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Record W4283457550 · doi:10.3390/biom12070893

Biotechnological Approaches to Optimize the Production of Amaryllidaceae Alkaloids

2022· review· en· W4283457550 on OpenAlex
Manoj Koirala, Vahid Karimzadegan, Nuwan Sameera Liyanage, Natacha Mérindol, Isabel Desgagné‐Penix

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiomolecules · 2022
Typereview
Languageen
FieldChemistry
TopicChemical synthesis and alkaloids
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsAmaryllidaceae AlkaloidsAmaryllidaceaeProduction (economics)Biochemical engineeringBiotechnologyComputer scienceBiologyEngineeringBotanyEconomics

Abstract

fetched live from OpenAlex

Amaryllidaceae alkaloids (AAs) are plant specialized metabolites with therapeutic properties exclusively produced by the Amaryllidaceae plant family. The two most studied representatives of the family are galanthamine, an acetylcholinesterase inhibitor used as a treatment of Alzheimer’s disease, and lycorine, displaying potent in vitro and in vivo cytotoxic and antiviral properties. Unfortunately, the variable level of AAs’ production in planta restricts most of the pharmaceutical applications. Several biotechnological alternatives, such as in vitro culture or synthetic biology, are being developed to enhance the production and fulfil the increasing demand for these AAs plant-derived drugs. In this review, current biotechnological approaches to produce different types of bioactive AAs are discussed.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.199
GPT teacher head0.288
Teacher spread0.089 · 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