Bovine casein peptides co-stimulate naive macrophages with lipopolysaccharide for proinflammatory cytokine production and nitric oxide release
Bibliographic record
Abstract
Three bovine casein peptides, ie LLY, PGPIPN and TTMPLW, were used to investigate their effects on cytokine (TNF-α and IL-6) production and nitric oxide (NO) release by murine bone marrow macrophages (BMMs). The results showed that these peptides alone were incapable of stimulating cytokine production or NO release in naive or IFN-γ-primed BMMs. However, when BMMs were co-incubated with the peptides at a concentration of 1.0 µM and lipopolysaccharide (LPS; 100 ng ml−1), an augmentative effect on TNF-α, IL-6 and NO production was observed. Of the three peptides, TTMPLW had the greatest augmentative effect on NO production by LPS-stimulated BMMs and induced the highest amount of TNF-α production at a concentration of 1.0 µM. All the peptides at a concentration of 1.0 µM stimulated IL-6 production by BMMs. TNF-α was neutralised by anti-TNF-α monoclonal antibody and the release of NO was reduced by about 33.3% (p < 0.01). These results demonstrate that bovine casein peptides can co-stimulate naive macrophages with LPS for proinflammatory cytokine production and NO release and may play a role in host defence against pathogens. © 2000 Society of Chemical Industry
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".