Passive protection of mice against<i>Streptococcus pneumoniae</i>challenge by naturally occurring and vaccine-induced human anti-PhtD antibodies
Bibliographic record
Abstract
Currently marketed Streptococcus pneumoniae vaccines are based on polysaccharide capsular antigens from the most common strains. Pneumococcal histidine triad protein D (PhtD) is a conserved surface protein that is being evaluated as a candidate for a vaccine with improved serotype coverage. Here, we measured the functional activity of human anti-PhtD antibodies in a passive protection model wherein mice were challenged with a lethal dose of S. pneumoniae by intravenous injection. This functional activity was compared with anti-PhtD antibody concentrations measured by enzyme-linked immunosorbent assay (ELISA) to estimate the 50% protective dose (ED50). Anti-PhtD antibodies affinity purified from pooled normal human sera passively protected mice with an ED50 of 1679 ELISA units/ml (95% confidence interval, 1420-1946). Sera from subjects injected with aluminum-adjuvanted PhtD in a phase I trial had similar activity per unit of antibody (ED50 = 1331 ELISA units/ml [95% confidence interval, 762-2038]). Vaccine-induced activity in the passive protection model was blocked by pre-incubation with recombinant PhtD but not by a control S. pneumoniae antigen (LytB). These results show that human anti-PhtD antibodies, whether naturally acquired or induced by the PhtD candidate vaccine, are functional. This supports the development of the PhtD candidate as part of a broadly protective pneumococcal vaccine.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".