Plant-based solutions for veterinary immunotherapeutics and prophylactics
Bibliographic record
Abstract
An alarming increase in emergence of antibiotic resistance among pathogens worldwide has become a serious threat to our ability to treat infectious diseases according to the World Health Organization. Extensive use of antibiotics by livestock producers promotes the spread of new resistant strains, some of zoonotic concern, which increases food-borne illness in humans and causes significant economic burden on healthcare systems. Furthermore, consumer preferences for meat/poultry/fish produced without the use of antibiotics shape today's market demand. So, it is viewed as inevitable by the One Health Initiative that humans need to reduce the use of antibiotics and turn to alternative, improved means to control disease: vaccination and prophylactics. Besides the intense research focused on novel therapeutic molecules, both these strategies rely heavily on the availability of cost-effective, efficient and scalable production platforms which will allow large-volume manufacturing for vaccines, antibodies and other biopharmaceuticals. Within this context, plant-based platforms for production of recombinant therapeutic proteins offer significant advantages over conventional expression systems, including lack of animal pathogens, low production costs, fast turnaround and response times and rapid, nearly-unlimited scalability. Also, because dried leaves and seeds can be stored at room temperature for lengthy periods without loss of recombinant proteins, plant expression systems have the potential to offer lucrative benefits from the development of edible vaccines and prophylactics, as these would not require "cold chain" storage and transportation, and could be administered in mass volumes with minimal processing. Several biotechnology companies currently have developed and adopted plant-based platforms for commercial production of recombinant protein therapeutics. In this manuscript, we outline the challenges in the process of livestock immunization as well as the current plant biotechnology developments aimed to address these challenges.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".