Selection of adjuvants for vaccines targeting specific pathogens
Bibliographic record
Abstract
INTRODUCTION: Adjuvants form an integral component in most of the inactivated and subunit vaccine formulations. Careful and proper selection of adjuvants helps in promoting appropriate immune responses against target pathogens at both innate and adaptive levels such that protective immunity can be elicited. Areas covered: Herein, we describe the recent progress in our understanding of the mode of action of adjuvants that are licensed for use in human vaccines or in clinical or pre-clinical stages at both innate and adaptive levels. Different pathogens have distinct characteristics, which require the host to mount an appropriate immune response against them. Adjuvants can be selected to elicit a tailor-made immune response to specific pathogens based on their unique properties. Identification of biomarkers of adjuvanticity for several candidate vaccines using omics-based technologies can unravel the mechanism of action of modern and experimental adjuvants. Expert opinion: Adjuvant technology has been revolutionized over the last two decades. In-depth understanding of the role of adjuvants in activating the innate immune system, combined with systems vaccinology approaches, have led to the development of next-generation, novel adjuvants that can be used in vaccines against challenging pathogens and in specific target populations.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".