Colostrum-derived extracellular vesicles: potential multifunctional nanomedicine for alleviating mastitis
Bibliographic record
Abstract
Bovine mastitis is an infectious disease that causes substantial economic losses to the dairy industry worldwide. Current antibiotic therapy faces issues of antibiotic misuse and antimicrobial resistance, which has aroused concerns for both veterinary and human medicine. Thus, this study explored the potential of Colo EVs (bovine colostrum-derived extracellular vesicles) to address mastitis. Using LPS-induced murine mammary epithelial cells (HC11), mouse monocyte macrophages (RAW 264.7), and a murine mastitis model with BALB/C mice, we evaluated the safety and efficacy of Colo EVs, in vivo and in vitro. Colo EVs had favorable biosafety profiles, promoting cell proliferation and migration without inducing pathological changes after injection into murine mammary glands. In LPS-induced murine mastitis, Colo EVs significantly reduced inflammation, improved inflammatory scores, and preserved tight junction proteins while protecting milk production. Additionally, in vitro experiments demonstrated that Colo EVs downregulated inflammatory cytokine expression, reduced inflammatory markers, and attenuated NF-κB pathway activation. In summary, we inferred that Colo EVs have promise as a therapeutic approach for mastitis treatment, owing to their anti-inflammatory properties, potentially mediated through the NF-κB signaling pathway modulation.
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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) | 0.000 | 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".