Regional variability, processing methods, and biophysical properties of human fascia lata extracellular matrix
Bibliographic record
Abstract
This study aims to assess the regional variability, processing methods, mechanical, biochemical, and cellular properties of human fascia lata as a scaffold for soft tissue repair and tissue engineering applications. Ten pairs of fascia lata (donor age 18-55) were used. One fascia patch from each pair was used to assess the geometric and biomechanical variability of fresh fascia. The other from each pair was subjected to 1 of 2 allograft processing methods: antibiotic soak alone or acellularization plus antibiotic soak. Stiffness, modulus, hydroxyproline, chondroitin/dermatan sulfate glycosaminoglycan (CSDS GAG), and DNA content were quantified in fascia from fresh and treated groups. The effect of location was not significant for thickness or stiffness within a 6 x 12 cm2 region of the iliotibial tract of fresh human fascia lata. Processing did not significantly change the stiffness, modulus, or CSDS GAG content of fascia ECM. However, hydroxyproline (collagen) content is significantly reduced in acellularized fascia, probably reflecting a removal of soluble collagen during the treatment (p < 0.02). Processing reduced the DNA content of fresh fascia approximately 10-fold (p < 0.001). The mechanical, chemical and ultrastructural similarities between fascia lata and tendon may make fresh or processed fascia an attractive ECM scaffold for soft tissue, particularly tendon, repair.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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".