Specific anti-SARS-CoV-2 S1 IgY-scFv is a promising tool for recognition of the virus
Bibliographic record
Abstract
As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to spread globally, a series of vaccines, antibodies and drugs have been developed to combat coronavirus disease 2019 (COVID-19). High specific antibodies are powerful tool for the development of immunoassay and providing passive immunotherapy against SARS-CoV-2 and expected with large scale production. SARS-CoV-2 S1 protein was expressed in E. coli BL21 and purified by immobilized metal affinity chromatography, as antigen used to immunize hens, the specific IgY antibodies were extracted form egg yolk by PEG-6000 precipitation, and the titer of anti-S1 IgY antibody reached 1:10,000. IgY single chain variable fragment antibody (IgY-scFv) was generated by using phage display technology and the IgY-scFv showed high binding sensitivity and capacity to S1 protein of SARS-CoV-2, and the minimum detectable antigen S1 protein concentration was 6 ng/µL. The docking study showed that the multiple epitopes on the IgY-scFv interacted with multiple residues on SARS-CoV-2 S1 RBD to form hydrogen bonds. This preliminary study suggests that IgY and IgY-scFv are suitable candidates for the development of immunoassay and passive immunotherapy for COVID-19 to humans and animals.
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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".