Combined application of chondroitinase ABC and photobiomodulation with low-intensity laser on the anal sphincter repair in rabbit
Bibliographic record
Abstract
BACKGROUND: Photobiomodulation with low-intensity laser (LIL) and chondroitinase ABC (ChABC) can repair damaged muscle tissue, so the aim of this study was to investigate the effect of co-administration of these two factors on anal sphincter repair in rabbits. METHODS: Male rabbits were studied in 5 groups (n = 7): Control (intact), sphincterotomy, laser, ChABC and laser + ChABC. 90 days after intervention were evaluated resting and maximum squeeze pressures, number of motor units, collagen amount, markers of muscle regeneration and angiogenesis. RESULTS: Resting pressure in the Laser + ChABC group was higher than the sphincterotomy, laser and ChABC groups (p < 0.0001). Maximum squeeze pressure in the all study groups was higher than sphincterotomy group (p < 0.0001). In the laser + ChABC and ChABC groups, motor unit numbers were more than the sphincterotomy group (p < 0.0001). Collagen content was significantly decreased in the laser (p < 0.0001) and laser + ChABC groups. ACTA1 (p = 0.001) and MHC (p < 0.0001) gene expression in the Laser + ChABC group were more than the laser or ChABC alone. VEGFA (p = 0.009) and Ki67 mRNA expression (p = 0.01) in the Laser + ChABC group were more than the laser group, But vimentin mRNA expression (p < 0.0001) was less than the laser group. CONCLUSION: Co-administration of ChABCs and photobiomodulation with LIL appears to improve the tissue structure and function of the anal sphincter in rabbits more than when used alone.
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) | 0.000 | 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) | 0.001 | 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".