Effects of Local Melatonin Application on Implant Osseointegration
Bibliographic record
Abstract
PURPOSE: The aim of this study was to assess the effect of local melatonin administration on bone osseointegration around implants in rabbit tibiae. MATERIAL AND METHODS: Ten female, 6-month-old New Zealand rabbits were randomly divided into two groups: the experimental group, where five rabbits were treated with local application of melatonin (3 mg) to implant sites when placed into the rabbit tibia, and the control group, those who where without additive materials. Four weeks later, animals were sacrificed; tibiae were dissected from soft tissues and fixed in buffered formaldehyde, and then included in methacrylate. Histological sections were performed to be studied under light microscopy and analyzed morphometrically to evaluate the amount of bone to implant contact (BIC), trabecular area density, and cortical area density. One-way analysis of variance test was used for statistical evaluation. p < .05 was considered to be significant. RESULTS: Histological evaluation showed more trabecular reaction in the melatonin group. Morphometrical analysis showed a statistically significant increase in trabecular BIC in the melatonin group when compared with the control group (24.61% ± 2.87 vs 13.62% ± 1.44; p < .01). Cortical BIC was decreased in the melatonin group, without statistical significance (71.08 ± 3.63 vs 76.28 ± 2.57; p = 0.31). Trabecular area density was increased significantly in the melatonin group (8.68 ± 1.61 vs 4.02 ± 0.36; p < .05). Cortical area density was decreased significantly in the melatonin group (91.31 ± 1.6 vs 95.7 ± 0.5; p < .05). CONCLUSION: Within the limitation of this animal study, local melatonin application at the time of implant placement might induce more trabecular bone at implant contact and higher trabecular area density.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".