Application of 12S rRNA Barcodes for the Identification of Animal-Derived Drugs
Bibliographic record
Abstract
PURPOSE. Animal-derived drugs are the major source of biological products and traditional medicine, but they are often difficult to identify, causing confusion in the clinical application. Among these medicinal animals, a number of animal species are endangered, leading to the destruction of biodiversity. The identification of animal-derived drugs and their alternatives would be a first step toward biodiversity conservation and safe medication. Until now, no effective method for identifying animal-derived drugs has been demonstrated; DNA-based species identification presents a brand-new technique. METHODS. We designed primers to amplify a 523-bp fragment of 12S rRNA and generated sequences for 13 individuals within six medicinal animal species. We examined the efficiency of species recognition based on this sequence, and we also tested the taxonomic affiliations against the GenBank database. RESULTS. All the tested drugs were identified successfully, and a visible gap was found between the inter-specific and intra-specific variation. We further demonstrated the importance of data exploration in DNA-based species identification practice by examining the sequence characteristics of relative genera in GenBank. This region of the 12S rRNA gene had a 100% success rate of species recognition within the six medicinal animal species. CONCLUSIONS. We propose that the 12S rRNA locus might be universal for identifying animal-derived drugs and their adulterants. The development of 12S rRNA for indentifying animal-derived drugs that share a common gene target would contribute significantly to the clinical application of animal-derived drugs and the conservation of medicinal animal species. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".