In vitro Evaluation of ASCs and HUVECs Co-cultures in 3D Biodegradable Hydrogels on Neurite Outgrowth and Vascular Organization
Bibliographic record
Abstract
Vascular disruption following spinal cord injury (SCI) decisively contributes to the poor functional recovery prognosis facing patients with the condition. Using a previously developed gellan gum hydrogel to which the adhesion motif GRGDS was grafted (GG-GRGDS), this work aimed to understand the ability of adipose-derived stem cells (ASCs) to impact vascular organization of human umbilical vein endothelial cells (HUVECs), and how this in turn affects neurite outgrowth of dorsal root ganglia (DRG) explants. Our data shows that culturing these cells together lead to a synergistic effect as showed by increased stimulation of neuritogenesis on DRG. Importantly, HUVECs were only able to assemble into vascular-like structures when cultured in the presence of ASCs, which shows the capacity of these cells in reorganizing the vascular milieu. Analysis of selected neuroregulatory molecules showed that the co-culture upregulated the secretion of several neurotrophic factors. On the other hand, ASCs, and ASCs + HUVECs presented a similar profile regarding the presence of angiotrophic molecules herein analyzed. Finally, the implantation of GG-GRGDS hydrogels encapsulating ASCs in the chick chorioallantoic membrane (CAM) lead to increases in vascular recruitment toward the hydrogels in comparison to GG-GRGDS alone. This indicates that the combination of ASCs with GG-GRGDS hydrogels could promote re-vascularization in trauma-related injuries in the central nervous system and thus control disease progression and induce functional recovery.
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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".