Neutralizing antibody responses to the Delta variant of SARS-CoV-2 following vaccination with Ad5-nCoV (CanSino) in the Mexican population
Bibliographic record
Abstract
During the COVID-19 pandemic, the Ad5-nCoV vaccine was applied to the Mexican population before the WHO approved it. In a transversal study, we compare the CanSino vaccine efficacy and a natural SARS-CoV-2 infection in eliciting neutralizing antibodies against the SARS-CoV-2 Delta variant in Guadalajara, Mexico. Participants between 30-60 years were included in the study and classified into three groups: 1) Natural immunity (unvaccinated), 2) Vaccine-induced immunity (vaccinated individuals without a COVID-19 history), and 3) Natural immunity + vaccine-induced immunity. These groups were matched by age and gender. We assessed the ability of individuals' serum to neutralize the Delta variant and compared the results of the different groups using a neutralization test followed by plaque-forming units. Results showed that 39% of individuals' serum with a history of COVID-19 (natural immunity, Group 1) could not neutralize the Delta variant, compared to 33% in vaccinated individuals without COVID-19 (vaccine immunity, Group 2). In contrast, only 7% of vaccinated individuals with a history of COVID-19 (natural + vaccine immunities) could not neutralize the Delta variant. We concluded that the effectiveness of the Ad5-nCoV vaccine to induce neutralizing antibodies against the Delta variant is comparable to that of natural infection (61% vs. 67%). However, in individuals with both forms of immunity (Group 3), it increased to 93%. Based on these results, despite the Ad5-nCoV vaccine originally being designed as a single-dose regimen, it could be recommended that even those who have recovered from COVID-19 should consider vaccination to boost their immunity against this variant.
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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 | 0.001 |
| 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".