Modification of gelatin–<scp>DNA</scp> interaction for optimised <scp>DNA</scp> extraction from gelatin and gelatin capsule
Bibliographic record
Abstract
BACKGROUND: Poor quality and quantity of DNA extracted from gelatin and gelatin capsules often causes failure in the determination of animal species using PCR. Gelatin, which is mainly derived from porcine and bovine, has been a matter of concern among customers in order to fulfill religious obligation and safety precaution against several transmissible infectious diseases associated with bovine species. Thus, optimised DNA extraction from gelatin is very important for successful real-time PCR detection of gelatin species. In this work, the DNA extraction method was optimised in terms of lysis incubation period and inclusion of pre-treatment pH modification of samples. RESULTS: The yield of DNA extracted from porcine gelatin was significantly increased when the pH of the samples was adjusted to pH 8.5 prior to DNA precipitation with isopropanol. The optimal pH for DNA precipitation from bovine gelatin solution was then determined at the original pH range of solution: pH 7.6 to 8. A DNA fragment of approximately 300 base pairs was available for PCR amplification. CONCLUSION: DNA extracted from gelatin and commercially available capsules has been successfully utilised for species detection using real-time PCR assay. However, significant adulterations of porcine and bovine in pure gelatin and capsules have been detected, which require further analytical techniques for validation. © 2015 Society of Chemical Industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".