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Record W2333000754 · doi:10.1055/s-0031-1298368

Intestinal Transport of Pure Diester-type Alkaloids from an Aconite Extract across the Caco-2 Cell Monolayer Model

2012· article· en· W2333000754 on OpenAlexaff
Na Li, Tsao Rong, Zhigang Sui, Jingwei Ma, Zhiqiang Liu, Zhongying Liu

Bibliographic record

VenuePlanta Medica · 2012
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPlant-based Medicinal Research
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsCaco-2MonolayerChemistryStereochemistryTraditional medicineChromatographyCellMedicineBiochemistry

Abstract

fetched live from OpenAlex

Aconitine (AC), mesaconitine (MA), and hypaconitine (HA) are the active alkaloids identified in aconite tuber, an important traditional Chinese medicine. The study is aimed to investigate their intestinal transport profiles and potential interaction during the intestinal absorption using the Caco-2 cell monolayer model. All three alkaloids had good permeability with P(app) values greater than 1 × 10 (-6) cm · s (-1). However, AC, MA, and HA in a mixture and as an extract, in both cases with the same content of alkaloids, showed higher transport efficiency in the apical to basolateral, and lower transport efficiency in the basolateral to apical directions. Digoxin, as a P-glycoprotein (P-gp) substrate, was substantially effluxed in the basolateral to apical direction but inhibited by the three alkaloids. Furthermore, the backwards transport of MA and HA was inhibited by the P-gp inhibitor verapamil. These observations indicated that the three alkaloids may not only be P-gp inhibitors but also its substrates; they interact with each other and can potentially enhance their own bioavailability when taken concomitantly.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.156
GPT teacher head0.463
Teacher spread0.307 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations36
Published2012
Admission routes1
Has abstractyes

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