The Plant Viruses and Molecular Farming: How Beneficial They Might Be for Human and Animal Health?
Bibliographic record
Abstract
Plant viruses have traditionally been studied as pathogens in the context of understanding the molecular and cellular mechanisms of a particular disease affecting crops. In recent years, viruses have emerged as a new alternative for producing biological nanomaterials and chimeric vaccines. Plant viruses were also used to generate highly efficient expression vectors, revolutionizing plant molecular farming (PMF). Several biological products, including recombinant vaccines, monoclonal antibodies, diagnostic reagents, and other pharmaceutical products produced in plants, have passed their clinical trials and are in their market implementation stage. PMF offers opportunities for fast, adaptive, and low-cost technology to meet ever-growing and critical global health needs. In this review, we summarized the advancements in the virus-like particles-based (VLPs-based) nanotechnologies and the role they played in the production of advanced vaccines, drugs, diagnostic bio-nanomaterials, and other bioactive cargos. We also highlighted various applications and advantages plant-produced vaccines have and their relevance for treating human and animal illnesses. Furthermore, we summarized the plant-based biologics that have passed through clinical trials, the unique challenges they faced, and the challenges they will face to qualify, become available, and succeed on the market.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".