Heterozygous <scp>NF1</scp> dermal fibroblasts modulate exosomal content to promote angiogenesis in a <scp>tissue‐engineered</scp> skin model of neurofibromatosis type‐1
Bibliographic record
Abstract
Neovascularization is a critical process in tumor progression and malignant transformation associated with neurofibromatosis type 1 (NF1). Indeed, fibroblasts are known to play a key role in the tumoral microenvironment modification by producing an abundant collagenous matrix, but their contribution in paracrine communication pathways is poorly understood. Here, we hypothesized that NF1 heterozygosis in human dermal fibroblasts could promote angiogenesis through exosomes secretion. The purposes of this study are to identify the NF1 fibroblast-derived exosome protein contents and to determine their proangiogenic activity. Angiogenic proteome measurement confirmed the overexpression of VEGF and other proteins involved in vascularization. Tube formation of microvascular endothelial cells was also enhanced in presence of exosomes derived from NF1 skin fibroblasts. NF1 tissue-engineered skin (NF1-TES) generation showed a significantly denser microvessels networks compared to healthy controls. The reduction of exosomes production with an inhibitor treatment demonstrated a drastic decrease in blood vessel formation within the dermis. Our results suggest that NF1 haploinsufficiency alters the dermal fibroblast function and creates a pro-angiogenic signal via exosomes, which increases the capillary formation. This study highlights the potential of targeting exosome secretion and angiogenesis for therapeutic interventions in NF1.
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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".