Acellular pertussis vaccines and the role of pertactin and fimbriae
Bibliographic record
Abstract
The introduction of acellular pertussis (Pa) vaccines in countries with a low uptake of whole-cell pertussis (Pw) vaccines has led to a dramatic reduction in pertussis disease. Diphtheria-tetanus-acellular pertussis (DTPa) vaccines have also ensured continued high level disease protection in these countries following the shift from Pw- to Pa-containing vaccines, and allowed pertussis booster programs to be implemented. Vaccines containing between one and five components have been licensed and implemented. Those with three or more components consisting of filamentous hemagglutinin (FHA), pertussis toxin (PT) and pertactin (PRN) are considered to be more effective than one/two-component Pa vaccines that contain only PT or both PT and FHA. Changes in circulating Bordetella pertussis strains may impact vaccine efficacy and, thus, incidence and transmission of pertussis and deserve to be followed carefully. To date, vaccine-induced shifts among fimbriae (FIM) are reported and this could impact the efficacy of FIM-containing vaccines. Currently, FIM3 appears to be dominant in most European countries, Canada and Australia. Data obtained from a DTPa5 vaccine containing FIM2 and FIM3 have indicated a shift towards an increase in FIM3-expressing B. pertussis clinical breakthrough cases when compared with control vaccine. By contrast, relatively minor PT and PRN sequence polymorphisms have been identified without demonstrable association with vaccination programs. Adsorption of PRN to aluminum salt appears critical for optimal protective capacity in murine pertussis lung challenge. In addition, clinical studies have shown anti-PRN antibody levels to be higher when PRN is adsorbed at a 8-microg dosage versus non-adsorbed PRN at a 3-microg dosage. The available data, therefore, demonstrate that appropriately formulated acellular vaccines containing PT and PRN are the preferred option for pertussis immunization.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.000 | 0.000 |
| 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 teacher head, 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".