A novel rat CVB1-VP1 monoclonal antibody 3A6 detects a broad range of enteroviruses
Bibliographic record
Abstract
Enteroviruses (EVs) are common RNA viruses that cause diseases ranging from rash to paralytic poliomyelitis. For example, EV-A and EV-C viruses cause hand-foot and mouth disease and EV-B viruses cause encephalitis and myocarditis, which can result in severe morbidity and mortality. While new vaccines and treatments for EVs are under development, methods for studying and diagnosing EV infections are still limited and therefore new diagnostic tools are required. Our aim was to produce and characterize new antibodies that work in multiple applications and detect EVs in tissues and in vitro. Rats were immunized with Coxsackievirus B1 capsid protein VP1 and hybridomas were produced. Hybridoma clones were selected based on their reactivity in different immunoassays. The most promising clone, 3A6, was characterized and it performed well in multiple techniques including ELISA, immunoelectron microscopy, immunocyto- and histochemistry and in Western blotting, detecting EVs in infected cells and tissues. It recognized several EV-Bs and also the EV-C representative Poliovirus 3, making it a broad-spectrum EV specific antibody. The 3A6 rat monoclonal antibody can help to overcome some of the challenges faced with commonly used EV antibodies: it enables simultaneous use of mouse-derived antibodies in double staining and it is useful in murine models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".