Improving sensitivity of single tube nested PCR to detect fastidious microorganisms
Bibliographic record
Abstract
Single Tube Nested PCR (ST-nPCR) is of value to clinical laboratories with limited settings for the detection of fastidious microorganisms. The detection sensitivity of ST-nPCR is dependent on ensuring minimal leftovers of outer primers during the second round of the reaction. In this work, we investigated various approaches to optimize the performance of outer primers, including decreasing outer primer concentrations; using antisense oligonucleotides to block outer primers; using chemically modified inner primers; and using Q5 Taq polymerase that lacks 5′-3′ exonuclease and strand displacement capabilities. These solutions were tested on C. abortus and C. psittaci , which are both fastidious intracellular bacteria that are difficult to diagnose. The best obtained result was by using Q5 Taq polymerase. A detection limit with a range between 0.1 and 1 ag was achieved, which corresponds to a range between 0.2 and 2 copies of the plasmid positive control. This level of sensitivity is comparable or even better than the sensitivity achieved by TaqMan probe based real-time PCR assays. The assay was validated using 70 veterinary clinical samples from small ruminant abortions and 10% of these samples gave positive results. In conclusion, sensitivity of ST-nPCR to detect fastidious microorganisms can be improved by using Taq polymerases that lacks 5′-3′ exonuclease. The proposed assay is affordable and applicable to a wide range of fastidious pathogens and can be suitable for laboratories with limited settings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".