Molecular Strategies to Engineer Transgenic Rice Seed Compartments for Large-Scale Production of Plant-Made Pharmaceuticals
Bibliographic record
Abstract
The use of plants as bioreactors for the large-scale production of recombinant proteins has emerged as an exciting area of research. The current shortages in protein therapeutics due to the capacity and economic bottlenecks faced with modern protein production platforms (microbial, yeast, mammalian) has driven considerable attention towards molecular pharming. Utilizing plants for the large-scale production of recombinant proteins is estimated to be 2-10% the cost of microbial platforms, and up to 1,000-fold more cost effective than mammalian platforms (Twyman et al. Trends Biotechnol 21:570-578, 2003; Sharma and Sharma, Biotechnol Adv 27:811-832, 2009). In order to achieve an economically feasible plant production host, protein expression and accumulation must be optimized. The seed, and more specifically the rice seed has emerged as an ideal candidate in molecular pharming due to its low protease activity, low water content, stable protein storage environment, relatively high biomass, and the molecular tools available for manipulation (Lau and Sun, Biotechnol Adv 27:1015-1022, 2009).
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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".