Evaluating the Potential of Galactosaminogalactan as a Diagnostic Target for Invasive <scp> <i>Aspergillosis</i> </scp>
Bibliographic record
Abstract
BACKGROUND: Early diagnosis of invasive aspergillosis (IA) is critical for the initiation of effective antifungal therapy. Currently, detection of galactomannan (GM), a secreted fungal glycan, is the most used culture-independent diagnostic test for IA. However, limitations in the sensitivity and specificity of this test have led to interest in identifying other target molecules. Galactosaminogalactan (GAG), a polysaccharide cell wall component secreted by Aspergillus hyphae, is a potential diagnostic marker for IA. OBJECTIVES: To evaluate the utility of GAG as a diagnostic target, we generated a monoclonal antibody against GAG (mAb 1D1), established a GAG enzyme-linked immunosorbent assay (ELISA), evaluated its cross-reactivity with other respiratory pathogens, and compared the performance of the GAG detection ELISA with GM antigen detection in both an in vivo mouse model and human samples from patients with pulmonary aspergillosis. RESULTS: The GAG ELISA demonstrated strong reactivity with culture supernatants from Aspergillus fumigatus and Aspergillus flavus but limited reactivity with culture supernatants of other Aspergillus spp. and non-Aspergillus filamentous fungi. In a mouse model of IA, GAG was detected in lung tissue, serum, bronchoalveolar lavage fluid (BALF), and urine samples. Although GAG was detected by mAb 1D1 staining of Aspergillus hyphae in infected human lung tissue samples, it was not detectable in the serum, BALF, and urine of patients with pulmonary aspergillosis. CONCLUSIONS: Further studies are required to determine whether the failure to detect GAG in the serum, BALF, and urine of patients with pulmonary aspergillosis is due to absence or low GAG levels or other reasons.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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".