Designing the Next Generation of Vaccines: Relevance for Future Pandemics
Bibliographic record
Abstract
The development of vaccines is one of the greatest medical interventions in the history of global infectious diseases and has contributed to the annual saving of at least 2 to 3 million lives worldwide. However, many diseases are not preventable through currently available vaccines, and the potential of modulating the immune response during vaccination has not been fully exploited. The first golden age of vaccines was based on the germ theory and the use of live, attenuated, inactivated pathogens or toxins. New strategies and formulations (e.g., adjuvants) with an immunomodulatory capacity to enhance the protective qualities and duration of vaccines have been incompletely exploited. These strategies can prevent disease and improve protection against infectious diseases, modulate the course of some noncommunicable diseases, and increase the immune responses of patients at a high risk of infection, such as the elderly or immunocompromised patients. In this minireview, we focus on how metabolic and epigenetic modulators can amplify and enhance the function of immunity in a given vaccine. We propose the term "amplifier" for such additives, and we pose that future vaccines will have three components: antigen, adjuvant, and amplifier.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".