Bibliographic record
Abstract
Aluminum compounds (alum) are the most widely used adjuvants in veterinary and human vaccines. Alum was initially thought to be a simple depot for antigen retention; however, our understanding of the mechanism by which it works has progressed substantially in recent decades. Nonetheless, consensus regarding its roles in different aspects of immune regulation has not been reached, and it remains a long-standing research subject in the field of vaccinology. This review, in chronological order, discusses the various hypotheses proposed in mostly inadequate attempts to illuminate the mechanism by which alum works, from the depot theory to the involvement of the NLRP3 inflammasome and from cell death-associated danger factors to crystalline structure-mediated plasma membrane alteration. In addition, novel findings of unexpected beneficial effects of decreased HBV (Hepatitis B virus) viral load and HBeAg seroconversion in chronically infected patients, as well as significant tumor suppression in experimental mice following multiple alum-only injections are examined, revealing alum's potential clinical applications beyond its use as a simple tool in antigen preparation. With increasing threats of emerging microbes, originating from natural or man-made sources, that pose significant health concerns at the population scale, the potential use of alum as a 'first-aid' vaccine is also discussed.
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 | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".