Isolation of Neutralizing Human Single Chain Antibodies Against Conserved Hemagglutinin Epitopes of Influenza a Virus H3N2 Strain.
Bibliographic record
Abstract
BACKGROUND: Immunotherapies using monoclonal antibodies against influenza A hemagglutinin (HA) has been an effective means for controlling Influenza spread. An alternative method for viral prophylaxis and treatment is the development of human single-chain variable fragment (scFv) antibodies with no human anti-mouse antibody (HAMA) response and high specificity. In the present study, two highly conserved sequences of HA were used to select specific neutralizing scFvs against H3N2 strain of influenza A virus. METHODS: Biopanning process was performed to isolate specific scFv antibodies against highly conserved HA sequences, aa173-181 and 227-239, of the influenza A H3N2 strain from a scFv library. The peptide-binding specificity of the selected clones was examined via phage ELISA. The soluble forms of the clones were prepared and assessed using western blot analysis and neutralization efficiency of the selected clones were examined by TCID50 neutralizing assay and real-time PCR. RESULTS: scFv 1 and scFv 2 were selected against HA of H3N2 influenza A virus with frequencies of 95% and 30% in the panning process, respectively. Western blot analysis confirmed the scFv band size. Significant neutralization in the presence of scFv 1 and scFv 2 were obtained. Real time PCR revealed significant decrease in viral copy number. CONCLUSION: Two specific neutralizing scFvs against two highly conserved neutralizing epitopes of the influenza A virus HA glycoprotein were selected. A strong neutralization effect of scFv1, showed the potential of this antibody for H3N2 influenza A controlling in the viral spread.
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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 | 0.001 |
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".