Genomic epidemiology of European <i>Aspergillus fumigatus</i> causing COVID-19-associated pulmonary aspergillosis in Europe
Bibliographic record
Abstract
Abstract The opportunistic fungus Aspergillus fumigatus has been found to cause coinfections in patients with severe SARS-CoV-2 virus infection, leading to COVID-19-associated pulmonary aspergillosis (CAPA). The CAPA all-cause mortality rate is approximately 50% and may be complicated by azole-resistance. Genomic epidemiology can help shed light on the genetics of A. fumigatus causing CAPA including the prevalence of alleles that are associated with azole-resistance. Here, a population genomic analysis of 21 CAPA isolates from four European countries is presented. The CAPA isolates were compared with A. fumigatus from a wider population of 167 non-CAPA clinical isolates and 73 environmental isolates. Bioinformatic analysis and antifungal susceptibility testing were performed to quantify resistance and identify possible genetically-encoded azole-resistant mechanisms. Phylogenetic analysis of the 21 CAPA isolates showed a lack of genetic distinction from the wider A. fumigatus population, with isolates distributed within two distinct clades (A and B), with the majority of the CAPA isolates in clade B (71.4%). The prevalence of phenotypic azole-resistance in CAPA was 14.3% ( n =3/21); all three CAPA isolates contained a known resistance-associated cyp51A polymorphism. CAPA isolates are drawn from the wider A. fumigatus population rather than forming a unique genetic background showing that COVID-19 patients are susceptible to the entire A. fumigatus population. However, the relatively high prevalence of azole-resistance alleles that we document poses a threat to treatment success rates, warranting enhanced detection and surveillance of A. fumigatus genotypes in these patients. Furthermore, potential changes to antifungal first-line treatment guidelines may be needed to improve patient outcomes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".