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Record W2898724864 · doi:10.1139/gen-2018-0059

DNA barcoding of species of <i>Bacopa</i> coupled with high-resolution melting analysis

2018· article· en· W2898724864 on OpenAlexvenueno aff
Chayapol Tungphatthong, Jutharat Somnuek, Thatree Phadungcharoen, Kornkanok Ingkaninan, Jessada Denduangboripant, Suchada Sukrong

Bibliographic record

VenueGenome · 2018
Typearticle
Languageen
FieldMedicine
TopicMedicinal Plants and Neuroprotection
Canadian institutionsnot available
Fundersnot available
KeywordsHigh Resolution MeltBacopa monnieriDNA barcodingBiologyAmpliconBotanyEvolutionary biologyPolymerase chain reactionGeneticsGene

Abstract

fetched live from OpenAlex

In Thailand, there are three species of Bacopa, namely, B. monnieri, B. caroliniana, and B. floribunda. Among these species of Bacopa, B. monnieri is the only medicinal species, used for the treatment of cognitive impairment and improvement of cognitive abilities because of its bioactive constituents, bacoside A and B. However, because of the similar characteristics of these species, it is difficult to differentiate among related species, resulting in confusion during identification. For this reason, and to ensure therapeutic quality for consumers, authentication is important. In this study, the three abovementioned species of Bacopa were evaluated using barcoding coupled with high-resolution melting (Bar-HRM) analysis based on primers designed for the trnL-F sequences of the three species. The melting profiles of the trnL-F amplicons of B. caroliniana and B. floribunda were clearly different from the melting profile of the trnL-F amplicon from B. monnieri; thus, the species could be discriminated by Bar-HRM analysis. Bar-HRM was then used to authenticate commercial products in various forms. The melting curves of the six commercial samples indicated that all the tested products contained genuine B. monnieri species. This method provides an efficient and reliable authentication system for future commercial herbal products and offers a reference system for quality control.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.237
Teacher spread0.216 · 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.

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

Citations9
Published2018
Admission routes1
Has abstractyes

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