Vaccine adjuvants – understanding molecular mechanisms to improve vaccines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Infectious pathogens are responsible for high utilisation of healthcare resources globally. Attributable morbidity and mortality remains exceptionally high. Vaccines offer the potential to prime a pathogen-specific immune response and subsequently reduce disease burden. Routine vaccination has fundamentally altered the natural history of many frequently observed and serious infections. Vaccination is also recommended for persons at increased risk of severe vaccine-preventable disease. Many current nonadjuvanted vaccines are poorly effective in the elderly and immunocompromised populations, resulting in nonprotective postvaccine antibody titres, which serve as surrogate markers for protection. The vaccine-induced immune response is influenced by: (i.) vaccine factors i.e., type and composition of the antigen(s), (ii.) host factors i.e., genetic differences in immune-signalling or senescence, and (iii.) external factors such as immunosuppressive drugs or diseases. Adjuvanted vaccines offer the potential to compensate for a lack of stimulation and improve pathogen-specific protection. In this review we use influenza vaccine as a model in a discussion of the different mechanisms of action of the available adjuvants. In addition, we will appraise new approaches using "vaccine-omics" to discover novel types of adjuvants.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it