Hypoxia-Inducible Factor Drives Vascularization of Modularly Assembled Engineered Tissue
Why this work is in the frame
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Bibliographic record
Abstract
Robust vascularization is critical for engineering tissues of clinically relevant size and cell loads. Delineating the rate-limiting steps underlying vascularization is necessary to creating strategies for faster, better vascularization of tissue constructs. We used two inhibitory methods to dissect the role of hypoxia-inducible factor (HIF) in vascularization-inducing engineered tissues, in this study constructed from self-assembly of submillimeter-sized tissues injected subcutaneously. Both systemic pharmacological inhibition using digoxin, and genetic inhibition (short hairpin RNA-transduced endothelial cells [ECs]) reduced vascularization and oxygenation within constructs, but elicited different mechanisms of action. Systemic inhibition negatively impacted early (day 3) recruitment of host-derived endothelial progenitor cells and macrophages/monocytes to the implant. Genetic inhibition in graft-derived ECs impaired their survival in low serum conditions as well as their baseline angiogenic function. Altogether, our study demonstrates that HIF is an important driver of vascularization in tissue constructs. While hypoxia is assumed to be an important feature of tissue engineering, this study directly connects inhibition of vascularization to HIF inhibition. Using two inhibitory methods, we demonstrated that hypoxia-inducible factor (HIF) plays an important role in vascularizing and oxygenating modularly-assembled engineered tissues. Each inhibitory technique elucidated a different mechanism by which this occurred. Whereas systemic inhibition negatively impacted early recruitment of host-derived cells, genetic inhibition in grafted endothelial cells was detrimental to their survival. Taken together, our study suggests that methods of HIF-mediated mechanisms could be harnessed to tune the extent and rate of vascularization in engineered tissue constructs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it