Bovine Milk Extracellular Vesicles Are Osteoprotective by Increasing Osteocyte Numbers and Targeting RANKL/OPG System in Experimental Models of Bone Loss
Bibliographic record
Abstract
Studying effects of milk components on bone may have a clinical impact as milk is highly associated with bone maintenance, and clinical studies provided controversial associations with dairy consumption. We aimed to evaluate the impact of milk extracellular vesicles (mEVs) on the dynamics of bone loss in mice. MEVs are nanoparticles containing proteins, mRNA and microRNA, and were supplemented into the drinking water of mice, either receiving diet-induced obesity or ovariectomy (OVX). Mice receiving mEVs were protected from the bone loss caused by diet-induced obesity. In a more severe model of bone loss, OVX, higher osteoclast numbers in the femur were found, which were lowered by mEV treatment. Additionally, the osteoclastogenic potential of bone marrow-derived precursor cells was lowered in mEV-treated mice. The reduced stiffness in the femur of OVX mice was consequently reversed by mEV treatment, accompanied by improvement in the bone microarchitecture. In general, the RANKL/OPG ratio increased systemically and locally in both models and was rescued by mEV treatment. The number of osteocytes, as primary regulators of the RANKL/OPG system, raised in the femur of the OVX mEVs-treated group compared to OVX non-treated mice. Also, the osteocyte cell line treated with mEVs demonstrated a lowered RANKL/OPG ratio. Thus, mEVs showed systemic and local osteoprotective properties in two mouse models of bone loss reflected in reduced osteoclast presence. Data reveal mEV potential in bone modulation, acting via osteocyte enhancement and RANKL/OPG regulation. We suggest that mEVs could be a therapeutic candidate for the treatment of bone loss.
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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 | 0.001 | 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".