The fundamentals of fibroblast growth factor 9
Bibliographic record
Abstract
Fibroblast growth factor 9 (FGF9) was first identified during a screen for factors acting on cells of the central nervous system (CNS). Research over the subsequent two decades has revealed this protein to be a critically important and elegantly regulated growth factor. A hallmark control feature is reciprocal compartmentalization, particularly during development, with epithelium as a dominant source and mesenchyme a prime target. This mesenchyme selectivity is accomplished by the high affinity of FGF9 to the IIIc isoforms of FGFR1, 2, and 3. FGF9 is expressed widely in the embryo, including the developing heart and lungs, and more selectively in the adult, including the CNS and kidneys. Global Fgf9-null mice die shortly after birth due to respiratory failure from hypoplastic lungs. As well, their hearts are dilated and poorly vascularized, the skeleton is small, the intestine is shortened, and male-to-female sex reversal can be found. Conditional Fgf9-null mice have revealed CNS phenotypes, including ataxia and epilepsy. In humans, FGF9 variants have been found to underlie multiple synostoses syndrome 3, a syndrome characterized by multiple joint fusions. Aberrant FGF9 signaling has also been implicated in differences of sex development and cancer, whereas vascular stabilizing effects of FGF9 could benefit chronic diseases. This primer reviews the attributes of this vital growth factor.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".