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  • Open Access
    Authors: 
    Julius Beer; Stefania Crotta; Angele Breithaupt; Annette Ohnemus; Jan Becker; Benedikt Sachs; Lisa Kern; Miriam Llorian; Nadine Ebert; Fabien Labroussaa; +10 more
    Publisher: Rockefeller Univ. Press
    Country: Switzerland

    AbstractSARS-CoV-2 is a highly contagious respiratory virus and the causative agent for COVID-19. The severity of disease varies from mildly symptomatic to lethal and shows an extraordinary correlation with increasing age, which represents the major risk factor for severe COVID-191. However, the precise pathomechanisms leading to aggravated disease in the elderly are currently unknown. Delayed and insufficient antiviral immune responses early after infection as well as dysregulated and overshooting immunopathological processes late during disease were suggested as possible mechanisms. Here we show that the age-dependent increase of COVID-19 severity is caused by the disruption of a timely and well-coordinated innate and adaptive immune response due to impaired interferon (IFN) responses. To overcome the limitations of mechanistic studies in humans, we generated a mouse model for severe COVID-19 and compared the kinetics of the immune responses in adult and aged mice at different time points after infection. Aggravated disease in aged mice was characterized by a diminished IFN-γ response and excessive virus replication. Accordingly, adult IFN-γ receptor-deficient mice phenocopied the age-related disease severity and supplementation of IFN-γ reversed the increased disease susceptibility of aged mice.Mimicking impaired type I IFN immunity in adult and aged mice, a second major risk factor for severe COVID-192–4, we found that therapeutic treatment with IFN-λ in adult and a combinatorial treatment with IFN-γ and IFN-λ in aged Ifnar1-/-mice was highly efficient in protecting against severe disease.Our findings provide an explanation for the age-dependent disease severity of COVID-19 and clarify the nonredundant antiviral functions of type I, II and III IFNs during SARS-CoV-2 infection in an age-dependent manner. Based on our data, we suggest that highly vulnerable individuals combining both risk factors, advanced age and an impaired type I IFN immunity, may greatly benefit from immunotherapy combining IFN-γ and IFN-λ.

  • Open Access
    Authors: 
    Dawei Yang; Tao Xu; Xun Wang; Deng Chen; Ziqiang Zhang; Lichuan Zhang; Jie Liu; Kui Xiao; Li Bai; Yong Zhang; +24 more
    Publisher: Elsevier BV

    AbstractBackgroundThe outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. It causes acute respiratory distress syndrome and results in a high mortality rate if pneumonia is involved. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans, which facilitates the spread of the disease at the community level, and contributes to the overwhelming of medical resources in intensive care units.GoalThis study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist global frontline doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result.MethodsWith online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan Unversity (approval number B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. These patients had SARS-CoV-2 RT-PCR test results and chest CT scans, both of which were used as the gold standard for the diagnosis of COVID-19 and COVID-19 pneumonia. In particular, the dataset included 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, and 122 asymptomatic cases who had positive RT-PCR test results, amongst whom 31 cases were diagnosed. We also integrated the function of a survey in nCapp to collect user feedback from frontline doctors.FindingsWe applied the statistical method of a multi-factor regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are fast and accessible: ‘Residing or visiting history in epidemic regions’, ‘Exposure history to COVID-19 patient’, ‘Dry cough’, ‘Fatigue’, ‘Breathlessness’, ‘No body temperature decrease after antibiotic treatment’, ‘Fingertip blood oxygen saturation ≤93%’, ‘Lymphopenia’, and ‘C-reactive protein (CRP) increased’. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). To ensure the sensitivity of the model, we used a cutoff value of 0.09. The sensitivity and specificity of the model were 98.0% (95% CI: 96.9%, 99.1%) and 17.3% (95% CI: 15.0%, 19.6%), respectively, in the training dataset, and 96.5% (95% CI: 95.1%, 98.0%) and 18.8% (95% CI: 16.4%, 21.2%), respectively, in the validation dataset. In the subset of the 137 indeterminate cases who initially did not have RT-PCR tests and subsequently had positive RT-PCR results, the model predicted 132 cases, accounting for 96.4% (95% CI: 91.7%, 98.8%) of the cases. In the subset of the 62 suspected cases who initially had false-negative RT-PCR test results and subsequently had positive RT-PCR results, the model predicted 59 cases, accounting for 95.2% (95% CI: 86.5%, 99.0%) of the cases. Considering the specificity of the model, we used a cutoff value of 0.32. The sensitivity and specificity of the model were 83.5% (95% CI: 80.5%, 86.4%) and 83.2% (95% CI: 80.9%, 85.5%), respectively, in the training dataset, and 79.6% (95% CI: 76.4%, 82.8%) and 81.3% (95% CI: 78.9%, 83.7%), respectively, in the validation dataset, which is very close to the published AI model.The results of the online survey ‘Questionnaire Star’ showed that 90.9% of nCapp users in WeChat mini programs were ‘satisfied’ or ‘very satisfied’ with the tool. The WeChat mini program received a significantly higher satisfaction rate than other platforms, especially for ‘availability and sharing convenience of the App’ and ‘fast speed of log-in and data entry’.DiscussionWith the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results. These patients require timely isolation or close medical supervision. By applying the model, medical resources can be allocated more reasonably, and missed diagnoses can be reduced. In addition, further education and interaction among medical professionals can improve the diagnostic efficiency for COVID-19, thus avoiding the transmission of the disease from asymptomatic patients at the community level.

