Characterization of cytokine expression in milk somatic cells during intramammary infections with Escherichia coli or Staphylococcus aureus by real-time PCR
Bibliographic record
Abstract
The expression of inflammatory cytokines, including interleukin (IL)-6, IL-8, IL-12, granulocyte macrophage-colony stimulating factor (GM-CSF), tumor necrosis factor (TNF)-$\alpha$, and interferon (IFN)-$\gamma$, by milk somatic cells was characterized by real-time polymerase chain reaction (PCR) in dairy cows experimentally challenged with either E. coli (n = 8) or S. aureus (n = 8). The mRNA abundance of a target gene was calibrated with that of a reference gene ($\beta$-actin) and expressed as fold of induction over the control quarter at each time point. At no single time point did all eight quarters challenged with the same type of bacteria demonstrated increased expression of a target gene and there was large variation among animals at each given time. As a consequence, most tested comparisons were not statistically significant except the peak time points of IL-8 expression (75- and 29- fold in glands challenged with E. coli and S. aureus, respectively). However, the average fold induction of all targeted cytokines was increased in response to both bacterial challenges with the exception of IFN-$\gamma$. The expression of IFN-$\gamma$ was only increased in milk somatic cells isolated from E. coli, but not S. aureus, challenged mammary glands. Moreover, upregulated expression of cytokine genes had higher magnitudes and/or faster responses in glands challenged with E. coli in comparison with those challenged with S. aureus. We propose that the compromised upregulation of inflammatory cytokines in S. aureus infected glands may, at least partially, contribute to the chronic course of infection caused by this pathogen. Further research on identifying factors responsible for the differentially expressed cytokine profiles may be fundamental to developing strategies that mitigate the outcome of bovine mastitis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".