Validation and Optimization of qPCR Method for Identification of <i>Actaea racemosa</i> (Black Cohosh) NHPs
Bibliographic record
Abstract
BACKGROUND: Actaea racemosa (black cohosh) herbal dietary supplements are commonly used to treat menopausal symptoms in women. However, there is a considerable risk of contamination of A. racemosa herbal products in the natural health product (NHP) industry, impacting potential efficacy. Authentication of A. racemosa products is challenging because of the standard, multi-part analytical chemistry methods that may be too costly and not appropriate for both raw and finished products. OBJECTIVE: In this paper, we discuss developing and validating quick alternative biotechnology methods to authenticate A. racemosa herbal dietary supplements, based on the use of a species-specific hydrolysis PCR probe assay. METHODS: A qPCR-based species-specific hydrolysis probe assay was designed, validated, and optimized for precisely identifying the species of interest using the following analytical validation criteria: (1) specificity (accuracy) in determining the target species ingredient, while not identifying other non-target species; (2) sensitivity in detecting the smallest amount of the target material; and (3) reliability (repeatability and reproducibility) in detecting the target species in raw materials on a real-time PCR platform. RESULTS: The results show that the species-specific hydrolysis probe assay was successfully developed for the raw materials and powders of A. racemosa. The specificity of the test was 100% to the target species. The efficiency of the assay was observed to be 99%, and the reliability of the assay was 100% for the raw/starting and powder materials. CONCLUSION: The method developed in this study can be used to authenticate and perform qualitative analysis of A. racemosa supplements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".