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Record W2410249890 · doi:10.1007/978-1-62703-194-3_22

Molecular Strategies to Engineer Transgenic Rice Seed Compartments for Large-Scale Production of Plant-Made Pharmaceuticals

2012· review· en· W2410249890 on OpenAlexaff
Trevor Greenham, Illimar Altosaar

Bibliographic record

VenueMethods in molecular biology · 2012
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicTransgenic Plants and Applications
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEconomic shortageBiotechnologyBioreactorProduction (economics)Biochemical engineeringYeastRecombinant DNABiomass (ecology)Genetically modified cropsTransgeneScale (ratio)Genetically modified riceBiologyEngineeringBotanyBiochemistryAgronomyGeneGeography

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.825
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.074
GPT teacher head0.455
Teacher spread0.381 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreReview

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".

Quick stats

Citations18
Published2012
Admission routes1
Has abstractyes

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