Probiotics: Can it modulate fracture healing?
Bibliographic record
Abstract
OBJECTIVE: Fractures remain a huge burden and their management adversely affects individuals' function and productivity during the lengthy healing period. Gut microbiota exerts a systemic influence on diverse aspects of host physiology, including bone. The primary objective of this study was to evaluate if oral probiotic treatment before or after a fracture in a mouse model could increase cytokines and biomarkers essential for bone healing with subsequent improvement in the biomechanical properties of the healed callus. METHODS: Femoral osteotomy and intramedullary pinning were performed on C57BL/6 mice. Group 1 received either control PBS or probiotic via oral gavage for 5 weeks before fracture (pre-fracture). Group 2 received equivalent treatments for 4 weeks only after fracture (post-fracture). Fracture calluses were harvested on day 3 and 7 for RT-qPCR to quantify osteogenic-related inflammatory cytokines and bone biomarkers. Fractured femurs were evaluated day 28 post-osteotomy via microstructural analysis (μCT) and biomechanical testing (torsion). RESULTS: Mice treated with probiotics pre-fracture (group 1) showed significantly increased gene expression on day 3 of cytokines TGF-β, IL-6 and IL-17F and a corresponding increase in gene expression on day 7 for Col1 and Runx2. Significant improvement was also seen in bone volume fraction, bone mineral density, tissue mineral density, maximum yield torque, stiffness and strain energy. Mice treated with probiotics post-fracture (group 2), demonstrated no changes in cytokine or bone marker gene expression with no significant changes on microstructural analysis. However, significant increases were seen in twist angle at failure and strain energy, with a corresponding reduction in torsional stiffness. CONCLUSION: Our results suggest that oral probiotic administration, before or after a fracture, may sufficiently alter the gut flora microenvironment leading to improved bone healing biomechanical properties. The use of probiotics may provide a cost-effective and low-risk adjunctive therapy to improve fracture healing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".