Protective efficacy of a<i>Mycoplasma pneumoniae</i>P1C DNA vaccine fused with the B subunit of<i>Escherichia coli</i>heat-labile enterotoxin
Bibliographic record
Abstract
In the present study, we investigated the immunomodulatory responses of a DNA vaccine constructed by fusing Mycoplasma pneumoniae P1 protein carboxy terminal region (P1C) with the Escherichia coli heat-labile toxin B subunit (LTB). BALB/c mice were immunized by intranasal inoculation with control DNAs, the P1C DNA vaccine or the LTB-P1C fusion DNA vaccine. Levels of the anti-M. pneumoniae antibodies and levels of interferon-γ and IL-4 in mice were increased significantly upon inoculation of the LTB-P1C fusion DNA vaccine when compared with the inoculation with P1C DNA vaccine. The LTB-P1C fusion DNA vaccine efficiently enhanced the M. pneumoniae-specific IgA and IgG levels. The IgG2a/IgG1 ratio was significantly higher in bronchoalveolar lavages fluid and sera from mice fusion with LTB and P1C than mice receiving P1C alone. When the mice were challenged intranasally with 10(7) CFU M. pneumoniae strain (M129), the LTB-P1C fusion DNA vaccine conferred significantly better protection than P1C DNA vaccine (P < 0.05), as suggested by the results, such as less inflammation, lower histopathological score values, lower detectable number of M. pneumoniae strain, and lower mortality of challenging from 5 × 10(8) CFU M. pneumoniae. These results indicated that the LTB-P1C fusion DNA vaccine efficiently improved protective efficacy against M. pneumoniae infection and effectively attenuated development of M. pneumoniae in mice.
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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 | 0.001 |
| 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".