Objective: To rapidly increase childhood immunization through a preventive, multi-antigen, vaccination campaign in Mambéré-Kadéï prefecture, Central African Republic, where a conflict from 2012 to 2015 reduced vaccination coverage.
Methods: The three-round campaign took place between December 2015 and June 2016 using: (i) oral poliomyelitis vaccine (OPV); (ii) combined diphtheria, tetanus and pertussis (DTP) vaccine, type B (Hib) and hepatitis B (DTP-Hib-hepatitis B) vaccine; (iii) pneumococcal conjugate vaccine (PCV); (iv) measles vaccine; and (v) yellow fever vaccine. Administrative data were collected on vaccines administered by age group and vaccination coverage surveys were carried out before and after the campaign.
Findings: Overall, 294 054 vaccine doses were administered. Vaccination coverage for children aged 6 weeks to 59 months increased to over 85% for the first doses of OPV, DTP-Hib-hepatitis B vaccine and PCV and, in children aged 9 weeks to 59 months, to over 70% for the first measles vaccine dose. In children aged 6 weeks to 23 months, coverage of the second doses of OPV, DTP-Hib-hepatitis B vaccine and PCV was over 58% and coverage of the third doses of OPV and DTP-Hib-hepatitis B vaccine was over 20%. Moreover, 61% (5804/9589) of children aged 12 to 23 months had received two PCV doses and 90% (25933/28764) aged 24 to 59 months had received one dose.
Conclusion: A preventive, multi-antigen, vaccination campaign was effective in rapidly increasing immunization coverage in a post-conflict setting. To sustain high coverage, routine immunization must be reinforced.
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http://dx.doi.org/10.2471/BLT.17.204321 | DOI Listing |
Sci Rep
December 2024
Plum Island Animal Disease Center, Agricultural Research Service, USDA, Greenport, NY, 11944, USA.
For over a century African swine fever (ASF) has been causing outbreaks leading to devastating losses for the swine industry. The current pandemic of ASF has shown no signs of stopping and continues to spread causing outbreaks in additional countries. Currently control relies mostly on culling infected farms, and strict biosecurity procedures.
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December 2024
Department of Mathematics, GC University, Lahore, Pakistan.
In this article, a nonlinear fractional bi-susceptible [Formula: see text] model is developed to mathematically study the deadly Coronavirus disease (Covid-19), employing the Atangana-Baleanu derivative in Caputo sense (ABC). A more profound comprehension of the system's intricate dynamics using fractional-order derivative is explored as the primary focus of constructing this model. The fundamental properties such as positivity and boundedness, of an epidemic model have been proven, ensuring that the model accurately reflects the realistic behavior of disease spread within a population.
View Article and Find Full Text PDFNat Commun
December 2024
Division of Rheumatology and Clinical Immunology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
Antibody-mediated protection against pathogens is crucial to a healthy life. However, the recent SARS-CoV-2 pandemic has shown that pre-existing comorbid conditions including kidney disease account for compromised humoral immunity to infections. Individuals with kidney disease are not only susceptible to infections but also exhibit poor vaccine-induced antibody response.
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December 2024
Laboratory of Aging Research and Cancer Drug Target, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
The immune escape capacities of XBB variants necessitate the authorization of vaccines with these antigens. In this study, we produce three recombinant trimeric proteins from the RBD sequences of Delta, BA.5, and XBB.
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December 2024
Oncology Bioinformatics, Genentech, South San Francisco, CA, USA.
Based on the success of cancer immunotherapy, personalized cancer vaccines have emerged as a leading oncology treatment. Antigen presentation on MHC class I (MHC-I) is crucial for the adaptive immune response to cancer cells, necessitating highly predictive computational methods to model this phenomenon. Here, we introduce HLApollo, a transformer-based model for peptide-MHC-I (pMHC-I) presentation prediction, leveraging the language of peptides, MHC, and source proteins.
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