Induction of Oral Tolerance with Transgenic Plants Expressing Antigens for Prevention/Treatment of Autoimmune, Allergic and Inflammatory Diseases
Bibliographic record
Abstract
The prevalence and incidence of autoimmune and allergic diseases have increased dramatically over the last several decades, especially in the developed world. The treatment of autoimmune and allergic diseases is typically with the use of non-specific immunosuppressive agents that compromise the integrity of the host immune system and therefore, increase the risk of infections. Antigenspecific immunotherapy by reinstating immunological tolerance towards self antigens without compromising immune functions is a much desired goal for the treatment of autoimmune and allergic diseases. Mucosal administration of antigen is a long-recognized method of inducing antigen-specific immune tolerance known as oral tolerance, which is viewed as having promising potential in the treatment of autoimmune and allergic diseases. Plant-based expression and delivery of recombinant antigens provide a promising new platform to induce oral tolerance, having considerable advantages including reduced cost and increased safety. Indeed, in recent years the use of tolerogenic plants for oral tolerance induction has attracted increasing attention, and considerable progress has been made. This review summarizes recent advances in using plants to deliver tolerogens for induction of oral tolerance in the treatment of autoimmune, allergic and inflammatory diseases.
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 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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".