Plant Molecular Pharming and Plant-Derived Compounds towards Generation of Vaccines and Therapeutics against Coronaviruses
Bibliographic record
Abstract
The current century has witnessed infections of pandemic proportions caused by Coronaviruses (CoV) including severe acute respiratory syndrome-related CoV (SARS-CoV), Middle East respiratory syndrome-related CoV (MERS-CoV) and the recently identified SARS-CoV2. Significantly, the SARS-CoV2 outbreak, declared a pandemic in early 2020, has wreaked devastation and imposed intense pressure on medical establishments world-wide in a short time period by spreading at a rapid pace, resulting in high morbidity and mortality. Therefore, there is a compelling need to combat and contain the CoV infections. The current review addresses the unique features of the molecular virology of major Coronaviruses that may be tractable towards antiviral targeting and design of novel preventative and therapeutic intervention strategies. Plant-derived vaccines, in particular oral vaccines, afford safer, effectual and low-cost avenues to develop antivirals and fast response vaccines, requiring minimal infrastructure and trained personnel for vaccine administration in developing countries. This review article discusses recent developments in the generation of plant-based vaccines, therapeutic/drug molecules, monoclonal antibodies and phytochemicals to preclude and combat infections caused by SARS-CoV, MERS-CoV and SARS-CoV-2 viruses. Efficacious plant-derived antivirals could contribute significantly to combating emerging and re-emerging pathogenic CoV infections and help stem the tide of any future pandemics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".