  • Open Access
    Authors: 
    Mert Golcuk; Ahmet Yildiz; Mert Gur;
    Publisher: Cold Spring Harbor Laboratory

    ABSTRACTSARS-CoV-2 infection is initiated by binding of the receptor-binding domain (RBD) of its spike glycoprotein to the peptidase domain (PD) of angiotensin-converting enzyme 2 (ACE2) receptors in host cells. Recently detected Omicron variant of SARS-CoV-2 (B.1.1.529) is heavily mutated on RBD. Currently, the most common Omicron variants are the original BA.1 Omicron strain and the BA.2 variant, which became more prevalent since it first appeared. To investigate how these mutations affect RBD-PD interactions, we performed all-atom molecular dynamics simulations of the BA.1 and BA.2 RBD-PD in the presence of full-length glycans, explicit water and ions. Simulations revealed that RBDs of BA.1 and BA.2 variants exhibit a more dispersed interaction network and make an increased number of salt bridges and hydrophobic interactions with PD compared to wild-type RBD. Although BA.1 and BA.2 differ in two residues at the RBD-ACE2 interface, no major difference in RBD-PD interactions and binding strengths were observed between these variants. Using the conformations sampled in each trajectory, the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) method estimated ~34% and ~51% stronger binding free energies for BA.1 and BA.2 RBD, respectively, than wild-type RBD, which may result in higher binding efficiency of the Omicron variant to infect host cells.

  • Open Access
    Authors: 
    Ravikiran Keshavamurthy; Samuel Dixon; Karl T. Pazdernik; Lauren E. Charles;
    Publisher: Elsevier BV

    AbstractDespite the complex and unpredictable nature of pathogen occurrence, substantial efforts have been made to better predict infectious diseases (IDs). Following PRISMA guidelines, we conducted a systematic review to investigate the advances in ID prediction capabilities for human and animal diseases, focusing on Machine Learning (ML) and Deep Learning (DL) techniques. Between January 2001 and May 2021, the number of relevant articles published steadily increased with a significantly influx after January 2019. Among the 237 articles included, a variety of IDs and locations were modeled, with the most common being COVID-19 (37.1%) followed by Influenza/influenza-like illnesses (8.9%) and Eastern Asia (32.5%) followed by North America (17.7%), respectively. Tree-based ML models (38.4%) and feed-forward DL neural networks (26.6%) were the most frequent approaches taking advantage of a wide variety of input features. Most articles contained models predicting temporal incidence (66.7%) followed by disease risk (38.0%) and spatial movement (31.2%). Less than 10% of studies addressed the concepts of uncertainty quantification, computational efficiency, and missing data, which are essential to operational use and deployment. Our study summarizes the broad aspects and current status of ID prediction capabilities and provides guidelines for future works to better support biopreparedness and response.

