OBJECTIVE: To determine the degree of awareness of COVID-19 among parents of children with celiac disease and examine the measures that they take. DESIGN AND SETTING: Cross-sectional study at a university hospital in the Middle Anatolian region of Turkey. METHODS: The diagnosis of celiac disease was confirmed through a survey conducted online among 73 parents between May and July 2020. RESULTS: The mean age was 37.57 ± 6.56 years for the mothers, 41.15 ± 5.56 years for the fathers and 11.36 ± 4.36 years for the children. 90.4% of the parents reported that COVID-19 was transmitted through “speaking, coughing, sneezing and infection of the face after contact with virus-infected surfaces”. Moreover, 78.1% indicated that they did not have any difficulty in finding gluten-free foods. CONCLUSION: These parents of children with celiac disease believed that their children’s risk of developing COVID-19 did not differ from that of healthy children. It was also observed that appetite and states of nervousness were higher among these children with celiac disease during lockdowns and that their sleep patterns were affected.

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    Authors: Santee, Clark; Nabeetha Nagalingam; Faruqi, Ali; DeMuri, Gregory; +3 Authors

    Normalized fluorescent intensity for Moraxella nonliquefaciens (eOTU 457) plotted for each subject showing significant differences (Welch’s t test, P ≤ 0.05) between (A) children with a prior history of acute sinusitis and those without a prior history of acute sinusitis as well as (B) children who developed acute sinusitis during the year following sample collection and those who did not. (TIFF 340 kb)

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    Authors: Rosenbaum, Janet E.; Jennings, Jacky; Ellen, Jonathan M.; Borkovic, Laurel M.; +3 Authors

    Additional file 1: Supplementary Material 1. Stata and R code used for analysis. This text file includes the Stata and R code used to produced the results in the paper.

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    Authors: Tiee, Madeline S.; Harrigan, Ryan J.; Thomassen, Henri A.; Smith, Thomas B.;

    Data organized by species with 95% CI

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    Dataset . 2018
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Scalf, Cassandra; Chariker, Julia; Rouchka, Eric; Ashley, Noah;

    Upregulated DEGs in hypothalamus. Raw DEG data for upregulated genes in hypothalamus. (XLSX 344 kb)

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    Dataset . 2019
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    Data sources: Datacite
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    Dataset . 2019
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      Dataset . 2019
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Y.-T, Huang; A., Steptoe; R., S.Patel; E., FullerThomson; +1 Authors

    Introduction Older adults are more vulnerable to COVID-19 infections; however, little is known about which comorbidity patterns are related to a higher risk of COVID-19 infection. This study investigated the role of long-term conditions or comorbidity patterns on COVID-19 infection and related hospitalisations. Methods This study included 4,428 individuals from Waves 8 (2016−2017) and 9 (2018−2019) of the English Longitudinal Study of Ageing (ELSA) who also participated in the ELSA COVID-19 Substudy in 2020. Comorbidity patterns were identified using an agglomerative hierarchical clustering method. The relationships between comorbidity patterns or long-term conditions and COVID-19 related outcomes were examined using multivariable logistic regression. Results Among a representative sample of community-dwelling older adults in England, those with cardiovascular disease (CVD) and complex comorbidities had an almost double risk of COVID-19 infection (OR=1.87, 95%CI=1.42−2.46) but not of COVID-19 related hospitalisation. A similar pattern was observed for the heterogeneous comorbidities cluster (OR=1.56, 95%CI=1.24−1.96). The individual investigations of long-term conditions with COVID-19 infection highlighted primary associations with CVD (OR=1.46, 95%CI=1.23−1.74), lung diseases (OR=1.40, 95%CI=1.17−1.69), psychiatric conditions (OR=1.40, 95%CI=1.16−1.68), retinopathy/eye diseases (OR=1.39, 95%CI=1.18−1.64), and arthritis (OR=1.27, 95%CI=1.09−1.48). In contrast, metabolic disorders and diagnosed diabetes were not associated with any COVID-19 outcomes. Discussion/Conclusion This study provides novel insights into the comorbidity patterns that are more vulnerable to COVID-19 infections and hospitalisations, highlighting the vulnerability of those with CVD and other complex comorbidities. These findings facilitate crucial new evidence that should be considered for appropriate screening measures and tailored interventions for older adults in the ongoing global outbreak.

