A single subcutaneous or intranasal immunization with adenovirus‐based SARS‐CoV‐2 vaccine induces robust humoral and cellular immune responses in mice
Bibliographic record
Abstract
Optimal vaccines are needed for sustained suppression of SARS-CoV-2 and other novel coronaviruses. Here, we developed a recombinant type 5 adenovirus vector encoding the gene for the SARS-CoV-2 S1 subunit antigen (Ad5.SARS-CoV-2-S1) for COVID-19 immunization and evaluated its immunogenicity in mice. A single immunization with Ad5.SARS-CoV-2-S1 via S.C. injection or I.N delivery induced robust antibody and cellular immune responses. Vaccination elicited significant S1-specific IgG, IgG1, and IgG2a endpoint titers as early as 2 weeks, and the induced antibodies were long lasting. I.N. and S.C. administration of Ad5.SARS-CoV-2-S1 produced S1-specific GC B cells in cervical and axillary LNs, respectively. Moreover, I.N. and S.C. immunization evoked significantly greater antigen-specific T-cell responses compared to unimmunized control groups with indications that S.C. injection was more effective than I.N. delivery in eliciting cellular immune responses. Mice vaccinated by either route demonstrated significantly increased virus-specific neutralization antibodies on weeks 8 and 12 compared to control groups, as well as BM antibody forming cells (AFC), indicative of long-term immunity. Thus, this Ad5-vectored SARS-CoV-2 vaccine candidate showed promising immunogenicity following delivery to mice by S.C. and I.N. routes of administration, supporting the further development of Ad-based vaccines against COVID-19 and other infectious diseases for sustainable global immunization programs.
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 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) | 0.001 | 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 | 0.001 | 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".