Electrical Stimulation-Based Twitch Exercise Suppresses Progression of Immobilization-Induced Muscle Fibrosis via Downregulation of PGC-1α/VEGF Pathway
Bibliographic record
Abstract
This study aimed to determine whether electrical stimulation-based twitch exercise is effective in inhibiting the progression of immobilization-induced muscle fibrosis. 19 Wistar rats were randomly divided into a control group (n=6), an immobilization group (n=6; with immobilization only), and a Belt group (n=7; with immobilization and twitch exercise through the belt electrode device, beginning 2 weeks after immobilization). The bilateral soleus muscles were harvested after the experimental period. The right soleus muscles were used for histological analysis, and the left soleus muscles were used for biochemical and molecular biological analysis. As a result, in the picrosirius red images, the perimysium and endomysium were thicker in both the immobilization and Belt groups compared to the control group. However, the perimysium and endomysium thickening were suppressed in the Belt group. The hydroxyproline content and alpha-SMA, TGF-beta1, and HIF-1alpha mRNA expressions were significantly higher in the immobilization and belt groups than in the control group. These expressions were significantly lower in the Belt group than in the immobilization group. The capillary-to-myofiber ratio and the mRNA expressions of VEGF and PGC-1alpha were significantly lower in the immobilization and belt groups than in the control group, these were significantly higher in the Belt group than in the immobilization group. From these results, Electrical stimulation-based twitch exercise using the belt electrode device may prevent the progression of immobilization-induced muscle fibrosis caused by downregulating PGC-1alpha/VEGF pathway, we surmised that this intervention strategy might be effective against the progression of muscle contracture. Keywords: Immobilization, Skeletal muscle, Fibrosis, Electrical stimulation-based twitch exercise, PGC-1alpha/VEGF pathway.
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".