Multiple Influenza A (H3N2) Mutations Conferring Resistance to Neuraminidase Inhibitors in a Bone Marrow Transplant Recipient
Bibliographic record
Abstract
Immunocompromised patients are predisposed to infections caused by influenza virus. Influenza virus may produce considerable morbidity, including protracted illness and prolonged viral shedding in these patients, thus prompting higher doses and prolonged courses of antiviral therapy. This approach may promote the emergence of resistant strains. Characterization of neuraminidase (NA) inhibitor (NAI)-resistant strains of influenza A virus is essential for documenting causes of resistance. In this study, using quantitative real-time PCR along with conventional Sanger sequencing, we identified an NAI-resistant strain of influenza A (H3N2) virus in an immunocompromised patient. In-depth analysis by deep gene sequencing revealed that various known markers of antiviral resistance, including transient R292K and Q136K substitutions and a sustained E119K (N2 numbering) substitution in the NA protein emerged during prolonged antiviral therapy. In addition, a combination of a 4-amino-acid deletion at residues 245 to 248 (Δ245-248) accompanied by the E119V substitution occurred, causing resistance to or reduced inhibition by NAIs (oseltamivir, zanamivir, and peramivir). Resistant variants within a pool of viral quasispecies arose during combined antiviral treatment. More research is needed to understand the interplay of drug resistance mutations, viral fitness, and transmission.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".