Effect of Insulin Treatment on Orthodontic Tooth Movement and Osteoclast Count in Diabetic Rats
Bibliographic record
Abstract
Objectives: To estimate osteoclast count and orthodontic tooth movement in a group of insulin treated diabetic rats. Study Design: Animal experimental study. Setting: Animal House and Histopathology Department, Post-graduate Medical Institute, Lahore. Duration: One year, June 2013- June 2014. Sampling Technique: Simple random allocation Methodology: Total 44 male wistar rats were selected and equally divided into Normoglycemic and Insulin Treated Diabetic groups. Type-1 diabetes mellitus was induced by injecting streptozotocin then treated with Insulin injections. Citrate buffer solution was injected in normoglycemic group. The rats were anesthetized with cocktail of ketamine and xylazine injections. Using split mouth design orthodontic appliance was placed only on right side of maxilla while left side was kept as control. Maxillary right first molar was moved mesially by applying 10 cN force. All rats were euthanized on 21st day and orthodontic tooth movements were recorded using digital vernier calliper. Serial transverse sections of dissected maxilla in the interradicular bone at furcation area of first molar distobuccal root were obtained for quantification of osteoclasts by histomorphometry. Results: Mean osteoclast count in normoglycemic group was 2.94±0.42 and 2.65±0.36 in insulin treated diabetic group with significant difference, while no osteoclast found on control side. Mean orthodontic tooth movement in normoglycemic group was 0.34±0.07 mm while 0.34±0.06 mm in insulin treated diabetic group with non-significant difference. Conclusion: Insulin therapy reversed the diabetic condition to the same level as that of normal subjects.
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 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) | 0.001 | 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 | 0.001 |
| 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".