Understanding fetal immune responses to congenital porcine reproductive and respiratory syndrome virus infection
Bibliographic record
Abstract
Abstract Porcine Reproductive and Respiratory Syndrome (PRRS) is one of the most economically significant diseases in the global swine industry, causing late-term abortions, early farrowing, and respiratory disease. Few studies have previously explored the immune pathways that alter fetal PRRS resistance/ susceptibility. Using a model where 3rd trimester pregnant gilts were euthanized at 2, 5, 8, 12, or 14 days post infection (DPI) with PRRS virus (PRRSV), samples from the fetal thymus and placenta, and maternal endometrium were collected and viral loads measured (U Saskatchewan) to determine when the virus crosses the placental barrier and infects each fetus. RNA was extracted with a Qiagen RNA Isolation kit, and gene expression determined using a 220 gene NanoString array to evaluate differential expression (DE) of genes and biomarkers previously predicted to alter PRRS resistance and susceptibility. Selected biomarkers that were measured included interferon signaling pathways, TREM1, HMGB1, B and T cell receptors, cell division, apoptosis, tissue remodeling and epithelial integrity, based on pathways identified using Ingenuity Pathway Analysis. Fetuses from the control gilt will be compared to those from three PRRSV infected gilts from the same DPI based on serum and thymus viral levels, comparing their viral negative (uninfected) and viral positive (infected) fetuses from the same litter. Comparisons of DE genes will be made between neighboring fetuses and across DPI. Data should reveal immunological pathways that contribute to fetal susceptibility. Understanding these mechanisms will benefit future research dedicated to exploring targeted approaches to halt the congenital spread of this disease.
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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".