The combined effects of low intensity pulsed ultrasound and heat on bone cell mineralization
Bibliographic record
Abstract
Low Intensity Pulsed UltraSound (LIPUS) has been shown to improve bone fracture healing in in vivo animal and human clinical studies. In vitro, this improvement has been shown through improved mineralization in bone cells. Low level heat of bone fractures has also been shown to improve healing. Moreover, low level heat has been shown to improve mineralization in bone cell cultures. The research version of a clinical LIPUS device was used in this study (Exogen® Bone Healing System, Smith & Nephew Inc., Memphis, TN). This study examines the concurrent effects of LIPUS and heat on MC3T3-E1 bone cells. The bone cells were split into four treatment groups: LIPUS, heat, LIPUS + heat, and control. The LIPUS treatment was delivered with the intensity of I SATA=30 mW/cm2 at the frequency of f=1.5 MHz for 40 minutes each day over 15 days. The heat treatment was applied at 40°C for 40 minutes each day over 15 days. The LIPUS + heat group received the treatments concurrently. Outside of heat treatment the cells were kept at 37 °C. The groups were tested for calcium mineralization using alizarin red staining and alkaline phosphatase activity in an alkaline phosphatase assay kit. All treatment groups showed statistically significantly improved mineralization when compared to the control cell cultures. Although die LIPUS and LIPUS + heat groups each showed almost a 4 fold increase in mineralization over the control, there was no statistical difference in mineralization between these two groups. Alkaline phosphatase activity was higher in both the LIPUS and the Control groups. Early results suggest that the concurrent effects of LIPUS and heat on MC3T3-E1 bone cells have no additive effect on mineralization.
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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) | 0.001 | 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".