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Record W4380738340 · doi:10.1101/2023.06.14.544834

Immunogenicity of COVID-19 vaccines and their effect on the HIV reservoir in older people with HIV

2023· preprint· en· W4380738340 on OpenAlexafffund
Vitaliy Matveev, Erik Z. Mihelic, Erika Benko, Patrick Budylowski, Sebastian Grocott, Terry Lee, Chapin S. Korosec, Karen Colwill, Henry Gilreath Stephenson, Ryan Law, Lesley A. Ward, Salma Sheikh‐Mohamed, Geneviève Mailhot, Melanie Delgado-Brand, Adrian Pasculescu, Jenny H. Wang, Freda Qi, Tulunay Tursun, Lela Kardava, Serena Chau, Philip Samaan, Annam Imran, Dennis C. Copertino, Gary Chao, Yoojin Choi, Robert J. Reinhard, Rupert Kaul, Jane M. Heffernan, R. Brad Jones, Tae‐Wook Chun, Susan Moir, Joel Singer, Jennifer L. Gommerman, Anne‐Claude Gingras, Colin Kovacs, Mario Ostrowski

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of British ColumbiaYork UniversitySt. Michael's HospitalCentre for Advancing Health OutcomesMaple Leaf Medical ClinicHIV Legal NetworkMcGill UniversityUniversity of Toronto
FundersNHLBI Division of Intramural ResearchUniversity of TorontoNational Institute of Allergy and Infectious DiseasesNatural Sciences and Engineering Research Council of CanadaOntario HIV Treatment NetworkNational Institutes of HealthCanadian Institutes of Health ResearchOntario GenomicsGenome Canada
KeywordsImmunogenicityViremiaImmune systemMedicineVaccinationHuman immunodeficiency virus (HIV)ImmunologyVirologyHIV vaccineVaccine trial

Abstract

fetched live from OpenAlex

ABSTRACT Older individuals and people with HIV (PWH) were prioritized for COVID-19 vaccination, yet comprehensive studies of the immunogenicity of these vaccines and their effects on HIV reservoirs are not available. We followed 68 PWH aged 55 and older and 23 age-matched HIV-negative individuals for 48 weeks from the first vaccine dose, after the total of three doses. All PWH were on antiretroviral therapy (cART) and had different immune status, including immune responders (IR), immune non-responders (INR), and PWH with low-level viremia (LLV). We measured total and neutralizing Ab responses to SARS-CoV-2 spike and RBD in sera, total anti-spike Abs in saliva, frequency of anti-RBD/NTD B cells, changes in frequency of anti-spike, HIV gag/nef-specific T cells, and HIV reservoirs in peripheral CD4 + T cells. The resulting datasets were used to create a mathematical model for within-host immunization. Various regimens of BNT162b2, mRNA-1273, and ChAdOx1 vaccines elicited equally strong anti-spike IgG responses in PWH and HIV - participants in serum and saliva at all timepoints. These responses had similar kinetics in both cohorts and peaked at 4 weeks post-booster (third dose), while half-lives of plasma IgG also dramatically increased post-booster in both groups. Salivary spike IgA responses were low, especially in INRs. PWH had diminished live virus neutralizing titers after two vaccine doses which were ‘rescued’ after a booster. Anti-spike T cell immunity was enhanced in IRs even in comparison to HIV - participants, suggesting Th1 imprinting from HIV, while in INRs it was the lowest. Increased frequency of viral ‘blips’ in PWH were seen post-vaccination, but vaccines did not affect the size of the intact HIV reservoir in CD4 + T cells in most PWH, except in LLVs. Thus, older PWH require three doses of COVID-19 vaccine to maximize neutralizing responses against SARS-CoV-2, although vaccines may increase HIV reservoirs in PWH with persistent viremia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.285
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2023
Admission routes2
Has abstractyes

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