Improvement in detecting cytomegalovirus drug resistance mutations in solid organ transplant recipients with suspected resistance using next generation sequencing
Bibliographic record
Abstract
OBJETIVES: The aim of this study was to identify CMV drug resistance mutations (DRM) in solid organ transplant (SOT) recipients with suspected resistance comparing next-generation sequencing (NGS) with Sanger sequencing and assessing risk factors and the clinical impact of resistance. METHODS: Using Sanger sequencing as the reference method, we prospectively assessed the ability of NGS to detect CMV DRM in the UL97 and UL54 genes in a nationwide observational study from September 2013 to August 2016. RESULTS: Among 44 patients recruited, 14 DRM were detected by Sanger in 12 patients (27%) and 20 DRM were detected by NGS, in 16 (36%). NGS confirmed all the DRM detected by Sanger. The additional six mutations detected by NGS were present in <20% of the sequenced population, being located in the UL97 gene and conferring high-level resistance to ganciclovir. The presence of DRM by NGS was associated with lung transplantation (p = 0.050), the administration of prophylaxis (p = 0.039), a higher mean time between transplantation and suspicion of resistance (p = 0.038) and longer antiviral treatment duration before suspicion (p = 0.024). However, the latter was the only factor independently associated with the presence of DRM by NGS in the multivariate analysis (OR 2.24, 95% CI 1.03 to 4.87). CONCLUSIONS: NGS showed a higher yield than Sanger sequencing for detecting CMV resistance mutations in SOT recipients. The presence of DRM detected by NGS was independently associated with longer antiviral treatment.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".