Standardization and characterization of antigens for the diagnosis of aspergillosis
Bibliographic record
Abstract
The aim of this study was to develop and characterize antigens for the diagnosis of aspergillosis. Nine strains of Aspergillus species Aspergillus fumigatus , Aspergillus flavus , and Aspergillus niger were grown in Sabouraud and Smith broth to produce exoantigens. The antigens were tested by immunodiffusion against sera from patients with aspergillosis and other systemic mycoses. The protein fraction of the antigens was detected by SDS-PAGE; Western blot and representative bands were assessed by mass spectrometry coupled to a nano Acquity UltraPerformance LC and analyzed by the Mascot search engine. Concurrently, all sera were tested with Platelia Aspergillus EIA. The most reactive antigens to sera from patients infected by A. fumigatus were produced by A. fumigatus MG2 Sabouraud and pooled A. fumigatus Sabouraud samples, both with a sensitivity of 93% and specificity of 100% and 97%, respectively. Aspergillus niger and A. flavus antigens were reactive against A. niger and A. flavus sera, each one with a sensitivity and specificity of 100%. Two proteins, probably responsible for antigenic activity, β-glucosidase in A. fumigatus and α-amylase in A. niger were attained. The commercial kit had a specificity of 22%, sensitivity of 100%, positive predictive value of 48%, and negative predictive value of 100%. The antigens produced showed high sensitivity and specificity and can be exploited for diagnostics of aspergilloma.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".