The tourniquet's effects on skeletal muscle during total knee arthroplasty
Bibliographic record
Abstract
Abstract This study investigates the impact of perioperative tourniquet on skeletal muscle cells during total knee arthroplasty (TKA) and its effects on the gene expression of apoptotic, inflammatory, and angiogenic pathways. The randomized controlled trial included 44 patients undergoing TKA. The patients were randomized to undergo surgery with ( n = 23) or without ( n = 21) tourniquet. The tourniquet was inflated before skin incision and deflated before wound closure in the tourniquet group. Biopsies from the lateral vastus muscle were obtained from both groups before wound closure and 8 weeks after surgery. The messenger ribonucleic acid (mRNA) expression and protein levels of angiopoietin‐like 4 (ANGPTL4), Hypoxia‐inducible Factor 1α, and Vascular Endothelial Growth Factor Alpha (VEGF‐A) in the biopsies were examined by reverse transcription‐quantitative polymerase chain reaction and tissue microarray, respectively. Differences in mean values (Δ C t for mRNA expression and staining positivity for protein expression) were compared with t ‐tests. The apoptotic marker BID and the angiogenic marker VEGF‐A were significantly lower in the tourniquet group compared to the control group ( p = 0.03, p = 0.047). However, there was a significant upregulation of VEGF‐A 8 weeks after surgery in the tourniquet group compared to perioperative biopsies ( p = 0.002), indicating persistent changes. A significant upregulation in protein expression of the angiogenic marker ANGPTL4 was found perioperatively in the tourniquet group ( p = 0.02). Our results demonstrate that the angiogenic gene expression is significantly altered by the tourniquet, the effects of which might contribute to postoperative interstitial edema, increased pain, and decreased muscle strength. These effects could lead to delayed rehabilitation and ultimately reduced patient satisfaction after TKA.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 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".