Two proteins, one goal: ELISAs based on p32 and L1R for LSDV antibodies detection
Bibliographic record
Abstract
Introduction Lumpy skin disease virus (LSDV), a member of the Capripoxvirus genus, poses a significant threat to livestock health and productivity in both endemic and newly affected regions. The disease is primarily transmitted by blood-feeding insects, leading to fever, cutaneous nodules, lymphadenopathy, and substantial economic losses. While vaccination remains the cornerstone of control efforts, effective surveillance—especially in high-risk areas—relies on robust and scalable diagnostic tools. Although the virus neutralization test is considered the reference standard among serological assays for detecting neutralising antibodies, it is labor-intensive and requires high-containment laboratories. Methods In this study, we produced and evaluated two recombinant LSDV antigens: ORF074 (p32), a well-known immunodominant protein, and ORF060 (homologous to the Vaccinia virus L1R), a myristoylated membrane protein identified as a promising immunogenic target. Both proteins were expressed in E. coli . Recombinant p32 was purified under native conditions, whereas recombinant L1R required denaturation and refolding. The antigens were used to develop two indirect ELISAs, and were evaluated using sera from experimentally infected cattle, as well as both vaccinated and infected field samples from Albania and Serbia. Results and discussion Both assays demonstrated high immunoreactivity and strong concordance with the VNT. These results support the suitability of both antigens for use in serological assays and suggest that a combined, multi-target ELISA approach could enhance diagnostic sensitivity. Once validated for routine use, these novel tools may significantly improve large-scale, cost-effective serological surveillance of LSDV in endemic and at-risk regions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".