| Download | - View final version: Modeling of longitudinal immune profiles reveals distinct immunogenic signatures following five COVID-19 vaccinations among people living with HIV (PDF, 6.3 MiB)
- View supplementary information: Modeling of longitudinal immune profiles reveals distinct immunogenic signatures following five COVID-19 vaccinations among people living with HIV (ZIP, 29.9 MiB)
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| DOI | Resolve DOI: https://doi.org/10.1016/j.patter.2025.101474 |
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| Author | Search for: Korosec, Chapin S.ORCID identifier: https://orcid.org/0000-0001-5137-2195; Search for: Conway, Jessica M.; Search for: Matveev, Vitaliy A.; Search for: Ostrowski, Mario; Search for: Heffernan, Jane M.; Search for: Ghaemi, Mohammad Sajjad1 |
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| Affiliation | - National Research Council Canada. Digital Technologies
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| Funder | Search for: Canadian Institutes of Health Research; Search for: National Institutes of Health; Search for: University of Toronto; Search for: Natural Sciences and Engineering Research Council of Canada |
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| Format | Text, Article |
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| Subject | SARS-CoV-2 vaccination; HIV; antiretroviral therapy; ART; machine learning; ML; random forests; RFs; synthetic data; immunology; vaccinology; adaptive immunity; Th1 imprinting; personalized vaccination strategies; biomarker classification; immune dysregulation |
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| Abstract | Vaccines are central to pandemic control, yet immune responses can vary greatly between individuals and populations. People living with HIV (PLWH) represent an important group for whom vaccine efficacy and immune durability remain under-characterized. By utilizing machine learning to profile vaccine-elicited biomarkers (such as cytokines and antibodies), this study identifies how COVID-19 vaccination elicits distinct immune response signatures in PLWH versus HIV-negative participants. Specifically, it shows that cytokines in combination with saliva antibodies best distinguish PLWH vaccine responses, suggesting functional differences in T cell activation as well as mucosal immune response. These results provide a mechanistic framework for understanding variable vaccine outcomes in immunocompromised populations and offer a model adaptable to other diseases and cohorts. More broadly, this work illustrates how data-driven immunology can inform precision vaccination strategies and improve continuity of care in immunologically compromised groups. |
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| Publication date | 2026-03-04 |
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| Publisher | Elsevier B.V. |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 424f8326-f855-4dbe-8fb0-43aa9e175be1 |
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| Record created | 2026-03-26 |
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| Record modified | 2026-05-04 |
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