Immune response and protective efficacy of S9 ribosomal protein of <i>Streptococcus pneumoniae</i> in a model of sepsis
Bibliographic record
Abstract
Vaccination is the most promising strategy to reduce the incidence of pneumococcal infection. Although there are vaccines available, all of them are based on polysaccharide antigens (conjugated or not). In addition to their high cost, those vaccines do not cover all serotypes. To overcome these hindrances, we evaluated the immunogenicity and the protective efficacy of the S9 ribosomal protein of Streptococcus pneumoniae with the aim of developing a protein-based vaccine in the future. The gene encoding the S9 ribosomal protein was cloned in pET21-a expression vector, and the recombinant S9 protein was used to immunize mice. Significantly higher levels of anti-S9 immunoglobulin G were achieved (with predominance of immunoglobulin G1) in comparison with the control. Antibodies elicited against S. pneumoniae protein extract in rabbit recognized the recombinant S9 protein by Western blot, thus demonstrating its immunogenicity. Moreover, mice immunized with recombinant S9 protein and challenged with a virulent strain of S. pneumoniae presented a significant reduction of bacteremia after 24 h of infection as compared with the control. However, in the S9-immunized mice the onset of death was insignificantly delayed, but all of them died by the fourth day postinfection.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".