Histologic evaluation of chitosan as an accelerator of bone regeneration in microdrilled rat tibias.
Bibliographic record
Abstract
BACKGROUND: Chitosan compounds have been shown to be suitable bone replacement materials. To evaluate the accelerating effects of chitosan on the bone regeneration process and assessing its histopathological adverse effects, we conducted this study on rat tibias. MATERIALS AND METHODS: In a laboratory experimental study, micro-drilled bone defects were created in the upper tibia of each leg in 15 adult male rats. The defect in the right leg, filled by the chitosan powder, was compared with the untreated defect in the left leg in each rat at 1, 2, and 4 weeks after surgery. Bone repair and inflammation in each specimen was blindly graded by a pathologist. Reaction to the foreign body and the amount of the remaining chitosan were studied in chitosan-treated specimens at the three stages of the study. RESULTS: Bone repair was significantly faster in the chitosan group, 1 week (P = 0.01) and 4 weeks (P = 0.038) after surgery, while the difference was not significant at the 2-week stage (P = 0.197) between chitosan and control groups. Chitosan-induced inflammation was not significant in any stage of the study. Reaction to the foreign body was seen in one case at 2 weeks and one case at 4 weeks postoperation. CONCLUSION: Chitosan significantly accelerated the bone regeneration process in rat tibias. Regarding its biocompatibility and osteoinductivity, it can be studied as a biomaterial in human bone 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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".