The cellular networks of normal ovine medial collateral and anterior cruciate ligaments are not accurately recapitulated in scar tissue
Bibliographic record
Abstract
The purpose of this study was to characterize the cellular organization of the ovine medial collateral ligament (MCL) and anterior cruciate ligament (ACL) and compare this organization with that found in ligaments undergoing healing. Indirect immunofluorescence microscopy, used in combination with antibodies to cytoskeletal proteins, was employed to visualize individual ligament cells. Normal ligaments contained fusiform cells arranged in rows, which were stacked at regular intervals across the body of the ligament forming a three-dimensional cellular lattice. Each cell exhibited prominent cytoplasmic processes that extended for long distances through the extracellular matrix to adjacent cells, and these processes contained gap junctions. Thus the cells in rows and between rows were interconnected. The cells of the MCL and ACL scars were also arranged in rows, but these rows were shorter, irregularly arranged and closely packed into bundles resulting in tissue with a higher cellular density. In addition, cells transiting the cell cycle were detected in the scar but not in normal ligament. While the rows of cells in the normal ligament extended along the long axis of the ligament, the bundles of rows of ligament scar cells had a random orientation with respect to one another and to the region outside the scar. Over time both the ACL and the MCL scars displayed discontinuities in their cellular rows. In contrast to the scars of the MCL, which contained discontinuities filled with cellular projections and gap junctions, ACL scars contained discontinuities that were devoid of cells and gap junctions. These discontinuities as well as the differences between normal and scar cytoarchitecture may represent features of an inadequate healing response and/or may provide the structural basis for the altered biomechanics of healing ligaments.
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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 | 0.001 |
| 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".