Anti-inflammatory effects of sericin and swimming exercise in treating experimental Achilles tendinopathy in rat
Bibliographic record
Abstract
The aim of this study was to assess the effectiveness of combining sericin with swimming exercise as a treatment for type-I collagenase-induced Achilles tendinopathy (AT) in rats, with a focus on inflammatory cytokines. An experimental AT model was established using type-I collagenase in male Sprague–Dawley rats, categorized into five groups: Group 1 (Control + Saline), Group 2 (AT), Group 3 (AT + exercise), Group 4 (AT + sericin), and Group 5 (AT + sericin + exercise). Intratendinous sericin administration (0.8 g/kg/mL) took place from days 3 to 6, coupled with 30 min daily swimming exercise sessions (5 days/week, 4 weeks). Serum samples were analyzed using ELISA for tumor necrosis factor-alpha (TNF-α), interleukin-1 beta (IL-1β), interleukin-10 (IL-10), and total antioxidant-oxidant status (TAS-TOS), alongside histopathological and immunohistochemical assessments of Achilles tendon samples. Elevated TNF-α and IL-1β and decreased IL-10 levels were evident in Group 2; Of these, TNF-α and IL-1β were effectively reduced and IL-10 increased across all treatment groups, particularly groups 4 and 5. Serum TAS was notably lower in Group 2 and significantly increased in Group 5 compared to Group 2. Histopathologically, Group 2 displayed severe degeneration, irregular fibers, and round cell nuclei, while Group 5 exhibited decreased degeneration and spindle-shaped fibers. The Bonar score increased in Group 2 and decreased in groups 4 and 5. Collagen type-I alpha-1 (Col1A1) expression was notably lower in Group 2 ( P = 0.001) and significantly increased in groups 4 and 5 compared to Group 2 ( P = 0.011 and 0.028, respectively). This study underscores the potential of sericin and swimming exercises in mitigating inflammation and oxidative stress linked to AT pathogenesis, presenting a promising combined therapeutic strategy.
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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 | 0.000 | 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) | 0.000 | 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".