In vitro expression of tumor necrosis factor-alpha, interleukin 1beta, and interleukin 8 mRNA by bovine macrophages following exposure to Porphyromonas levii.
Bibliographic record
Abstract
The objective was to evaluate the pro-inflammatory response of bovine macrophages towards Porphyromonas levii, an etiologic agent of acute interdigital phlegmon, by evaluating the mRNA expression of tumor necrosis factor-alpha (TNF-alpha), interleukin 1beta (IL-1beta), and interleukin 8 (IL-8). Bovine macrophages detect the presence of bacteria, such as P. levii, and respond by upregulating transcription of the genes for pro-inflammatory cytokines TNF-alpha and IL-1beta in addition to the neutrophil chemoattractant IL-8. Monocytes were isolated from blood obtained from Holstein steers. These cells were cultured and differentiated into macrophages over 7 d, followed by exposure to P. levii, Escherichia coli lipopolysaccharide (LPS), or tissue culture medium for 1, 1.5, 2, 4, 6, 8,12, or 24 h. Total RNA was extracted, and reverse transcriptase polymerase chain reaction was conducted to examine the presence of TNF-alpha, IL-1beta, or IL-8 mRNA. Products were visualized on agarose gels to determine the presence or absence of cytokine mRNA amplified DNA. Bovine macrophages, when exposed to P. levii or E. coli LPS, produced mRNA for TNF-alpha, IL-1beta, and IL-8. Expression of all 3 cytokines was higher in the P. levii and LPS-exposed macrophages at all time points examined, compared with tissue culture medium-treated cells. Expression of these cytokines by macrophages is likely directly involved in orchestration of the immune response, and particularly in neutrophil recruitment to affected tissues in acute interdigital phlegmon.
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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".