Kinetics of Viral Genome Distribution in Swine Peripheral Lymphoid Organs Following Oronasal Infection with Attenuated African swine fever virus strains
Bibliographic record
Abstract
African swine fever (ASF) continues to spread across the globe, causing a severe impact on the swine industry. Passive surveillance based on testing dead pigs is one of the most effective methods for early detection of ASF incursions. We have previously shown that the superficial inguinal lymph node (SILN) is a convenient and effective sample type for ASF virus (ASFV) genome detection in pigs succumbed to highly or moderately virulent ASFV infections. In this study, we explored the distribution kinetics of ASFV into SILN and other lymphoid tissues in pigs exposed to moderately virulent ASFV strains (ASFV Estonia 2014 and ASFV Malta'78), oronasally. The ASFV genome was detected in SILNs as early as 2-3 days post-infection (dpi), peaking around 5-9 dpi. The detection of ASFV Estonia 2014 started early, and the pigs succumbed to infection faster compared to the ASFV Malta'78 infected pigs that remained longer. All pigs that succumbed to ASF had comparable levels of ASFV genomic material in the spleen and SILNs. The levels of ASFV genomic material gradually started to decrease in pigs that did not succumb to ASF, indicating possible virus clearance. In contrast, ASFV genome levels in blood and spleen samples remained relatively steady during the study period. Immunohistochemistry and in situ hybridization of spleen and SILN samples supported real-time PCR results. This study demonstrates the distribution kinetics of moderately virulent ASFV in peripheral lymph nodes and highlights the utility of SILNs for dead pig screening.
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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".