Heterologous immunization with Covishield and Pfizer vaccines against SARS-CoV-2 elicits a robust humoral immune response
Bibliographic record
Abstract
Understanding the efficacy and durability of heterologous immunization schedules against SARS-CoV-2 is critical, as supply demands and vaccine choices become significant issues in the global vaccination strategy. Here we characterize the neutralizing antibodies produced in two subjects who received combination immunizations against SARS-CoV-2, first with Covishield (Oxford-AstraZeneca) vaccine, followed 33 days later with a second dose (booster) shot of the Pfizer-BioNTech vaccine. Serum samples were collected 25 days following the primary vaccination and 13 days after the secondary Pfizer vaccination. Both subjects exhibited increased levels of isotype IgG and IgM antibodies directed against the entire spike protein following immunizations. These antibodies also exhibited increased reactivity with the receptor binding domain (RBD) in the spike protein and neutralized the infectivity of replicating vesicular stomatitis virus (VSV) that contains the COVID-19 coronavirus S protein gene in place of its normal G glycoprotein. This VSV pseudovirus also contains the reporter gene for enhanced green fluorescent protein (eGFP). Antibody titers against the spike protein and serum neutralization titers against the reporter virus are reported for the 2 heterologous vaccinated individuals and compared to a positive control derived from a convalescent patient and a negative control from an unexposed individual. The Pfizer-BioNTech vaccine increased antibody binding to the spike protein and RBD, and approached levels found in the convalescent positive control. Neutralizing antibodies against the VSV-S pseudovirus in the 2 subjects also approached levels in the convalescent sera. These results firmly validate the value of the Pfizer-BioNTech vaccine in boosting immunity following initial Covishield inoculation.
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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 | 0.001 |
| 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".