Ultrasensitive Fluorogenic Reagents for Neuraminidase Titration
Bibliographic record
Abstract
Abstract Influenza viral neuraminidase plays a crucial role during infections. It is a major target for the development of anti‐influenza drugs and is also attracting increasing attention as a vaccine target as evidence accumulates that neuraminidase‐neutralizing antibodies contribute to protection. However, no method currently exists to accurately and efficiently measure concentrations of active neuraminidase in virus samples or other crude mixtures, which hampers development on both fronts. In this report, we describe the development of a selective and sensitive active‐site titration reagent for neuraminidase that can quantify viral neuraminidases down to sub‐nanomolar levels in crude samples, with no background from non‐viral neuraminidases. By using this reagent, we determined accurate kcat values for six influenza A and two influenza B neuraminidases for the first time. We also quantified the neuraminidase content in a commercial influenza vaccine, thus demonstrating that this titration reagent opens the possibility for better vaccine analysis.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".