Tuning Graft- and Host-Derived Vascularization in Modular Tissue Constructs: A Potential Role of HIF1 Activation
Bibliographic record
Abstract
A better understanding of the factors governing the vascularization of engineered tissues is crucial for their advancement as therapeutic platforms. Here, we studied the effect of implant volume and cell densities on the in vivo vascularization of modular engineered tissue constructs. Sub-millimeter collagen modules containing adipose-derived mesenchymal stromal cells (adMSC) and enveloped by human umbilical vein endothelial cells (HUVEC) were subcutaneously implanted in severe-combined immunodeficient mice with a beige-mutation (SCID-bg) mice. Implant volume and cell density was varied relative to a base case, defined as a 0.01 mL implant containing 1.5×10(7) adMSC/mL and 3.9×10(6) HUVEC/mL. At 7 and 14 days post-transplantation, the constructs were harvested for immunohistochemical analysis of total (CD31(+)) and graft-derived (UEA1(+)) vessel formation, hypoxia-inducible factor 1-alpha (HIF1α) expression, infiltration of host-derived leukocytes (CD45), and macrophages (F4/80). Implant volume and cell density affected the relative contributions of host- versus graft-derived vascularization, highlighting that different mechanisms underlie the two processes. Graft-derived vessel formation was most rapid and robust in implants with high HIF1α expression, namely large volume implants and implants with high adMSC and HUVEC density (p<0.01 compared to base case at day 7). Many HIF1α(+) cells were vessel-lining HUVEC, suggesting that HIF1 activation may be key to vessel assembly in the graft. Host vessel ingrowth, however, dominated the vascularization of small volume implants (of high and low adMSC density alike), which showed low HIF1α expression at day 7. Host vessels were sustained to day 14 when adMSC density alone was increased, presumably due to increased paracrine secretions. This study points to a potential role of HIF1 activation in the vascularization of tissue constructs, which may be harnessed to engineer robust vessels for therapeutic applications.
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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) | 0.000 | 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 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".