Identification of a recurrent mutation in the human hairless gene underlying atrichia with papular lesions
Bibliographic record
Abstract
Identification of mutations in the hairless (HR) gene in patients with atrichia with papular lesions (APL) has proven of critical importance, as it provides a basis for the differentiation between APL and alopecia universalis. The establishment of the diagnostic criteria for APL has triggered the identification of a large number of APL patients among those suspected to suffer from alopecia universalis. This advancement has resulted in the discovery of an increasing number of hairless mutations in both consanguineous and nonconsanguineous APL families. Here, we report the identification of a homozygous mutation, 3434delC, in an APL patient of Arab-Palestinian descent. The proband is a 23-year-old female with generalized scalp and body alopecia. To confirm the diagnosis of APL and to identify the specific mutation, we sequenced the hairless gene. Sequencing of all exons of the hairless gene revealed a homozygous frameshift mutation, 3434delC, in exon 18. Interestingly, the same mutation was previously identified in an Arab-Israeli family. Our data suggest that the 3434delC mutation most likely represents a founder mutation in this geographical region.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".