Longer Term Survival of a Child With Autosomal Recessive Cutis Laxa Due to a Mutation in <i>FBLN4</i>
Bibliographic record
Abstract
Autosomal recessive cutis laxa (ARCL) is a clinically and genetically heterogeneous group of disorders characterized by loose, inelastic skin and variable systemic involvement and severity. Mutations in the FBLN4 gene are associated with ARCL1B. Fibulin-4 is important in elastic fiber formation and smooth muscle cell differentiation. We describe herein an 8-year-old boy who presented with severe aortic root dilatation and arterial tortuosity at 1 year of age which required surgical repair. His parents were consanguineous and there was a family history of three brothers who died early in life with an unknown type of connective tissue disorder in the 1960s. Both parents of the patient reported here were related to these three boys. We used a homozygosity mapping strategy with a 900K SNP array and identified FBLN4 as a candidate gene in an extended region of homozygosity. We sequenced this gene in the patient and identified a homozygous non-synonymous mutation at c.376G>A (p.Glu126Lys) in exon 5 that was predicted to be damaging. ARCL1B has most typically been associated with early demise but our report suggests that long-term survival is possible. With this longer term survival we are learning more about the natural history of this disorder, which includes baroreceptor reflex failure and low bone mineral density in this patient.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".