Novel single-chain antibodies against highly conserved epitopes in the hemagglutinin of influenza A viruses: Promising agents for universal therapies.
Bibliographic record
Abstract
OBJECTIVES: Development of new antibodies with broad activity would provide anti-influenza prophylaxis and treatment. Human single-chain variable fragments (scFvs) are considered effective agents against viruses. In this study specific human scFvs against highly conserved epitopes in the hemagglutinin (HA) of influenza A viruses were selected and their neutralizing activity was evaluated. MATERIALS AND METHODS: Bioinformatic methods were used to evaluate HA epitopes. The panning process selected specific clones from a scFv library. PCR and DNA fingerprinting differentiated the common patterns. Soluble forms of scFvs were produced and evaluated using Western blot analysis. The neutralizing effects of anti-HA scFvs were assessed by microneutralization assay using MDCK cells. Real-time PCR was done to determine the exact copy number of the virus following neutralization. RESULTS: Bioinformatic evaluation confirmed the antigenicity and accessibility of the epitopes. Four specific anti-HA scFvs, scFvs I, II, I', and II' were selected. The scFvs neutralized 2009 H1N1 pandemic and 83.34%, 79.17%, 75%, and 62.5% reduction in the virus titers were obtained following treatments with scFv-II', I, I', and II, respectively. Real-time PCR demonstrated 98.6%, 95.7%, 95.26%, and 91.19% reductions in virus numbers following neutralization with scFv-II', I, I', and II, respectively. CONCLUSION: Anti-HA scFvs selected against highly conserved HA of influenza A virus with high neutralizing effects, offer novel human antibodies for prophylaxis and treatment of a wide range of influenza viruses including different subtypes of H1N1, H3N2, and H5N1 influenza A virus. The antibodies have the potential to be used for universal therapy.
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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 | 0.000 | 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".