Analytical Comparison of <i>In Vitro</i> -Spiked Human Serum and Plasma for PCR-Based Detection of Aspergillus fumigatus DNA: a Study by the European Aspergillus PCR Initiative
Bibliographic record
Abstract
The use of serum or plasma for Aspergillus PCR testing facilitates automated and standardized technology. Recommendations for serum testing are available, and while serum and plasma are regularly considered interchangeable for use in fungal diagnostics, differences in galactomannan enzyme immunoassay (GM-EIA) performance have been reported and are attributed to clot formation. Therefore, it is important to assess plasma PCR testing to determine if previous recommendations for serum are applicable and also to compare analytical performance with that of serum PCR. Molecular methods testing serum and plasma were compared through multicenter distribution of quality control panels, with additional studies to investigate the effect of clot formation and blood fractionation on DNA availability. Analytical sensitivity and time to positivity (TTP) were compared, and a regression analysis was performed to identify variables that enhanced plasma PCR performance. When testing plasma, sample volume, preextraction-to-postextraction volume ratio, PCR volume, duplicate testing, and the use of an internal control for PCR were positively associated with performance. When whole-blood samples were spiked and then fractionated, the analytical sensitivity and TTP were superior when testing plasma. Centrifugation had no effect on DNA availability, whereas the presence of clot material significantly lowered the concentration (P = 0.028). Technically, there are no major differences in the molecular processing of serum and plasma, but the formation of clot material potentially reduces available DNA in serum. During disease, Aspergillus DNA burdens in blood are often at the limits of PCR performance. Using plasma might improve performance while maintaining the methodological simplicity of serum testing.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".