  • Open Access English
    Authors: 
    Ikrame Aknouch; Adithya Sridhar; Eline Freeze; Francesca Paola Giugliano; Britt J van Keulen; Michelle Romijn; Carlemi Calitz; Inés García-Rodríguez; Lance Mulder; Manon E Wildenberg; +6 more
    Country: Netherlands
    Project: EC | OrganoVIR (812673)

    Human milk is important for antimicrobial defense in infants and has well demonstrated antiviral activity. We evaluated the protective ability of human milk against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in a human fetal intestinal cell culture model. We found that, in this model, human milk blocks SARS-CoV-2 replication, irrespective of the presence of SARS-CoV-2 spike-specific antibodies. Complete inhibition of both enveloped Middle East Respiratory Syndrome Coronavirus and human respiratory syncytial virus infections was also observed while no inhibition of non-enveloped enterovirus A71 infection was seen. Transcriptome analysis after 24h of the intestinal monolayers treated with human milk showed large transcriptomic changes from human milk treatment and subsequent analysis suggested that ATP1A1 downregulation by milk might be of importance. Inhibition of ATP1A1 blocked SARS-CoV-2 infection in our intestinal model, while no effect on EV-A71 infection was seen. Our data indicate that human milk has potent antiviral activity against particular (enveloped) viruses by potentially blocking the ATP1A1-mediated endocytic process. This work was funded under the OrganoVIR project (grant 812673) in the European Union's Horizon 2020 programme, the PPP allowance made available by Health~Holland, Top Sector Life Sciences and Health, to Amsterdam UMC, location Academic Medical Center to stimulate public-private partnerships, and funding from Stichting Steun Emma Kinderziekenhuis. The funders had no role in the design of the study, data analysis, writing of the manuscript, or in the decision to publish the results.

  • Open Access
    Authors: 
    Jabir Al Nahian; Abu Kaisar Mohammad Masum; Sheikh Abujar; Md. Jueal Mia;
    Publisher: Institute of Advanced Engineering and Science

    In this era, the moment has arrived to move away from disease as the primary emphasis of medical treatment. Although impressive, the multiple techniques that have been developed to detect the diseases. In this time, there are some types of diseases COVID-19, normal flue, migraine, lung disease, heart disease, kidney disease, diabetics, stomach disease, gastric, bone disease, autism are the very common diseases. In this analysis, we analyze disease symptoms and have done disease predictions based on their symptoms. We studied a range of symptoms and took a survey from people in order to complete the task. Several classification algorithms have been employed to train the model. Furthermore, performance evaluation matrices are used to measure the model's performance. Finally, we discovered that the part classifier surpasses the others. 11 pages, 6 figures, accepted in Bulletin of Electrical Engineering and Informatics Journal

  • Open Access English
    Authors: 
    Amandine Fillol; Esther McSween-Cadieux; Bruno Ventelou; Marie-Pier Larose; Ulrich Boris Nguemdjo Kanguem; Kadidiatou Kadio; Christian Dagenais; Valéry Ridde;
    Publisher: HAL CCSD
    Country: France

