Joint capsule mast cells and neuropeptides are increased within four weeks of injury and remain elevated in chronic stages of posttraumatic contractures
Bibliographic record
Abstract
The purpose of this article was to determine mast cell and neuropeptide nerve fiber numbers in joint capsules in posttraumatic contractures, as elevated numbers have been implicated in other fibrotic and contracture conditions. Twelve skeletally mature rabbits had intraarticular cortical windows removed from the medial and lateral femoral condyles and the knee joint immobilized. The contralateral unoperated limb served as a control. Equal numbers of rabbits were sacrificed 4 weeks after surgery or 40 weeks after the first surgery that included 32 weeks of remobilization. Six patients with chronic posttraumatic elbow joint contractures and six age-matched organ donor controls free of elbow contractures were also studied. Joint capsule myofibroblast, mast cell, and neuropeptide containing nerve fiber numbers were assessed with immunohistochemistry. The numbers of myofibroblasts, mast cells, and neuropeptide containing nerve fibers expressed as a percentage of total cells were significantly greater in the contracture capsules when compared to the control capsules at all time points (p < 0.0001). The range of percentages for the three components in the contracture capsules versus the controls were 41-48% versus 9-10%, 44-50% versus 11-13%, and 45-50% versus 10-12% for the acute and chronic stages of the rabbit model and the chronic stages in the human elbows, respectively. These data support the hypothesis that a myofibroblast-mast cell-neuropeptide fibrosis axis may underlie some of the pathologic changes in the joint capsule in posttraumatic contractures. Approaches designed to manipulate this axis, such as preventing degranulation of mast cells, warrant further investigation.
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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 | 0.000 | 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) | 0.001 | 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".