Edible vaccines: alternatives to conventional immunization.
Bibliographic record
Abstract
Abstract Increased numbers of new pathogenic infections and the development of antibiotic resistance to many old pathogens have forced us to focus on prevention, rather than treatment, of infectious diseases. Widespread vaccination has therefore become a major target for many countries. However, numerous countries do not have adequate access to vaccines that are regularly administered in developed countries. As well, there are many pathogens to which there are currently no vaccines available. One of the many new technologies/approaches for addressing these needs is the production of subunit vaccines in transgenic, edible plants, which enable oral vaccine delivery. A variety of plants have now been used as hosts for vaccines, still at the experimental stage, including tobacco, potatoes, carrots, rice, and tomatoes. The main goal of an oral vaccine is the induction of a mucosal immune response and a subsequent systemic immune response. By stimulating a strong mucosal immune response, edible vaccines may provide effective immunity against pathogens that invade both mucosal and systemic sites. Immunization studies with plant-derived subunit vaccines have demonstrated that plants are able to produce recombinant proteins that retain antigenic and immunogenic properties. Although many issues and concerns still remain regarding the safety and efficacy of edible vaccines in humans, current data indicate that edible vaccines are indeed a promising option for the future.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".