Post‐exercise massage affects skeletal muscle gene expression
Bibliographic record
Abstract
Massage therapy is commonly prescribed for individuals that suffer from chronic pain, inflammation, or musculoskeletal injury. Despite the widespread belief that massage augments muscle repair and reduces inflammation, there is little objective, scientific evidence to support its practice. Therefore, the purpose of this study was to evaluate the molecular effects of massage following a single unaccustomed bout of exercise. Eleven recreationally active, healthy males (age 22±1 yrs, VO 2peak 46±2 ml·kg −1 ·min −1 ) volunteered to participate in this study. Each subject completed an exhaustive endurance cycling protocol. After 15 mins of recovery, one quadricep was randomly chosen for 10 mins of massage (MASS) and the contralateral leg served as a control (CON). Muscle biopsies were acquired from the vastus lateralis at rest, immediately following massage, and 2.5h after massage was administered. Histology revealed that exercise induced significant muscle damage from rest at 2.5h (P<0.05), however there was no effect of massage (P>0.05). No differences were seen between CON or MASS in the oxidative stress markers 4HNE or protein carbonyls at any timepoint (P>0.05). Gene microarray analysis displayed 4 genes that were differentially expressed (P<0.05) for MASS vs CON immediately following massage as well as 11 genes at 2.5h that relate to pathways of inflammation and cellular remodeling. In summary, these data provide evidence that massage stimulates molecular events that may justify its use in the remediation of muscle injury. (Supported by NSERC Canada).
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 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 | 0.001 | 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 | 0.001 |
| 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 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".