Biophysical Parameters of Plasma-Derived Extracellular Vesicles as Potential Biomarkers of Bone Disturbances in Breast Cancer Patients Receiving an Individualized Nutrition Intervention
Bibliographic record
Abstract
Extracellular vesicles (EVs) are implicated in several biological conditions, including bone metabolism disturbances in breast cancer patients (BCPs). These disorders hinder the adjustment of nutrition interventions due to changes in bone mineral density (BMD). The biophysical properties of EVs (e.g., size or electrostatic repulsion) affect their cellular uptake, however, their clinical relevance is unclear. In this study, we aimed to investigate the association between the biophysical properties of the plasma-derived EVs and BMDs in BCPs who received an individualized nutrition intervention during the first six months of antineoplastic treatment. As part of the nutritional assessment before and after the intervention, body composition including bone densitometry and plasma samples were obtained. In 16 BCPs, EVs were isolated using ExoQuick® and their biophysical properties were analyzed using light-scattering techniques. We found that the average hydrodynamic diameter of large EVs was associated with femoral neck bone mineral content, lumbar spine BMD, and neoplasms’ molecular subtypes. These results provide evidence that EVs play a role in BCPs’ bone disorders and suggest that the biophysical properties of EVs may serve as potential nutritional biomarkers. Further studies are needed to evaluate EVs’ biophysical properties as potential nutritional biomarkers in a clinical context.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".