Identity, structure and compositional analysis of adjuvanted vaccines
Bibliographic record
Abstract
1) Background: Traditionally, complex biological products such as vaccines presented unique challenges to implementation of even rudimentary characterization packages; thus, the product was defined almost exclusively by its manufacturing process. The advances in technology and analytical tools allowed the application of more comprehensive characterization packages for products such as adsorbed combination vaccines, which contain several antigens in a single formulation to protect against more than one disease, and may contain adjuvants and excipients. Aluminum phosphate (AlPO4) is a well-established adjuvant for enhancing the uptake of vaccines and to induce robust immunity against pathogens. During manufacturing, adjuvant is mixed with protein antigens which may in turn impact their higher order structure and stability. 2) Methods: To study the structural changes of protein antigens after adsorption several analytical tools including DLS, FTIR, Fluorescence, LD, and SEM were used. 3) Results: the AlPO4 adjuvant suspension consists of small submicron particles that form a continuous porous surface. Secondary structure alpha-helix and beta-sheet content of DT and TT increased after adsorption to AlPO4 adjuvant, whereas no significant changes were noted for other protein antigens. Interactions were noted between AlPO4 adjuvant and DT, TT, and FHA. 4) Conclusions: here we report for the first time the use of SEM for the visualization of adsorbed multivalent vaccine components. A unique signature profile detected for each multivalent vaccine by FTIR can be used as a lean in-process test to verify vaccine product composition and identity prior to filling.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".