Serological analysis of specific IgA to <i>Chlamydia pneumoniae</i>: increased sensitivity of IgA antibody detection using prolonged incubation and high antigen concentration
Bibliographic record
Abstract
The microimmunofluorescence technique (MIF) is recognized as the only test hitherto allowing discrimination between different Chlamydia species and is considered to be the reference method for serology. This method was developed for the detection of IgG and IgM antibodies only. We investigated the effects of some test parameters on the ability of MIF to detect Chlamydia pneumoniae IgA. These parameters were the time needed for binding of serum IgA to C. pneumoniae antigen and the effect of antigen concentration on the outcome of IgA antibody testing. It was found that the most sensitive MIF tests for the detection of serum IgA antibodies were those in which an overnight incubation of sera with antigen slides containing high concentrations of chlamydial elementary bodies was employed. The number of patients with chronic infections found to have elevated IgA titers was increased by 25% using longer incubation times for the antibody-antigen reaction. Thirty-two sera from patients with coronary artery disease and confirmed chronic C. pneumoniae infection were used to compare antigen slides with low and high concentrations of elementary bodies with respect to IgA levels; 31/32 patients were found to have specific IgA antibodies to C. pneumoniae using the high antigen concentration, as opposed to only 22/32 patients using the low antigen concentration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".