Per Os to Protection – Targeting the Oral Route to Enhance Immune-mediated Protection from Disease of the Human Newborn
Bibliographic record
Abstract
Insight into the mechanisms that guide the host immune response towards either immunogenicity or tolerance is crucial for the success of many biomedical interventions, including vaccination. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 In this regard, early life is of particular interest as young infants not only suffer the highest burden of infectious disease across the human lifespan, but also receive the highest number of vaccinations.11, 12, 13, 14 Vaccines preventing infections or disease are amongst the most cost-effective life-saving medical interventions in history.15 The young (<2 years of age), including newborns, receive most of the vaccines given globally.16 For example, the World Health Organization (WHO) Expanded Program on Immunization (EPI) recommends up to 9 different types of vaccines (e.g. live attenuated, subunit, adjuvanted etc.) in the first 2 years of life, but only 4 and 3 to adolescents and adults, respectively; and between 21–24 doses of vaccines are recommended for children under 2 years of age, vs. only 7 for adolescents and 5 for adults.17 In this Perspective, we highlight how the uniqueness of orally (per os (p.o.) = by mouth) induced immunity in early life offers highly promising approaches to enhance immune-mediated protection at the start of life. The oral route also presents a more feasible thus scalable approach to public health interventions, especially in resource constrained settings.18, 19, 20, 21 An increased focus on investigating p.o. administration of immune modulating interventions (e.g. vaccines) thus appears prudent.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".