Evaluation of three polymerase chain reaction techniques for detection of<i>Brucella</i>DNA in peripheral human blood
Bibliographic record
Abstract
Brucellosis is a widespread zoonosis. Currently the diagnosis of this zoonosis is based on microbiological and serological laboratory tests. Polymerase chain reaction (PCR) has been used to detect DNA from Brucella. Different target genes, primer pairs, PCR techniques, and extraction procedures have previously been published for Brucella detection. But only a few of these primers have been used in human samples, and only one study has been carried out to compare sensitivity between them. In the present study, 3 sets of primers and 3 different PCR protocols amplifying 3 different regions of the Brucella genome were compared for detection of Brucella DNA in a peripheral-blood PCR assay to conclude which is most suitable for the clinical diagnostic laboratory. These 3 pairs of primers amplify 3 different fragments included in (i) a gene encoding a 31 kDa Brucella abortus antigen (B4/B5), (ii) a sequence 16S rRNA of B. abortus (F4/R2), and (iii) a gene encoding an outer membrane protein (omp-2) (JPF/JPR). Some modifications on the reported techniques were applied during the present work to improve the outcome. The results showed that the B4/B5 primer pair had the highest sensitivity for detection of positive samples (98%), the JPF/JPR primer pair detected 88.4% of positive samples, whereas F4/R2 primer pair was the least sensitive, being able to detect only 53.1% of positive samples. The specificity of the 3 techniques was 100%. The B4/B5 primer pair was also able to detect the smallest number of bacteria (700 cfu/mL), whereas JPF/JPR was able to detect 7 x 105 cfu/mL and F4/R2 was able to detect 7 x 107 cfu/mL. It is thus concluded that using the B4/B5 primer PCR with the suggested modifications is a robust assay, which meets the sensitivity requirements to be used for testing of human blood samples for brucellosis in the diagnostic laboratory.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".