The gift of preexisting immunity for developing an alternative vaccine strategy
Bibliographic record
Abstract
Despite the worldwide application of vaccination and other antiviral interventions, pulmonary viral infections remain a persistent threat to human health. The 1918 influenza pandemic killed more than 40 million people in just one year, and the SARS-CoV-2 pandemic has killed more than 6.9 million people since 2019. While the current approved COVID-19 vaccines are administered parenterally and induce systemic immunity, they only prevent the progression to severe disease. Thus, other vaccine platforms are still needed for completely preventing the disease and subsequent transmission. In this issue of the JCI, Kawai et al. present an adjuvant-free subunit (RBD-HA) fusion vaccine, which produces robust IgG and IgA antibody responses against SARS-CoV-2, enriched within the nasal cavity, by using the host's preexisting immunity to influenza infection. This preclinical study has tremendous implications for future mucosal vaccine design and provides a roadmap for generating a safer and effective intranasal vaccine against pulmonary infections.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".