Fibrillin‐1 in incisional hernias: an immunohistochemical study in scar and non‐scar regions of human skin and muscle fasciae
Bibliographic record
Abstract
Incisional hernias represent one of the most common complications after laparotomy. Specific pre-operative risk factors have not yet been identified. Recent studies indicate that changes in extracellular matrix components such as collagen I and collagen III may be involved in hernia development. In the present study we have evaluated the significance of fibrillin-1 in hernia development as one of the main components of the extracellular matrix. Tissue samples from non-scar skin and muscle fascia of 12 patients with incisional hernias as well as from the respective scar tissues were obtained. Corresponding tissue samples of 10 patients with normal postoperative wound healing served as controls. Distribution of fibrillin-1 was evaluated immunohistochemically. Differences in fibrillin-1 distribution in the non-scar tissues of muscle fascia have been found in patients with incisional hernia, compared to those without hernia. In scar regions of both patient groups, slight differences in the pattern of fibrillin-1 were observed. A tendency to a differential deposition of fibrillin-1 in skin samples, although hardly quantifiable, was observed as well. Our results suggest that fibrillin-1 is a relevant factor contributing to tissue stability. Disturbances in its deposition, even before scar formation, may be an important factor to the development of incisional hernias.
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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 | 0.001 | 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".