Impact of TNF and IL-1β on capillary networks within engineered human adipose tissues
Bibliographic record
Abstract
Inflammation is a normal phase of the wound healing process, which likely occurs following tissue transplantation. For reconstructive surgery purposes, engineered adipose tissues represent promising alternatives to autologous fat grafts. It is therefore important to study the impact of an inflammatory microenvironment on the cellular functions of the different cell types comprised within matrix-rich reconstructed tissues. In this study, human reconstructed adipose tissues (hrATs) featuring a preformed capillary network formed by microvascular endothelial cells (hMVECs) were produced from adipose-derived stem/stromal cells (ASCs) by the self-assembly approach of tissue engineering. We hypothesized that a prolonged inflammatory context, mediated by tumor necrosis factor (TNF) and interleukin-1β (IL-1β), would impact hrATs' secretory profile and mediate detrimental effects on the microvascular network in vitro. Analysis of conditioned media established tissue responsiveness through the increased secretion of monocyte chemoattractant protein-1 (up to 23 fold), interleukin-6 (up to 69 fold) and angiopoietin-1 (up to 2.7 fold) after 3 and 6 days of cytokine exposure, along with a significant reduction in adiponectin secretion. Imaging of the preformed capillary network within the hrATs revealed increased disorganization in the presence of TNF/IL-1β, featuring a less extended and less ramified network with apoptotic hMVECs in the remaining capillary structures. These results indicate that a prolonged inflammatory context can be deleterious to the capillary network featured by in vitro engineered tissues. Strategies aiming at preserving the integrity of the vascular network will help develop substitutes that are better suited to face inflammatory conditions upon grafting.
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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".