    International audience; Background: Epistemic injustices are increasingly decried in global health. This study aims to investigate whether the source of knowledge influences the perception of that knowledge and the willingness to use it in francophone African health policy-making context. Methods: The study followed a randomized experimental design in which participants were randomly assigned to one of seven policy briefs that were designed with the same scientific content but with different organizations presented as authors. Each organization was representative of financial, scientific or moral authority. For each type of authority, two organizations were proposed: one North American or European, and the other African. Results: The initial models showed that there was no significant association between the type of authority or the location of the authoring organization and the two outcomes (perceived quality and reported instrumental use). Stratified analyses highlighted that policy briefs signed by the African donor organization (financial authority) were perceived to be of higher quality than policy briefs signed by the North American/European donor organization. For both perceived quality and reported instrumental use, these analyses found that policy briefs signed by the African university (scientific authority) were associated with lower scores than policy briefs signed by the North American/European university. Conclusions: The results confirm the significant influence of sources on perceived global health knowledge and the intersectionality of sources of influence. This analysis allows us to learn more about organizations in global health leadership, and to reflect on the implications for knowledge translation practices.; Contexte : Les injustices épistémiques sont de plus en plus décriées dans le domaine de la santé mondiale. Cette étude vise à déterminer si la source des connaissances influence la perception de ces connaissances et la volonté de les utiliser. Méthodes : L’étude suit un devis expérimental randomisé dans lequel les participant·es ont été assigné·es au hasard à l'une des sept notes politiques conçues avec le même contenu scientifique, mais avec différentes organisations présentées comme autrices. Chaque organisation était représentative d'une autorité financière, scientifique ou morale. Pour chaque type d'autorité, deux organisations étaient proposées : l'une nord-américaine ou européenne, l'autre africaine. Résultats : Les résultats montrent que le type d’autorité et la localisation des organisations autrices ne sont pas significativement associés à la qualité perçue et à l’utilisation instrumentale déclarée. Toutefois, des interactions entre le type d’autorité et la localisation étaient significatives. Ainsi, les analyses stratifiées ont mis en évidence que pour la qualité perçue, les notes de politique signées par l'organisme bailleur (autorité financière) africain obtenaient de meilleurs scores que les notes de politique signées par l’organisme bailleur nord-américain / européen. Tant pour la qualité perçue que pour l'utilisation instrumentale déclarée, ces analyses stratifiées ont révélé que les notes de politique signées par l'université africaine (autorité scientifique) étaient associées à des scores plus faibles que les notes de politique signées par l'université nord-américaine/européenne. Interprétation : Les résultats confirment l'influence significative des sources sur la perception des connaissances en santé mondiale et rappellent l’intersectionnalité de l’influence des sources d’autorité. Cette analyse nous permet à la fois d'en apprendre davantage sur les organisations qui dominent la scène de la gouvernance mondiale en santé et de réfléchir aux implications pour les pratiques d'application des connaissances.

  • Open Access
    Authors: 
    Alissa Papadopoulos; Emily S. Nichols; Yalda Mohsenzadeh; Isabelle Giroux; Michelle F. Mottola; Ryan J. Van Lieshout; Emma G. Duerden;
    Publisher: Research Square Platform LLC
    Country: Canada

    Background: Rates of prenatal and postpartum stress and depression in pregnant individuals have increased during the COVID-19 pandemic. Perinatal maternal mental health has been linked to worse motor development in offspring, with motor deficits appearing in infancy and early childhood. We aimed to evaluate the relationship between prenatal and postpartum stress and depression and motor outcome in infants born during the COVID-19 pandemic. Methods: One hundred and seventeen participants completed an online prospective survey study at two timepoints: during pregnancy and within 2 months postpartum. Depression was self-reported using the Edinburgh Perinatal/Postpartum Depression Scale (EPDS), and stress via the Perceived Stress Scale (PSS). Mothers reported total infant motor ability (fine and gross) using the interRAI 0–3 Developmental Domains questionnaire. Results: Prenatal (EPDS median=10.0, interquartile range[IQR]=6.0 – 14.0, B=-0.035, 95%CI=-0.062 to -0.007, p = 0.014) and postpartum maternal depression outcomes (median=7, IQR=4–12, B=-0.037, 95%CI= -0.066 to -0.008, p = 0.012) were significantlynegatively associated with total infant motor ability. Neither pregnancy nor postpartum perceived stress was associated with infant motor function. A cluster analysis revealed that preterm and low-birth weight infants whose mothers reported elevated depressive symptoms during pregnancy and in the postpartum period had the poorest motor outcomes. Conclusions: Prenatal and postpartum depression, but not stress, was associated with early infant motor abilities. Preterm and low-birth weight infants whose mothers reported elevated depressive symptoms maybe at-risk of experiencing poor motor outcomes. These results highlight the importance of identifying pre- and postnatal maternal mental health issues, especially during the ongoing COVID-19 pandemic.

