Expression, Regulation, and Function of β-Defensins in the Bovine Mammary Glands: Current Knowledge and Future Perspectives
Bibliographic record
Abstract
β-Defensins are cationic antimicrobial peptides (AMPs) that play an important role in the innate immune defense of bovines. They are constitutively expressed in mammary glands and induced differently in response to pathogens. Their expression is influenced by various factors, including hormones, plant-derived compounds, and dietary energy imbalance. The toll-like receptors (TLRs)/nuclear factor-kappa B (NF-κB) pathway plays a crucial role in β-defensin induction, while alternative pathways such as mitogen-activated protein kinase (MAPK) and epigenetic regulation also make substantial contributions. β-Defensins exhibit bactericidal activity against a wide range of pathogens, including two major mastitis pathogens, Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), primarily through membrane disruption. β-Defensins have low cytotoxicity to host cells and demonstrate immunomodulatory properties, and pathogens also display minimal resistance to these AMPs. Given the increasing concern in antimicrobial resistance, the potential of β-defensins as natural antimicrobials has garnered considerable attention. This article provides an overview of the characteristics of bovine β-defensins, their expression pathways, their mode of action, and factors influencing their expression in the mammary glands of cattle. Additionally, it identifies the current gaps in research within this field and suggests areas that require further investigation. Understanding the regulation and function of β-defensins offers valuable insights to develop effective strategies for strengthening the immune system of mammary glands, reducing the reliance on synthetic antimicrobials, and explore novel natural antimicrobial alternatives.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".