Conditioned medium produced by fibroblasts cultured in low oxygen pressure allows the formation of highly structured capillary-like networks in fibrin gels
Bibliographic record
Abstract
Abstract Tissue engineering is an emerging and promising concept to replace or cure failing organs, but its clinical translation currently encounters issues due to the inability to quickly produce inexpensive thick tissues, which are necessary for many applications. To circumvent this problem, we postulate that cells secrete the optimal cocktail required to promote angiogenesis when they are placed in physiological conditions where their oxygen supply is reduced. Thus, dermal fibroblasts were cultivated under hypoxia (2% O2) to condition their cell culture medium. The potential of this conditioned medium was tested for human umbilical vein endothelial cell proliferation and for their ability to form capillary-like networks into fibrin gels. The medium conditioned by dermal fibroblasts under hypoxic conditions (DF-Hx) induced a more significant proliferation of endothelial cells compared to medium conditioned by dermal fibroblasts under normoxic conditions (DF-Nx). In essence, doubling time for endothelial cells in DF-Hx was reduced by 10.4% compared to DF-Nx after 1 week of conditioning, and by 20.3% after 2 weeks. The DF-Hx allowed the formation of more extended and more structured capillary-like networks than DF-Nx or commercially available medium, paving the way to further refinements.
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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".