Maternal Immunity Provides Protection against Pertussis in Newborn Piglets
Bibliographic record
Abstract
Pertussis continues to be a significant cause of morbidity and mortality in infants and young children worldwide. Methods to control the disease are based on vaccination with either whole-cell or acellular vaccines or treatment with antibiotics. However, despite worldwide vaccination infants are still at the highest risk for the disease. Here we used our newly developed newborn-piglet model to investigate whether transfer of maternal immunity can protect newborn piglets against infection with Bordetella pertussis. Pregnant sows were vaccinated with heat-inactivated B. pertussis or treated with saline (controls). Newborn piglets were allowed to suckle colostrum and milk for 4 to 5 days before they were challenged with 5 x 10(9) CFU of bacteria intrapulmonarily. Elevated levels of B. pertussis-specific secretory immunoglobulin A (S-IgA) and IgG antibodies were found in the colostrum and serum of vaccinated sows but not in those of control sows. Subsequently, significant levels of specific IgG and S-IgA were detected in the serum and bronchoalveolar lavage fluid of piglets born to vaccinated sows. Following infection with 5 x 10(9) CFU of B. pertussis, clinical symptoms, pathological alterations, and bacterial shedding were significantly reduced in piglets that had received passively transferred immunity. Thus, our results demonstrate that maternal immunization might represent an alternative approach to provide protection against pertussis in young infants.
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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) | 0.000 | 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 | 0.001 | 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".