Effect of calcium and magnesium on inflammatory cytokines in accidentally multiple fracture adults
Bibliographic record
Abstract
ABSTRACT: Calcium (Ca) and magnesium (Mg), which play an important role in several cellular processes, is essential for normal development of the skeleton and maintenance of tissue homeostasis. Deficiency of these elements might delay bone fracture recovery or accelerates bone loss. We aimed to examine whether supplementation of trace element (TE) promotes fracture healing in accidentally fracturing adults by involvement of inflammatory mechanism.A short-term follow-up in clinic was performed. Totally, 117 subjects diagnosed with multiple fractures by traffic accidents were recruited in this study. Serum Ca and Mg levels were measured by inductively coupled plasma atomic emission spectrophotometry. Short-term changes such as serum C-reactive protein, interleukin (IL)-1β, IL-6, and tumor necrosis factor alpha in normal treatment and TE supplement groups were detected by enzyme-linked immunosorbent assay. Student t test and the Spearman correlation were performed to analyze the data.Significantly negative correlations between Ca (r = 0.7032; P < .001) and Mg (r = 0.2719; P < .05) and injury severity score were observed. Serum Ca and Mg were significantly increased at Day 5, 7, and 9 following TE supplements. After treatment, serum C-reactive protein, IL-1β, IL-6, and tumor necrosis factor alpha were significantly reduced whereas cytokine levels of the TE supplement group were found to be lower than that of the normal treatment group after Day 3.These findings suggest that Ca and Mg levels are associated with the injury severity of multiple fractures, and the supplement could reduce the inflammation, which may be beneficial for the bone recovery and disease process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".