Immune responses of porcine airway epithelial cells to poly(I:C), a synthetic analogue of viral double-stranded RNA
Bibliographic record
Abstract
Yang, X.-q., Wang, L., Li, H.-t. and Liu, D. 2015. Immune responses of porcine airway epithelial cells to poly(I:C), a synthetic analogue of viral double-stranded RNA. Can. J. Anim. Sci. 95: 13–20. Swine respiratory disease (SRD) is one of the most economically important diseases affecting the pig industry. The main infectious agents that cause SRD are viruses, but the molecular pathogenesis of viral SRD has not been extensively studied. Here, using digital gene expression tag profiling, the global transcriptional responses to poly(I:C), a synthetic analogue of viral double-stranded RNA, was analyzed in porcine airway epithelial cells (PAECs). The profiling analysis revealed numerous differentially expressed genes (DEGs), including unknown sequences in the porcine nucleotide databases. Gene ontology enrichment analysis showed that DEGs were mainly enriched in response to stress (GO: 0006950), of which, defense response is one sub-process. Poly(I:C) challenge induced a general inflammation response as indicated by marked upregulation of a variety of pathogen recognition receptors, interferon-stimulated genes, proinflammatory cytokines, and chemokines, together with the significant downregulation of anti-inflammatory molecules. Furthermore, the antiapoptotic pathway was triggered, as demonstrated by the significant suppression of molecules involved in the induction of apoptosis, together with the significant stimulation of putative inhibitor of apoptosis. The results indicate that PAECs initiated defense against poly(I:C) challenge through the inflammation responses, whereas poly(I:C) can utilize antiapoptotic pathway to evade host defense.
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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 | 0.001 |
| 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".