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Record W2322814366 · doi:10.1139/gen-2012-0182

Authentication of official Da-huang by sequencing and multiplex allele-specific PCR of a short maturase K gene

2013· article· en· W2322814366 on OpenAlexvenueno aff
Guojie Xu, Xueyong Wang, Chunsheng Liu, Weidong Li, Shengli Wei, Ying Liu, Xiaoli Cheng, Juan Liu

Bibliographic record

VenueGenome · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant biochemistry and biosynthesis
Canadian institutionsnot available
FundersBeijing University of Chinese Medicine
KeywordsBiologyGeneticsGeneCladeMultiplex polymerase chain reactionMultiplexSingle-nucleotide polymorphismAlleleComputational biologyVariants of PCRPolymerase chain reactionGenotypePhylogenetic tree

Abstract

fetched live from OpenAlex

Rhubarb (official Da-huang) is an important medicinal herb in Asia. Many adulterants of official Da-huang have been discovered in Chinese markets in recent years, which has resulted in adverse effects in medicinal treatment. Here, novel molecular markers based on a short maturase K (matK) gene were developed for authenticating official Da-huang. This study showed that all the species from official Da-huang were clustered together in one clade in the polygenetic trees based on short matK. Two highly conserved single nucleotide polymorphisms of short matK were mined in the species from official Da-huang. Based on these polymophisms, four improved specific primers of official Da-huang were successfully developed that generated reproducible specific bands. These results suggest that the short matK sequence can be considered as a favorable candidate for distinguishing official Da-huang from its adulterants. The established multiplex allele-specific PCR was determined to be simple and accurate and may serve as a preferable tool for authentication of official Da-huang. In addition, we suggest that short-sized specific bands be developed to authenticate materials used in traditional Chinese medicine.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

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

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 head0.015
GPT teacher head0.209
Teacher spread0.194 · 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

Citations18
Published2013
Admission routes1
Has abstractyes

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