A new DIG‐PCR‐EIA method for the detection of <i>Chlamydia pneumoniae</i> DNA in clinical samples
Bibliographic record
Abstract
Chlamydia pneumoniae, a common respiratory pathogen, may also play a role in the pathogenesis of other chronic conditions. For accurate detection of infected persons and verification of results obtained by other PCR methods, a DIG-PCR-EIA method was evaluated. In the DIG-PCR-EIA, a 437 bp DNA sequence was amplified and hybridized with a newly synthesized 229 bp biotin-labeled probe. The end product was detected by an enzyme immunoassay. The sensitivity of DIG-PCR-EIA was compared with Southern blot hybridization and one-step HR/HL PCR, which was the routine method used. DNA was detected to the level of 20 elementary bodies of DIG-EIA-PCR compared to less than 2 by Southern blot, and 200 by HR/HL PCR. Thus a 100-fold increase in sensitivity could be expected by DIG-EIA-PCR compared to the routine method. Throat swabs and adenoid tissue from 22 children with otitis and middle ear secretions from 29 children, as well as throat swabs from 179 blood donors, were analyzed with DIG-EIA-PCR, HL/HR PCR and nested touchdown PCR. 32% of the ear secretions were positive by DIG-EIA-PCR as compared to 5% by the other two methods. Three adenoid tissue samples were positive by all methods applied. Among the child and adult throat samples, 18% and 32%, respectively, were positive by DIG-EIA-PCR and 5% and 10% by HR/HLPCR. The results indicate the suitability of DIG-PCR-EIA for verification of results of HR/HL PCR. DIG-PCR-EIA has a potential for increased sensitivity and adaptation for automation. It should be further evaluated using various types of tissue specimens and DNA extraction methods.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.004 | 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".