Inflammation-induced endothelial to mesenchymal transition promotes brain endothelial cell dysfunction and occurs during multiple sclerosis pathophysiology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The blood-brain barrier (BBB) has a major role in maintaining brain homeostasis through the specialized function of brain endothelial cells (BECs). Inflammation of the BECs and loss of their neuroprotective properties is associated with several neurological disorders, including the chronic neuro-inflammatory disorder multiple sclerosis (MS). Yet, the underlying mechanisms of a defective BBB in MS remain largely unknown. Endothelial to mesenchymal transition (EndoMT) is a pathophysiological process in which endothelial cells lose their specialized function and de-differentiate into mesenchymal cells. This transition is characterized by an increase in EndoMT-related transcription factors (TFs), a downregulation of brain endothelial markers, and an upregulation of mesenchymal markers accompanied by morphological changes associated with cytoskeleton reorganization. Here, we postulate that EndoMT drives BEC de-differentiation, mediates inflammation-induced human BECs dysfunction, and may play a role in MS pathophysiology. We provide evidence that stimulation of human BECs with transforming growth factor (TGF)-β1 and interleukin (IL)-1β promotes EndoMT, a process in which the TF SNAI1, a master regulator of EndoMT, plays a crucial role. We demonstrate the involvement of TGF-β activated kinase 1 (TAK1) in EndoMT induction in BECs. Finally, immunohistochemical analysis revealed EndoMT-associated alterations in the brain vasculature of human post-mortem MS brain tissues. Taken together, our novel findings provide a better understanding of the molecular mechanisms underlying BECs dysfunction during MS pathology and can be used to develop new potential therapeutic strategies to restore BBB function.
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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