Quantitation of Chlamydia trachomatis 16S rRNA Using NASBA Amplification and a Bioluminescent Microtiter Plate Assay
Bibliographic record
Abstract
We developed a nucleic acid sequence based amplification (NASBA) assay which employs the recombinant photoprotein Aequorin in a microtiter plate format for detection and quantitation of C. trachomatis that may be useful in large scale epidemiological studies aimed at improving our understanding of factors affecting transmission of this sexually transmitted pathogen. The conditions for NASBA amplification of the16S rRNA target were optimized (90 mM KCl, 12 mM MgCl(2), 0.2 microM P1 and P2 primers), amplified RNA was captured by a biotin-labelled capture probe immobilized onto streptavidin coated microtiter plates and detected with an FITC-labelled oligonucleotide probe and Aequorin-anti-FITC antibody conjugate. The analytical sensitivity of NASBA was 1,000 in vitro generated RNA transcripts and 1.6 IFU of C. trachomatis. The sensitivity of NASBA using the bioluminescent assay was 10 fold higher than Northern blotting. Time course amplification experiments performed with 10 fold serial dilutions of target established that amplification was linear at 75 min and extended over a range of five log units of input RNA copy number. Linear regression analysis confirmed a linear fit for the data with r(2) = 0.959 (p < 0.004). A double log plot of RLU signal versus copy number was linear; analysis of residuals from a series of runs tests confirmed a fit with a linear model (number of runs = 3, p = 0.5 where p < 0.05 indicates statistical deviation from a linear model). NASBA amplification coupled with bioluminescent detection in a microtiter plate format should provide a useful tool for quantitation of C. trachomatis in clinical specimens for use in epidemiological studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".