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Heterocyclic Amaryllidaceae Alkaloids: Biosynthesis and Pharmacological Applications

2016· review· en· W2514026372 on OpenAlex

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.

Bibliographic record

VenueCurrent Topics in Medicinal Chemistry · 2016
Typereview
Languageen
FieldChemistry
TopicChemical synthesis and alkaloids
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsAmaryllidaceae AlkaloidsAmaryllidaceaeBiosynthesisChemistryTraditional medicineBiologyBotanyBiochemistryMedicineGene

Abstract

fetched live from OpenAlex

Amaryllidaceae alkaloids (AAs), which are natural heterocyclic compounds, are isolated from Amaryllidaceae plants such as narcissus, snowdrop and spider lily. AAs have been extensively studied due to their multiple pharmacological properties. Nevertheless, knowledge of AA synthesis in plants is lacking and most genes encoding enzymes involved in their production remain unknown. AAs are structurally complex compounds which are challenging for total chemical synthesis that is economically viable. Therefore the understanding of AA biosynthesis could allow for the development of biotechnologies for the production of natural AAs or analogues, maintaining or improving their pharmacological properties. In this review, we describe the progress regarding the biosynthesis and pharmacological properties of AAs. The most recent developments in neurological, anti-cancer and anti-microbial bioactivities of heterocyclic AAs are covered.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.047
GPT teacher head0.355
Teacher spread0.308 · 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