Evaluation of a Lateral Flow Assay for Rapid Detection of African Swine Fever Virus in Multiple Sample Types
Bibliographic record
Abstract
Antibody-based lateral flow assay (LFA) is a quick and inexpensive tool used to detect pathogens in field samples, especially in hard-to-reach remote areas that may have limited access to central laboratories during an outbreak or surveillance. In this study, we investigated the ability of a commercially available LFA, PenCheck®, to detect African swine fever virus (ASFV) in clinical samples derived from pigs infected with highly virulent ASFV strains. The assay was specific and positively identified the majority of pigs showing high fever during the early stages (between 3 and 5 days) of infection. PenCheck® LFA also detected ASFV in serum and tissue samples collected from pigs that succumbed to experimental ASFV infection and whole blood, plasma, and tissue samples from the field. The limit of detection of the assay was ASFV titer 107.80 TCID50/mL, corresponding to ASFV real-time PCR values below 23 Ct. Although the sensitivity of the assay is less than that of the laboratory-based real-time PCR assays, the results obtained with the PenCheck® LFA in this study suggest that it can be used as a herd-level, field-deployable, and easy-to-use diagnostic tool to identify ASF-affected farms when access to portable molecular assays or central laboratories is not possible.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".