Recent Progress on Vaccines Produced in Transgenic Plants
Bibliographic record
Abstract
The development of vaccines from plants has been going on for over two decades now. Vaccine production in plants requires time and a lot of effort. Despite global efforts in plant-made vaccine development, there are still challenges that hinder the realization of the final objective of manufacturing approved and safe products. Despite delays in the commercialization of plant-made vaccines, there are some human vaccines that are in clinical trials. The novel coronavirus (SARS-CoV-2) and its resultant disease, coronavirus disease 2019 (COVID-19), have reminded the global scientific community of the importance of vaccines. Plant-made vaccines could not be more important in tackling such unexpected pandemics as COVID-19. In this review, we explore current progress in the development of vaccines manufactured in transgenic plants for different human diseases over the past 5 years. However, we first explore the different host species and plant expression systems during recombinant protein production, including their shortcomings and benefits. Lastly, we address the optimization of existing plant-dependent vaccine production protocols that are aimed at improving the recovery and purification of these recombinant proteins.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".