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Record W4283457550 · doi:10.3390/biom12070893

Biotechnological Approaches to Optimize the Production of Amaryllidaceae Alkaloids

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

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations28
Published2022
Admission routes2
Has abstractyes

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