Osteogenic capacity of transgenic flax scaffolds
Bibliographic record
Abstract
The modification of flax fibers to create biologically active dressings is of undoubted scientific and practical interest. Flax fibers, derived from transgenic flax expressing three bacterial genes for the synthesis of poly-3-hydroxybutyric acid (PHB), have better mechanical properties than unmodified flax fibers; do not show any inflammation response after subcutaneous insertion; and have a good in vitro and in vivo biocompatibility. The aim of this study was to examine the applicability of composites containing flax fibers of genetically modified (M50) or non-modified (wt-Nike) flax within a polylactide (PLA) matrix for bone regeneration. For this, the mRNA expression of genes coding for growth factors (insulin-like growth factor IGF1, IGF2, vascular endothelial growth factor), for osteogenic differentiation (alkaline phosphatase, osteocalcin, Runx2, Phex, type 1 and type 2 collagen), and for bone resorption markers [matrix metalloproteinase 8 (MMP8), acid phosphatase type 5] were analyzed using quantitative real-time polymerase chain reaction. We found a significant elevated mRNA expression of IGF1 with PLA and PLA-wt-Nike composites. The mRNA amount of MMP8 and osteocalcin was significantly decreased in all biocomposite-treated cranial tissue samples compared to controls, whereas the expression of all other tested transcripts did not show any differences. It is assumed that both flax composites are able to stimulate bone regeneration, but composites from transgenic flax plants producing PHB showed faster bone regeneration than composites of non-transgenic flax plants. The application of these linen membranes for bone tissue engineering should be proved in further studies.
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 | 0.000 | 0.000 |
| 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".