Embryonic Survival and Severity of Cardiac and Craniofacial Defects Are Affected by Genetic Background in Fibroblast Growth Factor-16 Null Mice
Bibliographic record
Abstract
Disruption of the X-chromosome fibroblast growth factor 16 (Fgf-16) gene, a member of the FGF-9 subfamily with FGF-20, was linked with an effect on cardiac development in two independent studies. However, poor trabeculation with lethality by embryonic day (E) 11.5 was associated with only one, involving maintenance in Black Swiss (Bsw) versus C57BL/6 mice. The aim of this study was to examine the potential influence of genetic background through breeding the null mutation onto an alternate (C57BL/6) background. After three generations, 25% of Fgf-16(-/Y) mice survived to adulthood, which could be reversed by reducing the contribution of the C57BL/6 genetic background by back crossing to another strain. There was no significant difference between FGF-9 and FGF-20 RNA levels in Fgf-16 null versus wild-type mice regardless of strain. However, FGF-8 RNA levels were reduced significantly in Bsw but not C57BL/6 mice. FGF-8 is linked to anterior heart development and like the FGF-9 subfamily is reportedly expressed at E10.5. Like FGF-16, neuregulin as well as signaling via ErbB2 and ErbB4 receptors have been linked to trabeculae formation and cardiac development around E10.5. Basal neuregulin, ErbB2, and ErbB4 as well as FGF-8, FGF-9, and FGF-16 RNA levels varied in Bsw versus C57BL/6 mice. These data are consistent with the ability of genetic background to modify the phenotype and affect embryonic survival in Fgf-16 null mice.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".