  • Open Access
    Authors: 
    Patrick M. D'Aoust; Xin Tian; Syeda Tasneem Towhid; Amy Xiao; Elisabeth Mercier; Nada Hegazy; Jian-Jun Jia; Shen Wan; Md Pervez Kabir; Wanting Fang; +18 more
    Publisher: Elsevier BV

    AbstractClinical testing has been the cornerstone of public health monitoring and infection control efforts in communities throughout the COVID-19 pandemic. With the extant and anticipated reduction of clinical testing as the disease moves into an endemic state, SARS-CoV-2 wastewater surveillance (WWS) is likely to have greater value as an important diagnostic tool to inform public health. As the widespread adoption of WWS is relatively new at the scale employed for COVID-19, interpretation of data, including the relationship to clinical cases, has yet to be standardized. An in-depth analysis of the metrics derived from WWS is required for public health units/agencies to interpret and utilize WWS-acquired data effectively and efficiently. In this study, the SARS-CoV-2 wastewater signal to clinical cases (WC) ratio was investigated across seven different cities in Canada over periods ranging from 8 to 21 months. Significant increases in the WC ratio occurred when clinical testing eligibility was modified to appointment-only testing, identifying a period of insufficient clinical testing in these communities. The WC ratio decreased significantly during the emergence of the Alpha variant of concern (VOC) in a relatively non-immunized community’s wastewater (40-60% allelic proportion), while a more muted decrease in the WC ratio signaled the emergence of the Delta VOC in a relatively well-immunized community’s wastewater (40-60% allelic proportion). Finally, a rapid and significant decrease in the WC ratio signaled the emergence of the Omicron VOC, likely because of the variant’s greater effectiveness at evading immunity, leading to a significant number of new reported clinical cases, even when vaccine-induced community immunity was high. The WC ratio, used as an additional monitoring metric, complements clinical case counts and wastewater signals as individual metrics in its ability to identify important epidemiological occurrences, adding value to WWS as a diagnostic technology during the COVID-19 pandemic and likely for future pandemics.

  • Publication . Article . Preprint . 2022
    Open Access
    Authors: 
    Michael Levitt; Francesco Zonta; John P.A. Ioannidis;
    Publisher: Zenodo

    ABSTRACTExcess death estimates have great value in public health, but they can be sensitive to analytical choices. Here we propose a multiverse analysis approach that considers all possible different time periods for defining the reference baseline and a range of 1 to 4 years for the projected time period for which excess deaths are calculated. We used data from the Human Mortality Database on 33 countries with detailed age-stratified death information on an annual basis during the period 2009-2021. The use of different time periods for reference baseline led to large variability in the absolute magnitude of the exact excess death estimates. However, the relative ranking of different countries compared to others for specific years remained largely unaltered. Averaging across all possible analyses, distinct time patterns were discerned across different countries. Countries had declines between 2009 and 2019, but the steepness of the decline varied markedly. There were also large differences across countries on whether the COVID-19 pandemic years 2020-2021 resulted in an increase of excess deaths and by how much. Consideration of longer projected time windows resulted in substantial shrinking of the excess deaths in many, but not all countries. Multiverse analysis of excess deaths over long periods of interest can offer a more unbiased approach to understand comparative mortality trends across different countries, the range of uncertainty around estimates, and the nature of observed mortality peaks.SIGNIFICANCE STATEMENTExcess death estimates are the ultimate assessment of the impact of multiple diseases and forces on the mortality of a population. However, their calculation can be notoriously unstable because it depends on a multitude of analytical choices. Other scientific fields have started using multiverse analysis approaches where all possible analytical choices are considered. We developed a multiverse analysis approach for excess death estimation. The approach is demonstrated with data from 33 countries for the period 2009-2021. Multiverse analysis offers a standardized way to demonstrate the sensitivity of estimates to analytic assumptions, to understand the presence of time patterns (rather than arbitrarily prespecify them), and to reveal consistent patterns that characterize excess deaths in a comparative fashion between different countries.

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