Hypophosphatasia: molecular testing of 19 prenatal cases and discussion about genetic counseling
Bibliographic record
Abstract
OBJECTIVE: We studied hypophosphatasia (HP) mutations in 19 cases prenatally detected by ultrasonography without familial history of HP. We correlated the mutations with the reported ultrasound signs, and discussed genetic counseling with regard to the particular dominantly inherited prenatal benign form of HP. METHOD: The coding sequence of the tissue nonspecific alkaline phosphatase (TNSALP) gene was analyzed by DNA sequencing, and 3D modeling was used to locate the mutated amino acids with regard to the functional domains of TNSALP. RESULTS: Although reported ultrasound signs were heterogeneous, two mutated alleles were found in 18 of the 19 cases studied, indicating recessive transmission of the disease. Functional domains of TNSALP were affected by 74% of missense mutations. In all the cases, including one with only a heterozygous mutation, molecular, biological, and familial data do not corroborate the hypothesis of prenatal benign HP. The mutation c.1133A>T observed in the prenatal benign form of HP and common in USA was not found in this series. CONCLUSION: The results point out the prenatally detectable allelic heterogeneity of HP. The nature of the detected mutations and the evidence of recessive inheritance do not support these cases being affected with prenatal benign HP.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".