Effect of Systemic Tetracycline on the Degradation of Tetracycline‐Impregnated Bilayered Collagen Membranes: An Animal Study
Bibliographic record
Abstract
BACKGROUND: Premature collagen membrane degradation may compromise the outcome of osseous regenerative procedures. Tetracyclines (TTCs) inhibit the catalytic activities of human metalloproteinases. Preprocedural immersion of collagen membranes in TTC and systemic administration of TTC may be possible alternatives to reduce the biodegradation of native collagen membranes. AIM: To evaluate the in vivo degradation of collagen membranes treated by combined TTC immersion and systemic administration. MATERIALS AND METHODS: Seventy-eight bilayered porcine collagen membrane disks were divided into three groups and were immersed in 0, 50, or 100 mg/mL TTC solution. Three disks, one of each of the three groups, were implanted on the calvaria of each of 26 Wistar rats. Thirteen (study group) were administered with systemic TTC (10 mg/kg), while the remaining 13 received saline injections (control group). Calvarial tissues were retrieved after 3 weeks, and histological sections were analyzed by image analysis software. RESULTS: Percentage of remaining collagen area within nonimpregnated membranes was 52.26 ± 20.67% in the study group, and 32.74 ± 13.81% in the control group. Immersion of membranes in 100 mg/mL TTC increased the amount of residual collagen to 63.46 ± 18.19% and 42.82 ± 12.99% (study and control groups, respectively). Immersion in 50 mg/mL TTC yielded maximal residual collagen values: 80.75 ± 14.86% and 59.15 ± 8.01% (study and control groups, respectively). Differences between the TTC concentrations, and between the control and the study groups were statistically significant. CONCLUSIONS: Immersion of collagen membranes in TTC solution prior to their implantation and systemic administration of TTC significantly decreased the membranes' degradation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".