MECHANICAL LOADING MODULATES ANGIOGENIC FACTORS IN TENDON CELLS
Bibliographic record
Abstract
Introduction Tendon disorders are a significant cause of pain and morbidity amongst athletes, workers and the general public.1 2 Tendinopathy is often viewed as the result of failed or inadequate healing response through repetitive overuse.3 The clinical symptoms of tendinopathy are activity–related pain, focal tenderness, and intratendinous imaging changes. Previous authors have suggested there may be an association between pain and neurovascular changes resulting from tendon overuse in tendinopathy patients.4 5 In order to examine the effects of repetitive overuse on the expression of angiogenic genes which regulate neovascularization in tendinopathy, primary human tendon cells were subjected to cyclic strain. Methods By using Flexcell Tension Systems, the isolated tendon cells from human hamstring tendons (excess ACL autograft material) were exposed to cyclic tension (1 Hz frequency and 10% strain). RNA samples were isolated at different time points and gene expression was evaluated by qPCR and qPCR array. Zymography assay was also conducted in order to measure the activity of MMP-2 in the supernatant of tendon cell culture. Results Initial experiments show that cyclic strain of two-dimensional primary tenocyte cell cultures (1 Hz) promotes increased expression of VEGF, bFGF, Cox-2 and IL-6 genes, and increased activity of MMP-2. But, by increasing the time course (∼after 4 h), bFGF, Cox-2 and VEGF are progressively downregulated. Our preliminary results of qPCR array for angiogenic profiling of tendon cells also led to the discovery of other genes (ANGPTL4, FGF-1, TGFα, VEGF-C, PROK2 and SPHK1) that may respond to tensile loading following a similar pattern. Conclusion It seems that the early response of the tendon cell to overuse tensile loading leads to upregulation of some angiogenic factors which may play an important role in tissue homeostasis following periods of overuse.
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) | 0.004 | 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".