Exposure to oxLDL impairs TGF-β activity in human tendon cells
Bibliographic record
Abstract
Abstract Background: Hypercholesterolemia is a known risk factor for the development of acute or chronic tendon injuries, but the mechanisms which link cholesterol and tendon pathology are not completely known. Previous studies have shown that patients with hypercholesterolemia experience elevated levels of oxidized LDL (oxLDL), a molecule which triggers inflammation and collagenase activity. In this study we discovered a potential mechanism that may contribute to tendon damage by oxLDL. Methods: Isolated human tendon cells were incubated in the presence or absence of oxLDL. The influence of oxLDL on the expression level of key mRNA and proteins was examined using real time quantitative PCR, ELISA and Western blots. The activities of enzymes relevant to collagen synthesis and breakdown (lysyl oxidase and matrix metalloproteinases) were quantified using fluorometry. Finally, the isolated human tendon cells in a 3D construct were exposed to combinations of oxLDL and TGF-β to examine their interacting effects on collagen matrix remodeling. Results: The expression of key mRNAs including TGFB, COL1A1, DCN, and LOX were reduced in human tendon cells by oxLDL while MMPs were increased. The oxLDL reduced the activity of LOX, whereas conversely MMP activities were induced. COL1A1 synthesis and TGF-β secretion were also inhibited. Adding recombinant TGF-β reversed the effects of oxLDL on the expression of collagens and LOX. OxLDL also impaired collagen matrix remodeling, and adding TGF-β restored the native phenotype. Conclusions: Exposure oxLDL in patients with hypercholesterolemia may adversely affect the mechanical and structural properties of tendon tissue through a direct action of oxLDL on tendon cells, including impairment of TGF-β expression. This impairment leads to disturbed matrix remodeling and synthesis, thereby potentially leading to increased risk of acute or chronic tendon injury.
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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) | 0.002 | 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".