Polarizing receptor activation dissociates fibroblast growth factor 2 mediated inhibition of myelination from its neuroprotective potential
Bibliographic record
Abstract
Fibroblast growth factor (FGF) signaling contributes to failure of remyelination in multiple sclerosis, but targeting this therapeutically is complicated by its functional pleiotropy. We now identify FGF2 as a factor up-regulated by astrocytes in active inflammatory lesions that disrupts myelination via FGF receptor 2 (FGFR2) mediated activation of Wingless (Wnt) signaling; pharmacological inhibition of Wnt being sufficient to abrogate inhibition of myelination by FGF2 in tissue culture. Using a novel FGFR1-selective agonist (F2 V2) generated by deleting the N-terminal 26 amino acids of FGF2 we demonstrate polarizing signal transduction to favor FGFR1 abrogates FGF mediated inhibition of myelination but retains its ability to induce expression of pro-myelinating and immunomodulatory factors that include Cd93, Lif, Il11, Hbegf, Cxcl1 and Timp1. Our data provide new insights into the mechanistic basis of remyelination failure in MS and identify selective activation of FGFR1 as a novel strategy to induce a neuroprotective signaling environment in multiple sclerosis and other neurological diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.002 | 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".