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    Dataset . 2023
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    Authors: Dietrich, Muriel; Kearney, Teresa; Seamark, Ernest C.J.; Paweska, Janusz T.; +1 Authors

    Seasonal reproduction is a period of extreme physiological and behavioral changes, yet we know little about how it may affect host microbial communities (i.e. microbiota) and pathogen transmission. Here, we investigated shifts of the bacterial microbiota in saliva, urine and faeces during the seasonal reproduction of bats in South Africa, and test for an interaction in shedding patterns of both bacterial (Leptospira) and viral (adeno- and herpes-viruses) agents. Based on a comparative approach in two cave-dwelling bat species and high-throughput sequencing of the 16S rRNA gene, we demonstrated a clear signature in microbiota changes over the reproduction season, consistent across the multiple body habitats investigated, and associated with sex, age and reproductive condition of bats. We observed in parallel highly dynamic shedding patterns for both bacteria and viruses, but did not find a significant association between viral shedding and bacterial microbiota composition. Indeed, only Leptospira shedding was associated with alterations in both the diversity and composition of the urinary microbiota. These results illustrate how seasonal reproduction in bats substantially affects microbiota composition and infection dynamics, and have broad implications for the understanding of disease ecology in important reservoir hosts, such as bats. Rousettus_SampleList_SalivaList and details of Rousettus samples.Miniopterus_SampleList_Saliva-Urine-FaecesList and details of Miniopterus samples.Rousettus fasta DryadA zip folder with fasta files for Rousettus.Miniopterus fasta dryadA zip folder with fasta files for Miniopterus.Rousettus_Saliva_OTUsOTU table for Rousettus saliva samples.Miniopterus_Saliva_OTUsOTU table for Miniopterus saliva samples.Miniopterus_Faeces_OTUsOTU table for Miniopterus faeces samples.Miniopterus_Urine_OTUsOTU table for Miniopterus urine samples.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DRYAD; NARCISarrow_drop_down
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    DRYAD; NARCIS
    Dataset . 2018
    License: CC 0
    Data sources: Datacite; NARCIS
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2018
    License: CC 0
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DANS-EASY
    Dataset . 2018
    Data sources: B2FIND
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DRYAD; NARCISarrow_drop_down
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      DRYAD; NARCIS
      Dataset . 2018
      License: CC 0
      Data sources: Datacite; NARCIS
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2018
      License: CC 0
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DANS-EASY
      Dataset . 2018
      Data sources: B2FIND
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    Authors: Dudas, Gytis; Bedford, Trevor;

    Additional file 5 Maximum likelihood inference of masked sequence location from genomes (left) and GP sequences (right) via a CTMC model implemented in TreeTime. Horizontal bars indicate the posterior distribution of masked tip locations coloured by country (Sierra Leone in blue, Liberia in red, Guinea in green) and location (lighter colours indicate administrative divisions lying towards west of the country). The correct location of each tip is outlined in white with the smaller plot to the right showing only the probability of the correct location. Bars marked with an open circle indicate cases where the correct location is within the 95% credible set and solid circles indicate cases where the location with the most probability is also the correct location. Genomes still perform better in terms of correct guess (0.432 probability that best guess location is true location for genomes versus 0.259 for GP), cross entropy (12012.800 nats for genome versus 24397.109 nats for GP) and mean probability-weighted great circle distance between true location population centroid and estimated location population centroid (87.568 km for genome versus 124.909 km for GP).

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    figshare
    Image . 2019
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    figshare
    Image . 2019
    License: CC BY
    Data sources: Datacite
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