FBN2 Silencing Recapitulates Hypoxic Conditions and Induces Elastic Fiber Impairment in Human Dermal Fibroblasts
Bibliographic record
Abstract
that may favor the development of the wound and/or delay healing. However, most studies regarding extracellular matrix remodeling in wound healing are conducted under normoxic conditions. Here, we investigated the consequences of hypoxia on elastic network formation, both in a mouse model of pressure-induced hypoxic ulcer and in human primary fibroblasts cultured under hypoxic conditions. In vitro, hypoxia inhibited elastic fiber synthesis with a reduction in fibrillin-2 expression at the mRNA and protein levels. Lysyl oxidase maturation was reduced, concomitant with lower enzymatic activity. Fibrillin-2 and lysyl oxidase could interact directly, whereas the downregulation of fibrillin-2 was associated with deficient lysyl oxidase maturation. Elastic fibers were not synthesized in the hypoxic inflammatory tissues resulting from in vivo pressure-induced ulcer. Tropoelastin and fibrillin-2 were expressed sparsely in hypoxic tissues stained with carbonic anhydrase IX. Different hypoxic conditions in culture resulted in the arrest of elastic fiber synthesis. The present study demonstrated the involvement of FBN2 in regulating elastin deposition in adult skin models and described the specific impact of hypoxia on the elastin network without consequences on collagen and fibronectin networks.
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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 | 0.000 | 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".