Comparative study of humoral and cellular immunity against SARS-CoV-2 induced by different COVID-19 vaccine types: Insights into protection against wildtype, Delta and JN.1 omicron strains
Bibliographic record
Abstract
We investigated the effectiveness of different COVID-19 vaccinations administered in Pakistan by studying the effect of inactivated virus, mRNA and vector formulations. This study in 916 participants was conducted between October 2021 and July 2022. Subjects receiving inactivated (A), mRNA (B), one-dose vector (C), and two-dose vector (D) vaccines were sampled at baseline, 6, 12, and 24 weeks. Serum IgG antibodies to wildtype Spike and its receptor binding domain (RBD) were measured. Pseudovirus particle-based neutralizing assays against wildtype, Delta, and JN.1 variants were performed. T cell IFN-γ responses to SARS-CoV-2 antigens were measured. Participants were aged 37.05 ± 14.44 years and comprised 48.6 % females. Baseline Spike seropositivity rose from 90 % to 96 % by 24 weeks; and 40 % to 90 % against RBD. Group B participants had the highest anti-RBD levels which peaked by 6 weeks. IgG RBD in group A and C increased up until 24 weeks. Anti-RBD levels were reduced in those over 50 years. At baseline neutralizing titers were present at 38.5 % against wildtype and in 34.2 % against Delta variants. Titers doubled in vaccine groups A-C by 12 weeks, with highest titers in B and lowest in group C participants. At baseline, neutralizing titers against the JN.1 variant were absent but low titers were evident in 10 % of participants after 12 weeks. T cell reactivity to SARS-CoV-2 increased from 31 % at baseline to 50 % in group A and 73 % in group B participants by 6 weeks after vaccination. Presence of immunity against wildtype and Delta variants in one-third of participants at baseline could be due to sub-clinical infections. Increase in humoral and cellular immunity was greater after mRNA as compared with inactivated vaccinations. As COVID-19 morbidity in the population remained low, our data supports effectiveness of multiple vaccine formulations in protecting against severe COVID-19 in this high transmission population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".