Myofibroblast Numbers are Elevated in Human Elbow Capsules After Trauma
Bibliographic record
Abstract
Elbow contractures, a frequent problem after injury, can be treated by excision of the joint capsule. However, the underlying changes in the joint capsule are poorly understood. Based on skin healing work, we examined the hypotheses that myofibroblast numbers and expression of a myofibroblast marker alpha-smooth muscle actin, are elevated in patients with posttraumatic joint contractures. Anterior capsules were obtained from six patients who had operative release of posttraumatic contractures greater than 5 months after injury and six elbows of organ donors free of contractures. Immunohistochemical studies revealed that myofibroblast numbers and percentage of total cells that were myofibroblasts were significantly elevated in the joint capsules from patients with contractures (326 +/- 61 cells per field, 36% +/- 4% total cells) when compared with similar tissues of the organ donors (69 +/- 41 cells per field, 9% +/- 4% total cells). Western blot analysis showed that protein levels of alpha-smooth muscle actin were significantly elevated in patients with posttraumatic joint contractures. However, analysis with reverse transcription-polymerase chain reaction determined that messenger ribonucleic acid levels for smooth muscle actin normalized to the housekeeping gene glyceraldehyde-3-phosphate dehydrogenase were not significantly different between the two groups. An association between increased numbers of myofibroblasts and posttraumatic joint contractures has been established in the human elbow capsule. Additional work is required to determine whether myofibroblast regulators may be targets for adjuvant therapies of posttraumatic contractures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".