Insights from targeting transferrin receptors to develop vaccines for pathogens of humans and food production animals
Bibliographic record
Abstract
While developing vaccines targeting surface transferrin receptor proteins in Gram-negative pathogens of humans and food production animals, the common features derived from their evolutionary origins has provided us with insights on how improvements could be implemented in the various stages of research and vaccine development. These pathogens are adapted to live exclusively on the mucosal surfaces of the upper respiratory or genitourinary tract of their host and rely on their receptors to acquire iron from transferrin for survival, indicating that there likely are common mechanisms for delivering transferrin to the mucosal surfaces that should be explored. The modern-day receptors are derived from those present in bacteria that lived over 320 million years ago. The pathogens represent the most host adapted members of their bacterial lineages and may possess factors that enable them to have strong association with the mucosal epithelial cells, thus likely reside in a different niche than the commensal members of the bacterial lineage. The bacterial pathogens normally lead a commensal lifestyle which presents challenges for development of relevant infection models as most infection models either exclude the early stages of colonization or subsequent disease development, and the immune mechanisms at the mucosal surface that would prevent disease are not evident. Development of infection models emulating natural horizontal disease transmission are also lacking. Our aim is to share our insights from the study of pathogens of humans and food production animals with individuals involved in vaccine development, maintaining health or regulation of products in the human and animal health sectors.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".