Type 2 diabetes impairs tendon repair after injury in a rat model
Bibliographic record
Abstract
Type 2 diabetes adversely affects the properties of native connective tissue. The underlying mechanisms, however, by which diabetes alters connective tissue metabolism, especially tendon, are poorly defined. The aim of this study was to determine the effect of type 2 diabetes on the mechanical, histological, and molecular properties of the intact and healing Achilles tendon. The right Achilles tendon was transected in 11 male diabetic Goto-Kakizaki (GK) and 10 age- and sex-matched Wistar control rats, while the left Achilles tendon was left intact. At 2 wk postinjury the intact and injured tendons were assessed by biomechanical testing and histology. The gene expression of collagen I and III, biglycan, versican, MMP-13, and MMP-3 was measured by quantitative RT-PCR, and their protein distribution was studied by immunohistochemistry. Intact tendons exhibited only small differences between the groups. In injured tendons, however, a significantly smaller transverse area and lower stiffness was found in diabetic GK compared with Wistar control rats. This correlated with impaired structural organization of collagen fibers and a reduced expression of collagen I and III in the injured tendons of the diabetic GK compared with Wistar control. Moreover, MMP-3 gene expression was downregulated in the injured diabetic GK tendons compared with injured Wistar controls. Our results indicate that in a rat model of diabetes tendon healing is impaired mainly due to altered expression of collagen and MMPs reflecting decreased degradation of matrix proteins and impaired tissue remodeling. Further our data suggest that therapeutic modulation of collagens or MMPs might be targets for new regenerative approaches in operated, injured, or maybe also degenerative tendon diseases in diabetes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".