Increased proteolytic activity and matrix metalloprotease expression in lungs during infection by porcine reproductive and respiratory syndrome virus
Bibliographic record
Abstract
The local increase in the secretion of extracellular proteases, allowing cleavage of the extracellular matrix and thereby facilitating the infiltration of T cells, monocytes and neutrophils, is a hallmark of chronic inflammation and autoimmunity. In pulmonary genetic diseases, such as emphysema and cystic fibrosis, proteases can also favour the development of local immunodeficiency by degrading key regulators of the immune response, such as CD4, CD8, IgG, ICAM-1 and C3b receptors. Since several infectious agents can give rise to severe pulmonary disorders associated with opportunistic infections, we sought to determine whether an increase in proteolytic activity occurred during infection with porcine reproductive and respiratory syndrome virus (PRRSV), the causative agent of a new disease in swine characterized by severe respiratory problems in young pigs. Piglets were infected with the virus and bronchoalveolar lavages were collected at various times post-infection to measure the net proteolytic activity. It was shown that PRRSV infection leads to a significant increase in proteolytic activity in pulmonary fluids. Maximal activity was found at 7 and 14 days post-infection, with a return towards normal levels at day 42. Zymographic analyses showed a significant increase in the secretion of matrix metalloproteases (MMPs) 2 and 9, two enzymes involved in tissue remodelling. Histological analyses showed a correlation between the increase in proteolytic activity and the appearance of lesions that were characterized by massive lymphomononuclear cell infiltration. These results suggest that virus infection of the lungs can lead to a transient increase in proteolytic activity that could favour opportunistic infection.
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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 | 0.001 | 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".