Assessment and Performance of Pooled Serum Samples for Monitoring Farm-Level Immunity in Tilapia Infected with Tilapia Lake Virus
Bibliographic record
Abstract
Effective surveillance of viral disease in fish populations is critical for disease control and the sustainable development of global aquaculture. Here, we evaluated the application and performance of pooled serum samples using an indirect ELISA based on recombinant segment 4 protein to assess farm-level immunity in tilapia infected with Tilapia lake virus (TiLV). The TiLV-S4 ELISA was developed using a recombinant nucleoprotein (segment 4) antigen, optimized through checkerboard titration, and validated for repeatability and reproducibility, with intra- and inter-assay coefficients of variation below 10%. A pooling strategy was used to combine multiple serum samples before testing for the presence of TiLV-specific antibodies using an enzyme-linked immunosorbent assay (ELISA). Our results showed that pooling five serum samples was effective for detecting TiLV-specific antibodies, particularly when multiple seropositive individuals were presented in the pool, supporting its application for population-level surveillance. However, ELISA sensitivity may be reduced when only one seropositive sample is included in the pool, due to the dilution effects. Despite this limitation, pooled testing yielded a high proportion of positive results, suggesting similar detection performance in many cases. Overall, the pooling strategy provides a cost-effective and time-efficient approach for large-scale monitoring of immune status in tilapia populations.
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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".