A Validated Real-Time PCR Method for the Specific Identification of Probiotic Strain <i>Lactobacillus rhamnosus</i> GG (ATCC 53103)
Bibliographic record
Abstract
BACKGROUND: Strain Lactobacillus rhamnosus GG is one of the best-studied and most widely used probiotic strains, with various health benefits. Because probiotic health benefits and safety are strain specific, the availability of a reliable assay for specific identification of Lactobacillus rhamnosus GG is vital to ensure probiotic efficacy. OBJECTIVE: To design and validate a probe-based real-time PCR assay for specific identification of strain Lactobacillus rhamnosus GG. METHOD: Rapid Annotation using Subsystem Technology (RAST) was used to find a unique sequence region in the genome of Lactobacillus rhamnosus GG. A probe-based assay was designed and evaluated for specificity, sensitivity, efficiency, repeatability, and reproducibility. RESULTS: RAST identified a unique gene coding for a hypothetical protein in the genome of Lactobacillus rhamnosus GG. The assay successfully amplified all 22 target samples and did not amplify any of the 28 non-target strains, achieving 100% true positive and 0% false positive results. The Limit of Detection (LOD) was determined to be 0.001 ng. Reaction efficiency values, from three dilution series, were 96.4%, 93.3%, and 96.8% with R square values of 0.9974, 0.9981, and 0.9998, respectively. Relative standard deviation (RSD, %) of repeatability was below 1% and RSD of reproducibility was below 4%. CONCLUSIONS: This Lactobacillus rhamnosus GG specific assay proved to be specific, sensitive, efficient, and reproducible. Since the assay was evaluated on two real-time PCR platforms, including a portable one, the assay can be used for onsite testing throughout the supply chain. HIGHLIGHTS: The availability of validated and reliable assays for strain-specific identification plays a vital role in achieving compliance in probiotic products.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".