A bivalent pneumococcal histidine triad protein D-choline-binding protein A vaccine elicits functional antibodies that passively protect mice from Streptococcus pneumoniae challenge
Bibliographic record
Abstract
Vaccines based on conserved pneumococcal proteins are being investigated because serotype coverage by pneumococcal polysaccharide and polysaccharide conjugate vaccines is incomplete and may eventually decrease due to serotype replacement. Here, we examined the functionality of human antibodies induced by a candidate bivalent choline-binding protein A- pneumococcal histidine triad protein D (PcpA-PhtD) vaccine. Pre- and post-immune sera from subjects who had been vaccinated with the PcpA-PhtD candidate vaccine were tested in an established passive protection model in which mice were challenged by intravenous injection with Streptococcus pneumoniae serotype 3 strain A66.1. Serum antibody concentrations were determined by enzyme-linked immunosorbent assay (ELISA). Bacterial surface binding by serum antibodies was determined by a flow cytometry-based assay. Sera from 20 subjects were selected based on low activity of pre-immune samples in the passive protection model. Bacterial surface binding correlated more strongly with anti-PcpA (0.87; p < 0.0001) than with anti-PhtD (0.71; p < 0.0001). The odds ratio for predicting survival in the passive protection assay was higher for the anti-PcpA concentration (470 [95% confidence interval (CI), 46.8 to >999.9]) than for the anti-PhtD concentration (3.4 [95% CI, 1.9 to 5.6]) or bacterial surface binding (9.4 [95% CI, 3.6 to 24.3]). Pooled post-immune serum also protected mice against a challenge with S. pneumoniae serotype 3 strain WU2. Both anti-PcpA and anti-PhtD antibodies induced by the bivalent candidate vaccine mediate protection against S. pneumoniae. The results also showed that the ELISA titer might be useful as a surrogate for estimating the functional activity of antibodies induced by pneumococcal protein vaccines.
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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 | 0.000 | 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) | 0.000 | 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".