Samples sizes required to accurately quantify viral load and histologic lesion severity at the maternal–fetal interface of PRRSV-inoculated pregnant gilts
Bibliographic record
Abstract
Porcine reproductive and respiratory syndrome virus (PRRSV) is transmitted vertically, causing fetal death in late gestation. Spatiotemporal distribution of virus at the maternal-fetal interface (MFI) is variable, and accurate assessment of viral concentration and lesions is thus subject to sampling error. Our objectives were: 1) to assess whether viral load and lesion severity in a single sample of endometrium (END) and placenta (PLC), collected near the base of the umbilical cord (the current standard), are representative of the entire organ; and 2) to compare sampling strategies and evaluate if spatial variation in viral load can be overcome by pooling of like-tissues. Spatially distinct pieces of END and PLC of 24 fetuses from PRRSV-2-infected dams were collected. PRRSV RNA quantified by RT-qPCR was compared in 5 individual pieces per fetus and in respective pools of tissue and extracted RNA. Three distinct pieces of MFI were assessed for histologic severity. Concordance correlation and kappa inter-rater agreement were used to characterize agreement among individual samples and pools. The viral load of individual samples and pools of END had greater concordance to a referent standard than did samples of PLC. Larger pool sizes had greater concordance than smaller pool sizes. Average viral load and lesion severity did not differ by location sampled, and no technical advantages of pooling tissues versus RNA extracts were found. We conclude that multiple pieces of MFI tissues must be evaluated to accurately assess lesion severity and viral load. Three pieces per fetus provided a reasonable balance of cost and logistic feasibility.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".